Dr Huichao Sun

Dr Qiang Zhang

Dr Huan He

Dr Kai Li

Dr Sarra Mamoghli

Keywords: Innovation, TRIZ (Theory of Inventive Problem Solving), Knowledge source, Semantic similarity, Case-based reasoning, Ontology, Ontology inference Formalize TRIZ Knowledge Ontology-based Knowledge Modeling 5.3. Semantic Relatedness

Cavallucci, for his continuous support of my Ph.D. study and research, and for his motivation, enthusiasm, and immense knowledge

Résumé

Un nombre croissant d'industries ressentent le besoin de formaliser leurs processus d'innovation. Dans ce contexte, les outils du domaine de la qualité et les approches d'aide à la créativité provenant du "brain storming" ont déjà montré leurs limites. Afin de répondre à ces besoins, la TRIZ (Acronyme russe pour Théorie de Résolution des Problèmes Inventifs), développée par l'ingénieur russe G. S. Altshuller au milieu du 20ème siècle, propose une méthode systématique de résolution de problèmes inventifs multidomaines.

Selon TRIZ, la résolution de problèmes inventifs consiste en la construction du modèle et l'utilisation des sources de connaissance de la TRIZ. Plusieurs modèles et sources de connaissances permettent la résolution de problèmes inventifs de types différents, comme les quarante Principes Inventifs pour l' élimination des contradictions techniques. Toutes ces sources se situent à des niveaux d'abstractions relativement élevés et sont, donc, indépendantes d'un domaine particulier, qui nécessitent des connaissances approfondies des domaines d'ingénierie différents. Afin de faciliter le processus de résolution de problèmes inventifs, un "Système Intelligent de Gestion de Connaissances" est développé dans cette thèse. D'une part, en intégrant les ontologies des bases de connaissance de la TRIZ, le gestionnaire propose aux utilisateurs de sources de connaissance pertinentes pour le modèle qu'ils construisent, et d'autre part, le gestionnaire a la capacité de remplir "automatiquement" les modèles associés aux autres bases de connaissance.

Ces travaux de recherche visent à faciliter et automatiser le processus de résolution de problèmes inventifs. Ils sont basés sur le calcul de similarité sémantique et font usage de différentes technologies provenantes de domaine de l'Ingénierie de Connaissances (modélisation et raisonnement basés sur les ontologies, notamment). Tout d'abord, des méthodes de calcul de similarité sémantique sont proposées pour rechercher et définir vi Résumé les liens manquants entre les bases de connaissance de la TRIZ. Ensuite, les sources de connaissance de la TRIZ sont formalisées comme des ontologies afin de pouvoir utiliser des mécanismes d'inférence heuristique pour la recherche de solutions spécifiques.

Pour résoudre des problèmes inventifs, les utilisateurs de la TRIZ choisissent dans un premier temps une base de connaissance et obtiennent une solution abstraite. Ensuite, les éléments des autres bases de connaissance similaires aux éléments sélectionnés dans la première base sont proposés sur la base de la similarité sémantique préalablement calculée. A l'aide de ces éléments et des effets physiques heuristiques, d'autres solutions conceptuelles sont obtenues par inférence sur les ontologies.

Enfin, un prototype logiciel est développé. Il est basé sur cette similarité sémantique et les ontologies interviennent en support du processus de génération automatique de solutions conceptuelles.

Mots-clés: Innovation; TRIZ (Théorie de Résolution des Problèmes Inventifs); Source de connaissance; Similarité sémantique; Case-based reasoning; Ontologie; Inférence de l'ontologie

Chapter 1 Introduction

This chapter gives a general introduction of this thesis that is realized at the Laboratoire du Génie de la Conception (LGECO) and the Laboratoire des Sciences de l'Ingénieur, de l'Informatique et de l'Imagerie (ICUBE). Firstly, the general background of this thesis is given. Then, the overall research problem, the motivation and research scope of this thesis are discussed. Finally, the structure of this thesis is presented.

General Background

Confronting the trend of globalization of economic activities, companies operate today, more than ever before, in a very competing and complex environment with rapidly changing market conditions. Thus, to play an important role in the global market, it is necessary to increase the capability to innovate and combine customer needs, productivity and competitiveness in the development of new products, services or business models. It is reported that successful companies produce seventy-five percent of their revenues from new products or services that did not exist five years ago [1]. This especially applies to companies that obtain their competitive advantages by technological lead.

Innovation is defined as the process of making changes, large and small, radical and incremental, to products, processes, and services that results in the introduction of something new for the organization that adds value to customers and contributes to the knowledge store of the organization [2]. Applying innovation is to use practical tools and techniques that can make these changes to products, processes, and services.

Innovation consists of the generation of a new idea and its implementation into a new 1.1. General Background product, process or service, leading to the dynamic growth of the national economy and the increase of employment as well as to a creation of pure profit for the innovative business enterprise. Innovation is never a one-time phenomenon, but a long and cumulative process of a great number of organizational decision-making process, ranging from the phase of generation of a new idea to its implementation phase. New idea refers to the perception of a new customer need or a new way to produce. It is generated in the cumulative process of information-gathering, coupled with an ever-challenging entrepreneurial vision. Through the implementation process the new idea is developed and commercialized into a new marketable product or a new process with attendant cost reduction and increased productivity [3].

The innovation in industries can also be considered as a complex process where creative ideas have been turned into new products and services in the market [4]. The innovation process consists of seven steps [5]:

• Step 1 Strategic Thinking The innovation process begins with the goal to create strategic advantage in the market, so in this stage, the way innovation adds value to the strategic intents and the areas innovation has the greatest potential to provide strategic advantage are considered.

• Step 2 Portfolio Management & Metrics

In this stage, the innovation portfolios are managed aggressively to balance the inherent risks of the unknown with the targeted rewards of success and the pursuit of the ideal with the realities of learning, risking, failing in order to ultimately succeed.

• Step 3 Research Through research, new significant opportunities for innovation are proposed in this stage.

• Step 4 Insight The innovation and the target are mutually clarified to confirm the right value proposition for the right customer.

• Step 6 Market Development This universal business planning process with brand identification and development is implemented to help customers to understand and choose the way to innovate and to rapid sales growth.

• Step 7 Selling In this stage, the customers benefit from successfully selling the new innovative products and services.

The scope of innovation exists primarily within the realm of the individual and the collective knowledge. This has become increasingly evident as the complexity of technology and markets has increased. The trends of more technology-intensive products or services intensify the necessity of innovation, and long term profitable growth of companies also requires a continuous flow of innovative products and processes. For the most of successful companies, innovation has become an indispensable factor, and therefore, in order to be more competitive in the market, the companies need to create an environment of continuous innovation. However, innovation is more than an idea or an invention but a complex and multidimensional process that should be investigated from different views [6]. During the last decades, innovation has undergone an abrupt change in the following three aspects [7]:

• Innovation has become a condition of survival.

• A continual flow of innovation is a frequent phenomenon.

• Innovation is now spreading throughout the industry.

At the same time, there is a rapid increase of complexity and uncertainty in the process of innovation because:

• There are much more diverse needs from customers and more competitions from the market than before.

• The life cycle is much shorter and the complexity of products is higher than before.

• There are increasing limited resources and strict environmental constraints.

In order to create innovative products or services in a continuous way, an increasing number of industries feel the need to formalize their innovation processes [8]. In this 1.1. General Background context, quality domain tools show their limits as well as the creativity assistance approaches derived from brainstorming [9].

TRIZ, the Theory of Inventive Problem Solving (Section 2.1), appears to be a pertinent answer to these needs. Developed in the middle of the 20th century by G. S. Altshuller, this methodology's goal was initially to improve and facilitate the resolution of technological problems [10,11].

TRIZ relies on a few main ideas:

• Technical artefacts evolve according to certain evolution laws.

• Inventions or patents from different domains often solve the same abstract problem.

• Finding an inventive solution to a problem is not easy, because we are not able to search for the solution beyond the fields we master. It is, therefore, necessary to break the psychological inertia.

• If design is considered as a problem, TRIZ is a method for solving this specific kind of problem.

• The whole makes up a self-designated "theory of inventive design".

The fact that technical artefacts evolve according to certain evolution laws represents an application of dialectics to technical systems that is also applied to organizations. For example, an artefact evolves by optimizing some of its parameters with the goal of getting a maximum of desired values, which is a compromise solution. When there is no solution of this kind, we are in presence of a "contradiction" (Section 2.1.3) in the dialectical sense of the term. It is not about a logical impossibility. The contradiction takes its place in a dynamic framework where the change in the value of a parameter has an influence on the change of another parameter.

TRIZ differs from other techniques by two of its three fundamental principles: problems appear because of and are solved taking into account certain evolution laws that are independent of the designer, and the description of the problem in terms of contradictions eases its resolution. According to the third principle, a problem has to be solved by the maximum use of existing resources, but it is common to the notion of value.

As shown in Figure 1.1, the process of using classic TRIZ can be identified as three different phases:

Research Problems

According to TRIZ, the resolution of inventive problems consists in the construction of models and the use of the corresponding knowledge sources (Section 2.1.5). Different models and knowledge sources are built to facilitate the use of TRIZ in different domains. However, there exist several problems in the use of these models and knowledge sources: • There are different models at different levels of abstraction. As shown in Figure 1.2, the initial problem being often fuzzy and not clearly expressed, the methodology pushes the designer to the building of a model in terms of a set of predefined TRIZ concepts. This model is called a "systemic model". Then, a second model emphasizes the main contradiction that is at the base of the problem ("technical contradiction" or "physical contradiction").

• Each model is built independently of the specific application field and has its set of associated knowledge sources. The resolution with different models is done starting from the different associated knowledge sources.

• There exists a gap between the high-level abstraction of TRIZ and reality, and TRIZ does not give directives on the way these models have to be used.

As a result, the TRIZ user is brought to work with several models at different levels of abstraction, whose use requires extensive knowledge about different engineering domains. In order to facilitate this innovative process, especially the use of TRIZ knowledge sources, this thesis studies the innovative topic: Automatic Ontology-based Mechanism for Solving Inventive Problems, which has never been addressed by any other researchers before. In the next section, the motivations for this study will be explained.

Motivation

The motivation driving these works is to facilitate and automate the process of solving inventive problem based on semantic technologies.

As stated in Section 1.2, different knowledge sources were established in order to solve different types of inventive problems, such as the forty inventive principles (Section 2.1.5.1) for eliminating the technical contradictions (Section 2.1.3.1) and the eleven separation methods (Section 2.1.5.3) for eliminating the physical contradictions (Section 2.1.3.2). These knowledge sources are all built independently of the application field, and their levels of abstraction are very different, making their use quite complicated. The main reasons for this difficulty are:

• The wealth of knowledge sources in TRIZ is available for solving a large variety of inventive problems but access to the needed specific knowledge might be troublesome.

• TRIZ definitions of physical concepts such as substances and fields are ambiguous and cannot be interpreted adequately.

• Using a recommendation proposed by TRIZ for solving a specific problem requires extensive knowledge of different engineering domains and is not currently supported by the methodology. As a consequence, the user is supposed to possess a high degree of expertise in engineering design.

More than that, these knowledge sources for resolutions are situated at different levels of "closeness to reality". In fact, inventive principles, for example, even if they seem to refer to concrete reality (for instance, "inert atmosphere") are conceptually more abstract than inventive standards (Section 2.1.5.2), which refer to concrete substances or fields. The imagination effort to be done by the user to apply an inventive principle is much more important than that the one to be done if he wants to use an inventive standard. Furthermore, even in the same knowledge source the concepts may be different or incompatible, as stated in [12].

Research Scope

Several works have been carried out regarding the formalization of the TRIZ knowledge sources to facilitate its use. For example, Bultey et al. established several ontologies, based on which an ideal TRIZ reasoning environment was proposed to support and simulate the process of Su-Field analysis [13], and Zanni-Merk et al. formalized the Inventive Design Method (IDM) in ontologies, such as the Evolution Hypotheses Ontology and the Problems and Partial Solutions Ontology [14]. However, their research focuses on one or more parts of the TRIZ knowledge souces, not yielding a comprehensive formalization for the TRIZ knowledge sources and their relations.

The main objective of this thesis is the formalization of the TRIZ knowledge sources, to complete the model and make it wholly consistent by the definition of the missing semantic links. This formalization should allow the development of an intelligent management system of these knowledge sources, with the aim of assisting the TRIZ expert during his activity. Indeed, during the processing of a new case, experts are brought to work with various models at different levels of abstraction. The knowledge management system should suggest the experts the use of the relevant knowledge sources, consistent with the level of abstraction of the model they are building. It will also be able to complete "automatically" the rest of the models, by exploiting the semantic links obtained among the different knowledge sources.

As a result, this thesis focuses on the formalization of the TRIZ knowledge sources and the modelling of the inventive problem solving processes. From our point of view, the principal motivation factors can be summarized as follows:

• The TRIZ knowledge sources should be formalized to facilitate their use.

• The missing semantic links among the TRIZ knowledge sources need to be defined to complete the model and make it wholly consistent.

• The development of an intelligent management system of TRIZ knowledge sources facilitates the inventive problem solving processes and provides assistance to the TRIZ expert during his activity.

Research Scope

The research described in this thesis was conducted at the Laboratoire du Génie de la Conception (LGECO) and Knowledge Engineering Group of Bioinformatique théorique, Fouille de données et Optimisation stochastique (BFO) team of the Laboratoire des Sciences de l'Ingénieur, de l'Informatique et de l'Imagerie (ICUBE).

The research at LGECO is made in collaboration with industrial companies. It is characterized by its multidisciplinary approach, and the different research topics include diverse fields, such as production systems, mechanical and management sciences. The LGECO is a well known team that has been working for several years now on inventive design aspects. There are three main axes of representation for the research activities at LGECO, that is, the Research-Invention & Development (RID) Process for Early Stage of Innovation, the Human & Management Resources, and the Fields of Application for Inventive Design Process. Several ongoing research works are closely related to ours, such as Souili [15] who uses patent mining techniques to instantiate TRIZ ontologies.

The originality of the works developed within the Knowledge Engineering group, BFO team in ICUBE is founded on the proposal of a layered architecture Knowledge, Rules and Experience to manage the complexity of the development of a Knowledge based System (KBS). The Knowledge layer contains domain ontologies, the Rules layer allows different types of reasoning (monotonic, spatial, temporal, fuzzy, or others) depending on the application; and finally, the Experience layer allows the capitalization and reuse of prior knowledge, either by the use of Case-Based Reasoning (CBR) [16] or Set of Experience Knowledge Structure (SOEKS) and Decisional DNA (DDNA) [17] or by other mechanisms.

Figure 1.3 shows the focus of our research in the laboratories BFO/ICUBE and LGECO. The research in the Knowledge Engineering group of BFO/ICUBE can be divided into four branches according to the different areas of application:

• Semantic Region Labeling for Remote Sensing Image Interpretation.

• Semantic Technologies for Computer Aided Inventive Design.

• Formalization of Theoretical and Experience Knowledge for the Analysis and Diagnosis of SMEs.

• Environment and Sustainable Development.

This thesis focuses on the use of semantic technologies in the area of Computer Aided Inventive Design. At the same time, the research of RID Process in LGECO consists of four phases: Inventing, Modeling, Optimizing and Engineering, in which the user can find opportunities to generate creative ideas, and then transforms these ideas to concepts. However, there are still no standard problem formulation and repeatable procedure of generating ideas in this stage. Thus, this research focuses on Inventing and the transformation from Inventing to Modeling. GEP based on semantic similarity and case-based reasoning are developed.

Thesis Structure

• Step2-The Use of Inventive Standards: Using the methods of calculating semantic similarity to define the missing links among the TRIZ knowledge sources, formalizing the TRIZ knowledge sources based on ontology and searching for the heuristic abstract solution based on ontology reasoning.

• Step3-The Use of Physical Effects (Section 2.1.6): Formalizing the physical effects based on ontology and Retrieving the heuristic physical effects based on ontology reasoning.

As shown in Figure 1.5, the structure of the whole thesis is:

• Introduction -Chapter 1 gives a general introduction for the research background, research problem, motivation and research scope.

• Theoretical Foundations -Chapter 2 presents the theoretical foundations of this thesis, including a detailed literature review on TRIZ, the related knowledge about ontology-based knowledge modeling and ontology reasoning and their application status in the domain of Inventive Design.

Thesis Structure

• Contributions -Chapter 3 introduces the proposed methods of calculating semantic similarity in our research, which are used to match SPs and GEPs in the process of using inventive principles and to define the missing links among the TRIZ knowledge sources in the use of inventive standards.

-Chapter 4 corresponds to the first step of the proposed automatic ontologybased mechanism for solving inventive problems, in which the inventive principles ontology is firstly presented, and then the methods presented in Chapter 3 are used to match SPs and GEPs. In order to improve the performance of matching, the results of previous similar projects are suggested to the users according to a case-based reasoning approach.

-Chapter 5, corresponding to the second step, gives the detailed process of searching heuristic abstract solutions, including using the methods proposed in Chapter 3 to define the missing links among the TRIZ knowledge sources, the construction of the inventive standards ontology, and the search of heuristic abstract solutions based on ontology reasoning.

-Chapter 6 discusses the last step, that is, the ontology-based approach of using physical effects, including the re-formalization of physical effects based on ontology and the query of physical effects based on ontology reasoning.

• Validation -In Chapter 7, a prototype developed based on the research of this thesis is presented, and the resolution of a specific case using this prototype is also given.

• Conclusions and Perspectives

-Chapter 8 concludes our study and recommends several potential directions for future research.

Introduction Theoretical Foundations Contributions Validation Conclusions and Perspectives

Chapter 2 Theoretical Foundations

This chapter includes the TRIZ background and the general knowledge about Ontologybased Knowledge Modeling and Ontology Reasoning. At first, the definition of TRIZ, the process of using TRIZ to solve inventive problems and the basic tools of TRIZ, including the contradictions, the patterns of evolution, the TRIZ knowledge sources and the physical-chemical-geometrical effects, will be introduced. Then, in the parts of Ontology Modeling and Reasoning, an overview of their basic methods and concepts will be firstly given, and then the current situation of their applications in the field of inventive design will be illustrated. In the summary, this chapter will be concluded to show the necessity of this thesis.

The Theory of Inventive Problem Solving -TRIZ

The Theory of Inventive Problem Solving (TRIZ) was developed by G. Altshuller in Russia from 1946 to 1985. After initially reviewing over two hundred thousand patents, Altshuller focused on forty thousand of them as representative of inventive problems, based on which many findings of TRIZ were published. With the increasing exposure in introducing TRIZ, more and more people have been impressed by the power of this systematic innovative approach to creativity. Now it is known as a powerful methodology for technical problems solving that leads to enhancement of existing technique and strong acceleration of progress [1]. Furthermore, several extensions of TRIZ have been proposed, such as OTSM-TRIZ or IDM (Inventive Design Methodology), which is explored to deal with complex industrial problems and 2.1. The Theory of Inventive Problem Solving -TRIZ intended to process problems with hundreds of parameters, contradictions and problems not necessarily linked to inventive product design (for example, in software engineering or in the organization field) [2,3].

Definition of TRIZ

A definition "TRIZ is a human-oriented knowledge-based systematic methodology of inventive problem solving" is proposed by Savrancky in 2000 [1], and the explanation to this definition is:

• Human-oriented It is a human being instead of a machine to orient heuristics since the TRIZ practice depends on the problem itself and socioeconomic circumstances.

• Knowledge-based The knowledge about the generic problem solving heuristics is extracted from thousands of patents in different engineering fields. TRIZ uses not only the knowledge in the natural and engineering sciences, but also the knowledge about the domain in which the specific problem occurs.

• Systematic TRIZ provides the effective application of the existing resolutions to new problems, and the procedures to creativity are systematically structured.

• Inventive problem solving TRIZ aims to solve inventive problems, in which only one main contradiction is to be solved.

The Approach of TRIZ Solving Inventive Problem

During his research, Altshuller proved that a systematic approach to tackle inventive problems following a well-defined process is possible. A major conclusion of Altshuller's studies was that inventions were not the result of unorganized thinking, but instead the products of objective laws and recurrent trends of technology evolution.

Comprehensive studies of patent collections following this discovery resulted in two more findings. Firstly, Altshuller shows that an inventive solution results from the elimination of a contradiction which is caused by attempts to improve preceding products characteristics. Attempts to compromise without eliminating the contradiction do not allow a designer to achieve a degree of improvement qualified as "inventive". The second conclusion is that the majority of the patented inventions comply with a

Specific inventive technical problem

Informal problem

Abstract problem relatively small set of basic principles for eliminating the contradictions. Based on these findings, Altshuller has developed a scientifically-based problem solving methodology which codifies numerous inventive principles and, in some techniques, incorporates the laws of engineering system evolution.

Figure .2.1 describes the way classical TRIZ solves problems, and the interrelations among its different parts. Three different phases are clearly identified:

• The "formulation" phase, where the expert uses different tools to express the problem in the form of a contradiction (see later Section 2.1.3) network or another model.

• The "abstract solution finding" phase, where access to different knowledge bases is made to get one or more solution models. Generally, in this step, TRIZ users are required to have wide experience on the TRIZ knowledge sources. They need to be capable of choosing the accurate abstract solution according to the current abstract problem.

• The "interpretation" phase, where these solution models are instantiated with the 2.1. The Theory of Inventive Problem Solving -TRIZ help of the physical-chemical-geometrical effects knowledge base, to get one or more solutions to be implemented in the real world.

Contradiction

A goal of the design process is to map a function onto a physical principle that would be capable of performing the function. However, in the situations when it is not available to perform an exact function or none of the previous solutions meet the new specifications, inventive design is difficult to perform due to the uncertainty on how an original problem can be translated into the functional specifications.

From this point of view, the most important TRIZ achievement was that it reveals the common cause of inventive design problems: contradictions. A contradiction arises from mutually exclusive demands that may be placed on the same system where compromising does not produce the required result. Instead of solving inventive problems ad-hoc, TRIZ states that to obtain an inventive solution, a contradiction must be eliminated whereas compromise or optimization methods are not allowed, and it introduces principles for the formulation and elimination of the contradictions in a systematic way.

There are three types of contradictions, that is, administrative contradiction, technical contradiction and physical contradiction.

An administrative contradiction does not reveal any contradictory aspect, and it often describes a desire to improve a characteristic of a system without having an emerging direction of resolution. Its syntax is: I would like to [describe the desired characteristic of the studied system], but I do not know how.

For example, in the case of a "clothespin", an administrative contradiction might be: I would like to [prevent the clothespin from marking the laundry] but I do not know how. An analogy can be made at this stage between the part of the sentence regarding willingness to get an improvement and the notion of function as in functional analysis [4].

Taking into account that the administrative contradiction has no heuristic value and cannot indicate a direction to the answer, only technical and physical contradictions are considered because there are more concrete and interconnected.

Technical Contradiction

A technical contradiction arises when it is required to improve some feature of the existing prototype but all solutions known within the domain do not produce the required result or their use would cause a negative effect. The impossibility to improve one parameter and to prevent another important parameter from deterioration is the main feature which separates inventive problems from problems that can be solved by a procedure of routine design.

A technical contradiction describes the state of a system where there is an action having a useful effect but causing simultaneously an undesirable effect. In our example of the "clothespin", it could be said that trying not to mark the laundry generates an undesired effect: the laundry will be loosely fixed on the wire.

The resolution of a technical contradiction is mainly performed using reasoning by analogy, facilitated by the use of two TRIZ components: the inventive principles and the contradiction matrix (see later Section 2.1.5).

Physical Contradiction

A more precise form of contradictions arising in engineering is a physical contradiction, which indicates that a part of a design prototype should have two mutually exclusive values of the same physical parameter. To eliminate the physical contradiction, it is necessary to change the existing physical principle(s) the prototype is comprised of. Formulation of the physical contradiction makes it possible to identify precisely which part of the prototype is responsible for producing the negative effect. It is important to note that if the given problem may not be solved by using the principles for elimination of technical contradictions, such a situation indicates that a physical contradiction is present in a system.

A technical contradiction requires the definition of several parameters associated to the technical system or of any of its elements. The physical contradiction addresses the part of the technical contradiction centered on that parameter that must have at the same time two opposite values. When acting on a parameter to satisfy a requirement of the problem, it may appear that it must simultaneously have at the same time a value x and x (the opposite value of x). In the example of the "clothespin", it can be said that "the stiffness of the spring" must be "hard" to fix the laundry and "soft" not to mark it. There are two main schemes to represent contradictions. As shown in Figure 2.2 and Figure 2.3, the physical contradictions of the case of a "clothespin" are:

• If the stiffness of the spring is hard, the clothespin can fix the laundry better, but it worse prevents marking the laundry.

• If the stiffness of the spring is soft, the clothespin can prevent marking the laundry better, but it worse fixes the laundry.

Patterns of Evolution

Based on a long-term study of patent collections and hundreds of bibliographical sources, G. S. Altshuller discovered that the technical systems do not evolve at random but that they follow certain patterns, which he called "Laws of Evolution" or "Patterns of Evolution" [5].

These patterns can be revealed from the patent literature and analysis of system development, and purposefully used for systems development without numerous blind trials.

There are eight patterns of evolution, which present a systemic view of the conception of technical products:

• Technology follows a life cycle of birth, growth, maturity, and decline.

• Evolution toward increased ideality.

• Non-uniform development of systems elements.

• Evolution toward increased dynamism and controllability.

• Increased complexity then simplification (reduction) through integration.

• Evolution with matching and mismatching components.

• Evolution toward micro-level and increased use of energy fields to achieve better performance or control.

• Evolution toward decreased human involvement.

These patterns of evolution are potentially very useful because they describe what has repeatedly happened in the past for successful technologies, and consequently indicate what is likely to happen in the future. They can, in other words, be used to predict or at least suggest the next likely and beneficial technical development of a particular industry, for technical systems or their sub-systems [6].

The TRIZ Knowledge Sources

The knowledge sources for solving inventive problems including forty inventive principles, seventy-six inventive standards and eleven separation methods, are used to eliminate technical contradictions, provide common problem-solving methods and eliminate physical contradictions respectively. They are expressed in natural language and, although they 2.1. The Theory of Inventive Problem Solving -TRIZ are about related notions, the level of abstraction of the description is very different, making it difficult to understand the links among them 1 .

Inventive Principles

The first TRIZ problem solving technique was a collection of inventive principles aimed at eliminating technical contradictions [5]. They are heuristic principles based on the accumulated and generalized previous experience of inventors and are available in a form that is independent of any particular engineering domain.

Altshuller formulated thirty-nine generalized engineering parameters (Appendix A), like "the weight of a moving object" or "speed", and built a contradiction matrix (Appendix B), to make the inventive principles (Appendix C) applicable in a systematic way.

A new problem can be solved by the use of a proper inventive principle, after the problem has been formulated as a technical contradiction in terms of predefined generic engineering parameters: "a generic engineering parameter to be improved versus the other generic engineering parameter which deteriorates". Forty inventive principles aimed at resolving contradictions between generic engineering parameters are known, sometimes declined in sub-principles. In this research, these sub-principles are located in order to have a uniform granularity.

The inventive principles can be used in a systematic way by accessing the principles through indices in the contradiction matrix. Along the vertical axis of this matrix the generic engineering parameters which have to be improved are specified while along the horizontal axis the parameters which deteriorate as a result of the improvement are specified. These parameters can be looked up along the vertical and horizontal axes and the matrix suggests up to four principles that can be used to solve the contradiction.

Selected principles are ordered using statistical analysis according to their applicability. The principle chosen most often in the past will most likely solve the next problem and is given first.

Example 2 : Improving the strength of an object (the improvement feature) which consequentially gives rise to an undesirable conflict with the weight of the object (for example, it can be the case of a table). Using the contradiction matrix to identify which 1 The whole texts of the TRIZ knowledge sources can be found: inventive principles (Appendix C), inventive standards (Appendix D) and separation methods (Appendix E).

2 Adapted from [6].

inventive principles to use to get strength without increasing weight suggests the following ones:

• IP 40 Composite Materials

• IP 26 Copying • IP 27 Cheap Short -Living Object • IP 1 Segmentation
To understand and apply the suggested solutions it is needed to ask which all the desired benefits of the system are and which the priority of those benefits is.

• InventiveP rinciple 40: Composite Materials suggests using a strong but light material and together with the trigger of IP 1 Segmentation might suggest how to save weight by only putting strength where we need it.

• InventiveP rinciple 26: Copying suggests that we replace a strong and heavy material with one which is strong but lighter. It might be used a material that looks like wood but is actually a stronger copy material. This principle suggests to copy the heavy material with one which gives the needed strength but has a more appropriate weight. Another example of using this principle copying would be in testing something like a drill which is to be used in remote areas of inaccessible and very hard rock. The tests could be on a copy of rock -artificial rock which simulates natural rock in terms of strength in that it loads the drill with high torque, but could be made of a much lighter composite material which is more easily available than remote rock and easier to handle.

• InventiveP rinciple 27: Cheap Short -Living Object suggests something like a cardboard box -which assumes that durability is not a requirement. It is needed to understand all the requirements of the system (and the priority of benefits) to judge whether this is an appropriate solution or not.

• InventiveP rinciple 1: Segmentation represents most of the modern design of tables today: segmented tops for different sizes; segmented table legs for ease of manufacture and assembly; segmented joining of different materials for extra strength.

According to the analysis of principles in [7], the forty inventive principles can be divided into two kinds: thirty-three distinct principles and seven obscure principles. Distinct principles have obvious descriptive information and always share similar or common words with other principles, such as Inventive Principle 25 "Self-service" with the prefix "self-". Obscure principles share few similar and common words with others and it is difficult to compare with others only by text information. For example, Inventive Principle 2 "Extraction" describes the extracted objects or systems, without specifying the relationship of extraction by obvious text information. In this research, the distinct principles are directly used while the additional information, such as the similar words, need to be added to the obscure principles to ease their matching.

Inventive Standards

They are drawn from the fact that most inventions refer to conceptual modification of physical systems. This means that there should be some common problem solving methods applicable to the whole group of similar inventive problems. If problems from different domains result in identical physical models, this means that the problems are similar. Therefore, they can be solved by applying the same method. Standards are built in the form of recommendations, and generally, formulated as rules like If < Condition1 > and < Condition2 > then < Recommendation >. Both conditions permit recognizing the typology of the problem associated to the standard. This way, for a built problem model, there exist a certain number of recommendations allowing the construction of the corresponding solution model. In TRIZ, seventy-six inventive standards (Appendix D) are available. A problem with these inventive standards is that they are formulated abstractly, so their practical use is quite difficult. Some examples include:

• Inventive Standard 2.4.12 : Applying electrorheologic fluid.

(a) If a magnetic fluid cannot be used, the electrorheologic fluid may be useful.

• Inventive Standard 5.3.1 : Changing of phase state.

(a) Efficiency of the use of substance without introducing other substances is improved by changing its phase.

Although Su-Field analysis provides a simple means to model systems and reveal their problems, more than seventy standard solutions may make users rather confused and overwhelmed in their search for answers from these many possible solutions. As a result, several classifications of inventive standards were proposed to help the users to find the right solutions in an easy manner.

According to [8], the seventy-six inventive standards are firstly extended to eightyfour standard solutions taking into account that Inventive Standard 5.1.1 is divided into nine independent standard solutions. Then the eighty-four standard solutions are divided into four classes, that is, A-class: forty inventive standards generalized into seven general solutions, B-class: thirty-one inventive standards identified as some of the existing inventive principles, C-class: six inventive standards considered as new inventive principles, and D-class: seven inventive standards originated from the patterns of evolution (Section 2.1.4).

• A-class: Forty inventive standards are condensed and generalized into seven general solutions, which can improve all types of problematic Su-Field models, being incomplete or with harmful, insufficient or excessive impact. These seven generalized standard solutions are discussed as following1 :

-General Solution 1: Complete an Incomplete Su-Field Model. Complete a Su-Field model if any of its three components is missing, for example, as shown in Figure 2.4, the missing field F M needs to be added to connect S 2 to S 1 . -General Solution 3: Modify S 1 to be Insensitive or Less Sensitive to Harmful Impact. The physical and/or chemical characteristics of substance S 1 may be altered internally or externally, so that it becomes less sensitive or insensitive to a harmful impact, as seen in Figure 2.6. The modification may be either temporary or permanent. Additives may be needed in this modification. -General Solution 4: Change Existing Field to Reduce or Eliminate Harmful Impact. Changing the existing field while keeping the same substances may be a choice to reduce or remove the harmful impact, as shown in Figure 2.7.

Modify Substance S 1

Changing the existing field means increasing, decreasing the existing field, or completely removing the existing field and using another one.

-General Solution 5: Eliminate, Neutralize or Isolate Harmful Impact Using Another Counteractive Field F X . In a system in which harmful impact exists and substances S 1 and S 2 have to co-exist, a counteractive field of F X may be introduced to remove, neutralize or isolate the harmful impact as shown in Figure 2.8. Neither S 2 nor S 1 will change its physical and chemical characteristics in this solution. A third substance will be introduced to provide field F X . -General Solution 6: Introduce a Positive Field. Another field may be added to work with the current field in order to increase the useful effect and reduce the negative effect of the existing system while keeping all substances intact, as shown in Figure 2.9. • B-class: Thirty-one standards are identified as the implementation of existing inventive principles. On the one hand, the matches between the inventive principles and this kind of inventive standards are pre-stored in database, on the other hand, this kind of inventive principles are also considered as the candidates to be matched with the given inventive principles based on the proposed semantic similarity methods.

Change Existing Field

Introduce Counteractive Field

Introduce Another Positive Field

• C-class: Six standards are considered as new inventive principles since they cannot be explained using the Su-Field model but have the nature of inventive principles.

The six standards are:

-Inventive Standard 3.1.5 : System transition: opposite features of the whole and parts.

-Inventive Standard 5.1.1.3 : Use an external additive instead of an internal one.

-Inventive Standard 5.1.1.9 : Obtain the required additive by decomposition of either the environment or the object itself.

-Inventive Standard 5.2.2 : Use fields that are present in the environment.

-Inventive Standard 5.2.3 : Use substances that are the sources of fields.

-Inventive Standard 5.4.2 : Strengthen the output field when there is a weak input field.

• D-class: Seven standards that are originated from the patterns of evolution.

-Inventive Standard 2.2.2 originated from P attern of Evolution 6 (Transform from macro-system to micro-system).

-Inventive Standard 2.2.4 originated from P attern of Evolution 7 (Increase dynamism and controllability).

-Inventive Standard 3.1.2 originated from P attern of Evolution 4 (Match and mismatch).

-Inventive Standard 3.1.3 originated from P attern of Evolution 7 (Increase dynamism and controllability).

-Inventive Standard 3.1.4 originated from P attern of Evolution 5 (Increase complexity, then follow with simplicity through integration).

-Inventive Standard 3.2.1 originated from P attern of Evolution 6 (Transform from macro-system to micro-system).

-Inventive Standard 4.1.1 originated from P attern of Evolution 2 (Increase ideality).

Separation Methods

An advanced form of contradictions are the physical contradictions [5]. To model an inventive problem as a physical contradiction, a physical object of a prototypical design that must have two conflicting properties has to be identified. To solve problems containing physical contradictions, separation methods (Appendix E) for physical contradiction elimination are used. Among them, there are:

• Separation of conflicting properties in time.

• Separation of conflicting properties in space.

• Separation of conflicting properties by system transition.

• Separation of conflicting properties by phase transition.

• Separation of conflicting properties by physical-chemical transition.

As stated above, these methods are very general. Consequently, they are very hard to use without additional support by knowledge that is more specific. The output of using these principles might not be directly in the form of a design concept or in the form of any specific physical function to perform. This problem might be tackled by structuring inventive principles according to more general principles for eliminating physical contradictions.

Physical-Chemical-Geometrical Effects

While inventive principles, separation methods and inventive standards do not produce recommendations in terms of what physical substances or fields should be used, the collection of physical-chemical-geometrical effects (also called physical effects for short) provides the mapping between technical functions and known natural laws.

Studies of the patent collections indicated that the best and, as a consequence, more ideal inventive solutions are obtained by the use of natural phenomena previously unused in the engineering domain. Knowledge of natural phenomena often makes possible to avoid the development of complex and unreliable designs.

Example: Instead of a mechanical design including many parts for the precise displacement of an object for a short distance, it is possible to apply the effect of thermal expansion to control it.

As shown in Figure 2.11, the pointers to physical effects are defined to represent the way to use these effects. In order to facilitate this process, several different approaches and software were proposed. Sushkov [9] proposed an approach to model physical knowledge in terms of generic components and integrated inventive standards and sharable physical effects based on ontology. However, only the TRIZ knowledge sources are formalized using ontology while the further development, such as, using ontology inference to obtain The CREAX database1 organises a database of effects by function, and uses a webbased application to support the search of effects. As shown in Figure 2.12, it consists of two databases, that is, the Function Database and the Attribute Database. In the Function Database, a list of Functions (Pointers), such as, "Absorb" and "Produce", and four objects -"Solid", "Liquid", "Gas" and "Field" are pre-defined. The user needs to choose a Function and an object, such as, "Absorb Gas", and then all the related physical effects are obtained. In the Attribute Database, there are a series of Attributes, which describe the different attributes of technical systems or objects, such as, "Colour" and "Speed", and five kinds of behaviour -"Changing", "Decreasing", "Measuring", "Increasing" and "Stabilising" to define the different modifications of the Attribute. Similarly, the user also needs to select an Attribute and a kind of behaviour to search for the appropriate physical effects. However, this system responds to user-entered query by processing the query to a combination of two pre-stored key words, which is quite limited in the specific 2.1. The Theory of Inventive Problem Solving -TRIZ Figure 2.13: The scientific effects in the Invention Machine's Goldfire. applications. For example, if the user query comprises words that lack all the key words pre-stored, then the system cannot respond with any stored answer or it may respond with an incorrect pre-stored answer. Furthermore, the classification of objects -"Solid", "Liquid", "Gas" and "Field" is at high level of abstraction, and too many physical effects are eligible for a specific application. In this application, the query with key word is considered as the supplementary means. Invention Machine's Goldfire1 , a commercial software product from Invention Machine, provides tools for the organization's management of technology and for the individual's researching and engineering tasks. As shown in Figure 2.13, the similar research in GoldFire is the scientific effects tool, which helps to stimulate creative problem solving by browsing and searching effects in the Invention Machine Scientific Effects Database.

The physical effects are divided into twenty-six classes according to different functions, such as, "Substance: Absorb/Adsorb", and then for each class, there are several subclasses, for example, for the class "Substance: Absorb/Adsorb", the sub-classes are:

• "absorb liquid substances"

• "absorb/adsorb chemical compounds"

• "absorb/adsorb gas"

• "absorb/adsorb molecular and sub-molecular particles"

• "absorb/adsorb structured substances"

• "absorb/adsorb technical objects and substances"

• "adsorb particles" Each sub-class also consists of several sub-subsidiary classes, for example, "absorb/adsorb gas" is made up of "absorb/adsorb gas" and "absorb/adsorb vapor". As a result, the user needs to locate the specific case level-by-level to obtain the physical effects. With the good performance of the proposed semantic methods and the standard representative way of problem and solution, the results are almost satisfied. However, the hierarchy of the physical effects is stable and any modification of the classification needs to be maintained manually. Furthermore, the semantic search, only depending on the matching among two words with the same role in the sentence, cannot provide more extensive semantic information, such as, a physical effect related to "absorb vapor" is also an effect to "absorb gas".

As stated above, various existing approaches to choose physical effects are still completely or partly operated by TRIZ users, requiring a high expertise in TRIZ usage to appropriately manipulate these concepts. In order to facilitate this process, a new way to formalize physical effects and a new method of using physical effects are required, aiming at obtaining better results while requiring less expertise to complete the tasks.

Ontology-based Knowledge Modeling

Definition of Ontology

The term ontology originated from Philosophy, where it means a description of the nature of being (a "theory of existence"). In Artificial Intelligence (AI), the most frequently quoted definition is "an explicit specification of a conceptualization" [10]. The main difference is an ontology in Philosophy is a systematic and exhaustive account of Existence, only driven by the task of making the ontology. When making an AI-ontology, there should always be a task in mind, which means all the related restrictions of concepts need to be included in the ontology.

According to [11] a task neutral ontology intended to represent the common sense needed by any application does not exist, nor is it realistic it ever will exist. [12] States that Gruber's definition -"an ontology is a specification of a conceptualization", left room for too many possible interpretations, and despite an attempt to clarify and formalize the definition further by Guarino [13], new meanings of the term "ontology" continued to proliferate. W elty, Lehmann, Gruninger, and U schold reported in 1999 on a wide spectrum of information artifacts that had been at some time classified as ontologies. The results of their work are shown in Figure 2.14 [14].

• A catalog is nothing more than a numbered list of all relevant classes/concepts.

• A slightly more complex information system may provide simple natural language 2. Theoretical Foundations 37 texts and allow string matching.

• Glossaries are indexed catalogs with a natural language description of each concept/class.

• Thesauri are glossaries with a hierarchical structure.

• Taxonomies are thesauri where the properties of more general classes/concepts are inherited by the more specific classes/concepts.

• Frame-based systems are taxonomies, which provide relations between objects and restrictions on what and how classes/concepts of objects can be related to each other.

• The most expressive and complex information system ontologies use the axioms of full first order logic, higher order, or modal logic.

There are basically four building blocks in an ontology:

• Classes (or sometimes called concepts): Classes provide an abstraction mechanism for grouping resources with similar characteristics.

• Properties: Properties describe the features and attributes of the classes and consist of two main categories, that is, Object properties (link individuals to individuals) and Datatype properties (link individuals to data values).

• Restrictions: Restrictions describe the value a property is allowed to have. A property restriction is a special kind of class description. It describes an anonymous class, namely a class of all individuals that satisfy the restriction.

• Individuals: Individuals are instances of a class with certain restrictions.

In order to represent the conceptual architecture of the ontologies in this research, a framework of the ontology, including main Classes and Properties, is used in the following sections.

Web Ontology Language -OWL

The Web Ontology Language (OWL), is an integral component of the Semantic Web, as it can be used to write ontologies or formal vocabularies which form the basis for semantic web data markup and exchange [15].

OWL is a fairly recent language, released as a W3C (World Wide Consortium) recommendation in February 2004. As a part of the Semantic Web stack of languages, OWL is layered on top of RDF (basic assertional language) and RDFS (schema language extension for RDF) which themselves are layered over XML [16]. From its relationship with RDF comes the official OWL exchange syntax, namely RDF/XML [17]. In fact, OWL shares many features in common with RDF such as the use of Universal Resource Identifiers (URI) to unambiguously refer to web resources.

OWL consists of three increasingly expressive sub-languages or "species", that is, OWL-Lite, OWL-DL and OWL-Full, each of which corresponds to different requirements [START_REF] Antoniou | Web Ontology Language: OWL[END_REF]:

• OWL Full: The entire language is called OWL Full, and uses all the OWL languages primitives. It also allows to combine these primitives in arbitrary ways with RDF and RDF Schema. This includes the possibility (also present in RDF) to change the meaning of the pre-defined (RDF or OWL) primitives, by applying the language primitives to each other. For example, in OWL Full we could impose a cardinality constraint on the classification of all classes, essentially limiting the number of classes that can be described in any ontology. The advantage of OWL Full is that it is fully upward compatible with RDF, both syntactically and semantically: any legal RDF document is also a legal OWL Full document, and any valid RDF/RDF Schema conclusion is also a valid OWL Full conclusion. The disadvantage of OWL Full is the language has become so powerful as to be undecidable, dashing any hope of complete (let alone efficient) reasoning support.

• OWL DL: In order to regain computational efficiency, OWL DL (short for: Description Logic) is a sub-language of OWL Full which restricts the way in which the constructors from OWL and RDF can be used. The amounts to disallowing application of OWL's constructor's to each other ensure that the language corresponds to a well studied description logic. The advantage of this sub-language is that it permits efficient reasoning support and the disadvantage is that we loose full compatibility with RDF: an RDF document will in general have to be extended in some ways and restricted in others before it is a legal OWL DL document. Conversely, every legal OWL DL document is still a legal RDF document.

• OWL Lite: An ever further restriction limits OWL DL to a subset of the language constructors. For example, OWL Lite excludes enumerated classes, disjointness statements and arbitrary cardinality (among others). The advantage of this sublanguage is a language that is both easier to grasp (for users) and easier to implement (for tool builders) while obviously, the disadvantage is a restricted expressivity.

During the process of ontology modeling, which sub-language is used depends on many factors. However, there are some regular rules as following1 :

• The choice between OWL Lite and OWL DL depends on the extent to which users require the more-expressive constructs provided by OWL Lite and OWL DL.

• The choice between OWL DL and OWL Full mainly depends on the extent to which users require the meta-modeling facilities of RDF Schema (e.g. defining classes of classes, or attaching properties to classes).

There are strict notions of upward compatibility between these three sub-languages [START_REF] Antoniou | Web Ontology Language: OWL[END_REF]:

• Every legal OWL Lite ontology is a legal OWL DL ontology.

• Every legal OWL DL ontology is a legal OWL Full ontology.

• Every valid OWL Lite conclusion is a valid OWL DL conclusion.

• Every valid OWL DL conclusion is a valid OWL Full conclusion.

In this research, OWL-DL is chosen to formalize the knowledge for the following reasons:

• OWL-DL is a web-oriented ontology language and adapts to the situation of sharing domain knowledge through web-based applications and systems.

• OWL-DL is a sophisticated language, that is, on the one hand, it shares useful language constructs and design features with its predecessors, such as RDF(S); on the other hand, it adds desirable features through appropriate extensions to satisfy conflicting requirements.

• OWL-DL uses the Description Logic (DL) model, which specifies the meaning of ontology in rigorously well-defined logic semantics and in a format that can be further processed by programming. This also enables the automatic reasoning about knowledge sources, avoiding the terminological ambiguity.

Ontology-based Knowledge Modeling for Inventive Design

Several works have been carried out regarding the formalization of TRIZ knowledge based on ontology.

[19] intended a formalization of the TRIZ concepts and their relationships with the operational goal of having a computer-aided TRIZ problem formulation tool. This tool progressively builds a model of the problem according to different TRIZ points of view. The given problem is formulated according to different axes: contradictions, and functional and substance-field modeling. This is assured by the developed ontology which links all the TRIZ concepts together. A new ontological description of the TRIZ domain was also engaged with the operational goal of having a TRIZ problem-solving software tool (and not a tool for pure problem formulation as in [START_REF] Dubois | A model for problems' representation at various generic levels to assist inventive design[END_REF]). With this aim, problem-solving knowledge has to be incorporated. [START_REF] Bultey | A hybrid system combining description logics and rules for inventive design[END_REF] stated that substance-field modeling provided this kind of knowledge and proposed the enrichment of the former TRIZ ontology developed by [START_REF] Dubois | A model for problems' representation at various generic levels to assist inventive design[END_REF]. This new development has given birth to three ontologies: a general TRIZ ontology, a substance-field ontology and a scientific-engineering effects ontology. Also, the originality of these works is the modeling of substances in terms of matter and bonding fields, which makes the relationship with the physical effects ontology straightforward. [3] proposed the logical formalization of the IDM ontology, based on which the software tools were developed to assist the TRIZ experts in the conduction of the inventive design study, from problem formulation to the proposal of solution concepts.

There are also other attempts of formalization in the literature, but these propositions mainly rely on functional approaches, or the goal of the formalizations is not to build a knowledge-based system to assist in the formulation and solving of inventive design problems.

[21] proposed an information infrastructure for innovative product design, and the ontology developed did not propose concepts for the analysis of complex initial situations. [START_REF] Huang | Integrated architecture for triz based on knowledge network and its key technologies[END_REF] presented an integrated architecture for innovation knowledge acquisition and TRIZ knowledge network collaborative construction based on meta-ontologies and Web 2.0.

[23] presented the use of first-order logic to improve upon currently employed engineering design knowledge management techniques. Specifically, this work used description logics with Horn logics, not only to guide the knowledge acquisition process, but also to offer much needed support in decision-making during the engineering design process in a distributed environment. [START_REF] Yim | Development of an ontology for bio-inspired design using description logics[END_REF] proposed an ontology used to capture, retrieve and reuse novel bio-inspired design solutions and their associated physical architectures, behaviours, functions and strategies. These works intended to facilitate the use of the pointers to scientific-engineering effects.

Finally, [START_REF] Cascini | Functional modelling for triz-based evolutionary analyses[END_REF] proposed the use of functional analysis with TRIZ-based tools to improve the efficiency of Altshuller's laws of engineering system evolution as a means to produce technology forecasts.

However, their research is concerned with one or more parts of the TRIZ knowledge souces, not yielding a comprehensive formalization for the TRIZ knowledge sources and their relations. Consequently, this thesis focuses on the formalization of the TRIZ knowledge sources, to complete the model and make it wholly consistent by the definition of the missing semantic links. This formalization should allow the development of an intelligent management system of these knowledge sources, with the aim of assisting the TRIZ expert during his activity.

Ontology Reasoning

Semantic Web Rule Language -SWRL

The SWRL [START_REF] Horrocks | Swrl: A semantic web rule language combining owl and ruleml[END_REF], proposed by W3C, is an expressive OWL-based rule language. It is based on a combination of OWL DL and OWL Lite sub-languages of OWL with the 2.3. Ontology Reasoning Unary/Binary Datalog RuleML sub-languages of the Rule Markup Language1 . The SWRL proposal extends the OWL abstract syntax to include the syntax of these rules and the OWL model-theoretic semantics to provide a formal meaning for ontologies that include rules written in this syntax. The extension is strictly a syntactic and semantic extension, hence has a tight integration to OWL.

SWRL Syntax

A SWRL rule consists of an antecedent (body) and a consequent (head), both consisting of one or more atoms as following:

Antecedent(Atom ∧ Atom...) ⇒ Consequent(Atom ∧ Atom...) (2.1)
In a rule with multiple atoms in the body, the body is treated as a conjunction of its atoms. In a rule with multiple atoms in the head, the head is also treated as a conjunction. But such a rule can be easily transformed into multiple rules each with a single-atom head [START_REF] Horrocks | Swrl: A semantic web rule language combining owl and ruleml[END_REF]. Rule atoms can be of the form2 :

• A(x) -where A is an OWL Class (a simple named class or a class description) or a data range3 and x is either a variable, OWL individual or OWL data value.

• P(x,y) -where P is an OWL Property (an object property or a datatype property),

x is either a variable or an OWL individual and y is either a variable, an OWL individual or an OWL data value.

• sameAs(x,y), differentFrom(x,y) -where x, y are variables or OWL individuals.

• builtIn(r,x,...) -where r is a built-in relation and x,... are OWL data values.

A(x) can be referred to as a concept atom/class atom, P(x,y) can be referred to as a role atom/property atom. According to [START_REF] Horrocks | Swrl: A semantic web rule language combining owl and ruleml[END_REF], variables in a rule are treated as universally quantified, limited to the scope of that rule. Rules must be safe, i.e., only variables that occur in the rule body may occur in the rule head1 . The syntax of SWRL does not allow disjunction of atoms, negation of atoms or any non-monotonic features like negation as failure or defaults.

An informal human-readable syntax for these rules is also specified for ease of readability and a typical rule in this syntax would look like: hasP arent(?x, ?y) ∧ hasBrother(?y, ?z) ⇒ hasU ncle(?x, ?z)

(2.

2)

It states that an individual x having a parent y who has a brother z, has an uncle z.

SWRL Semantics

The SWRL semantics, a straightforward extension of the OWL semantics, is considered as a logical implication between its body and head. The intended meaning is that: whenever the conditions in the rule body hold, then the conditions in the rule head must also hold.

According to the SWRL proposal [START_REF] Horrocks | Swrl: A semantic web rule language combining owl and ruleml[END_REF], the formal semantics for the rules are given by an extension of the OWL interpretation by defining bindings. These bindings map variables in rules to elements in the ontology domain, and an interpretation satisfies an ontology if and only if (iff) it satisfies every axiom (OWL axioms and rule axioms) and fact in the ontology:

• A rule is satisfied by an interpretation iff every binding that satisfies its body also satisfies its head.

• A rule body or rule head is satisfied if the conjunction of its atoms is satisfied.

• A concept atom A(x) is said to be satisfied if x is an instance of the class description or data range A.

• A role atom P(x,y) is said to be satisfied if x is related to y by role P .

• A sameAs(x, y) atom is said to be satisfied if x is interpreted as the same object as y.

• A dif f erentF rom(x, y) atom is said to be satisfied if x and y are interpreted as different objects.

• A builtIn(r, x, ...) atom is said to be satisfied if the built-in relation r holds on the interpretations of the arguments.

Since a rule is treated as a logical implication, the implied meaning is that the contraposition law holds on the implication (rule). For example, whenever the rule: hasP arent(?x, ?y) ∧ hasBrother(?y, ?z) ⇒ hasU ncle(?x, ?z)

(2.3) holds, its contrapositive implication:

¬hasU ncle(?x, ?z) ⇒ ¬(hasP arent(?x, ?y) ∧ hasBrother(?y, ?z)) (2.4) also holds.

In this application, there are two reasons for choosing SWRL to represent the reasoning rules:

• SWRL has the advantages of formal model-theoretic semantics and the close association with OWL, and it can well integrate the OWL-based TRIZ knowledge sources ontologies and physical effects ontology into the rules.

• SWRL is a descriptive language that is independent of concrete rule implementation languages within inference engines, which makes it free to select the inference platforms.

A Rule Engine -Jess

Generally, there are two motivations for choosing an inference engine:

• There is a need for a data representation to enable software products (agents) to provide intelligent access to heterogeneous and distributed information.

• To provide tools that drive down the cost of establishing inter-operability between different data providers. The existing inference engines can be categorized into three classes according to the logic based on which they are implemented [START_REF] Dalwadi | Semantic web and comparative analysis of inference engines[END_REF]:

• Description Logic based Inference Engine: This kind of DL reasoners are used to perform basic reasoning tasks like consistency checking and subsumption concepts.

There are many DL reasoners, such as Racer [START_REF] Haarslev | Racer: A core inference engine for the semantic web[END_REF] and Pellet [START_REF] Parsia | Proceedings of the international semantic web conference[END_REF].

• First Order Logic (FOL) Theorem Prover based Inference Engine: These reasoners work on First Order Logic. The reasoner takes OWL file as input and first translated into FOL, and then inference is processed by using any one of existing automated theorem prover. Semantic reasoners like Hoolet Shell(Jess) [START_REF] Friedman-Hill | Jess, the rule engine for the java platform[END_REF] and Jena1 belong to this kind.

In our application, Jess is selected as the inference engine for the following four reasons:

• Jess works seamlessly with Java and is well documented, which makes easy to use and configure.

• Jess supports mainly forward chaining and, to some extent, backward chaining.

• For user-driven application, there exists SWRLJESSTab2 integrated with the software Protégé as shown in Figure 2.15. The interaction between OWL/SWRL and the JESS rule engine is user-driven with the SWRLJESSTab.

• For Java application, there exists SWRLRuleEngineBridge, a part of Protégé-OWL. This bridge is used to import SWRL rules and relevant OWL classes, individuals and properties from an OWL model and write that knowledge to a rule engine, allow the rule engine to perform inference and to assert its new knowledge back, and insert that asserted knowledge into an OWL model.

The typical rule-based engine keeps a list of rules and continuously cycles through the list, checking each rule's left-hand-side (LHS) against the working memory and executing

Asserted Facts

Step1 S t e p 1

Step2

St ep 3

Figure 2.17: The inference process in Jess.

the right-hand-side (RHS) of any rules that apply. As a rule engine, Jess uses a more efficient method known as the Rete algorithm than the above way [START_REF] Forgy | Rete: A fast algorithm for the many pattern/many object pattern match problem[END_REF], that is, by remembering past testing results across iterations of the rule loop, only new facts are tested against any rule LHSs to which they are most likely to be relevant.

As shown in Figure 2.16, Jess contains a rule base, a working memory, an inference engine and an execution engine. The inferences are carried out in Jess inference engine by matching facts in working memories against the rules in the rule base. By transforming structural knowledge in OWL into Jess facts, and constraint knowledge in SWRL into Jess rules, the inferred results are generated by the Jess inference engine and execution engine.

As shown in Figure 2.17, the inference process of Jess includes three steps:

• Step 1: OWL + SWRL → Jess will transfer the appropriate OWL knowledge and the rule knowledge in SWRL to Jess. This transformation are relatively by straightforward using the "OWL + SWRL → Jess" button in SWRLJESSTab, or using the command importSWRLRulesAndOWLKnowledge() in the interface SWRLRuleEngine1 .

• Step 2: After importing Jess files containing both Jess facts and Jess rules, the inference process is implemented by executing Jess. Pressing the "RUN Jess" button in SWRLJESSTab or use the command run() can initiate this process.

• Step 3: The Jess facts, including asserted facts and inferred facts, are back to OWL knowledge by using the "Jess → OWL" button in SWRLJESSTab or use the command writeInferredKnowledge2OWL().

Generally, Step 2 is executed automatically in Java applications, so only Step 1 and Step 3 need to be considered, which can be divided into the following three phases:

• The Function of OWL → Jess.

• The Function of SWRL → Jess.

• The Execution of the Jess Inference Engine.

Ontology Reasoning for Inventive Design

Summary

As the background of our research, the detailed information about TRIZ -the definition of TRIZ, the application steps of TRIZ solving inventive problems, and the basic tools of TRIZ are firstly presented. Then in order to exploit an automatic ontology-based mechanism for solving inventive problems, the knowledge about ontology modeling and the mechanism of ontology reasoning, are introduced, including their basic methods, concepts and the current situation of their use, especially for the applications in the field of inventive design.

In the next chapter, the methods of calculating semantic similarity, which are foundations of the following work in the automatic ontology-based mechanism, will be introduced.

Chapter 3 Semantic Similarity

This chapter presents the methods for calculating semantic similarity. Firstly, a literature review about semantic similarity is presented, and then the related methods for calculating semantic similarity in the domain of inventive design are given. Finally, two proposed methods including a basic method and an improved method are introduced in detail, to be used in the following sections.

Literature Review

Basic Methods

Semantic similarity is a measure of the likeness of meaning or semantic content assigned to a set of documents or to terms within term lists.

Semantic similarity is a special case of relatedness, which is applicable to taxonomic (IS-A) links [1]. As OWL ontologies are based on class structures which are comprised of IS-A links, semantic similarity measures are directly applicable to OWL ontologies. The methods developed to measure semantic similarity based on thesaurus can be categorized in three groups: Path-based Similarity, Information Content (IC) Similarity and Extended Gloss Overlaps.

Path-based Similarity is calculated according to the intuition: two concepts (senses) are similar if they are near each other in the thesaurus hierarchy. P athlen(c1,c2) is defined as 1 + number of edges in the shortest path in the hypernym graph between two concepts 56 3.1. Literature Review c1 and c2, and the similarity between c1 and c2 is:

sim path (c 1 , c 2 ) = 1 pathlen(c 1 , c 2 ) (3.1)
In IC Similarity [1], P(c) -the probability that a randomly selected word in a corpus is an instance of concept c, is firstly proposed to represent IC:

IC(c) = -logP (c) (3.2)
Resnik method [1] asserts that the more specific (least probable) the nearest common concept under which two concepts can be categorized (lowest common subsume, LCS), the more similar the concepts are, that is,

sim Resnik (c 1 , c 2 ) = -logP (LCS(c 1 , c 2 )) (3.3)
On the contrary, Lin proposed that the more differences between two concepts, the less similar they are, and defined in [2]:

sim Lin (c 1 , c 2 ) = 2logP (LCS(c 1 , c 2 )) logP (c 1 ) + logP (c 2 ) (3.4)
In [3], Jiang and Conrath used the form of the conditional probability of encountering an instance of a child set of synonyms given an instance of a parent set of synonyms, and the similarity is depicted as following:

sim Jiang and Conrath (c 1 , c 2 ) = 1 logP (c 1 ) + logP (c 2 ) -2logP (LCS(c 1 , c 2 )) (3.5)
The Extended Gloss Overlaps measure [4] words by finding overlaps in the glosses of the two synsets (Section 3.3.1), and the relatedness score is the sum of the squares of the overlap lengths.

Methods of Calculating Semantic Similarity between Short Texts

In recent years, several different approaches were proposed to measure the similarity between short texts, and the available work can be classified into three major categories: word co-occurrence/vector-based document model methods, corpus-based methods, and descriptive feature-based methods.

The vector-based document model methods are commonly used in Information Retrieval (IR) systems [5], where the document most relevant to an input query is determined by representing a document as a word vector and matching the query to the similar documents in the document database via a similarity metric [6]. This technique relies on the assumption that more similar documents have more words in common. But it is not always the case that texts with similar meaning necessarily share many words. The sentence representation is not efficient enough since the vector dimension is much larger than the number of words in a short-text, thus, resulting in many null vector components.

The corpus-based similarity includes two well-known methods, the Latent Semantic Analysis (LSA) [7,8] and the Hyperspace Analogues to Language (HAL) model [9]. LSA uses Singular Value Decomposition (SVD) to find the semantic representations of words by analyzing the statistical relationships among words in a large corpus of text. It does not take into account any syntactic information and is thus more appropriate for longer texts. The HAL method uses lexical co-occurrence to produce a high-dimensional semantic space, where words are represented as points, the position of each word along the axes is related to the word's meaning, and the distance between two words measure their relationships. However, the word-by-word matrix does not capture sentence meaning well and the sentence vector becomes diluted as a large number of words are added to it [10].

The feature-based methods try to represent a sentence using a set of predefined features. Similarity between two texts is obtained through a trained classifier. Searching of effective features and obtaining values for these features from sentences make this kind of methods more impractical.

Semantic Similarity for Inventive Design

Semantic Similarity for Inventive Design

To facilitate the automatic classification of patent documents according to inventive principles, Cong and Tong [11] grouped forty inventive principles according to text similarity and meaning similarity. Keeping the classical Matrix unchanged, for all previous patent documents, they obtained the results of classification automatically and for each new problem, they easily sorted it and reused the solutions of patents of the same kind.

As stated in Section 2.1.6, Invention Machine's Goldfire is a software that links a given function search to a compliant list of sentences extracted in both selected websites and patents that seemingly fit the required search. Its key technology is "Semantic Answering System and Method" [12], that is, responding the user query using semantic methods. On the one hand, the solutions are stored in the form Subject-Action-Object in the database; On the other hand, the problem statement for each user query is in the form Action-Object. So if the terms in Action-Object problem statement have similarity with at least one term of the Subject-Action-Object solution, then this solution will be returned.

In order to enhance the capability of developing innovative products of modern design tools, a knowledge portal based on semantic capabilities of text mining technology was proposed in [13] to supply the design with all the required information. The case of "designing a polymeric wheel" in the field of plastic design is considered as an example, and the integrated adoption of the knowledge based tools both for the conceptual design phase (TRIZ-based application) and for the detailed design phase (semantic knowledge portal) has provided satisfactory results in terms of augmented creativity and design efficiency.

Compared with the research stated above, this thesis aims at calculating the semantic similarity among all the TRIZ knowledge sources instead of a single knowledge source. At the same time, different preprocesses are implemented for different knowledge sources to ease their matching. Furthermore, in order to improve the performance, the match with word weight in double direction is proposed (Section 3.3.3).

Our Proposal

The Dictionary-WordNet

WordNet, proposed by George A. Miller in 1980s, is a large lexical database of English [14]. It groups English words into sets of synonyms, namely, "synset", and the sense of each concept is given by a word or a synset in the same contexts. A term can have several senses (that is, a term can belong to several different concepts), according to the context.

WordNet is organized in a hierarchy among concepts, based on an "is-a" relationship. Sub-concepts of a certain concept are called "hyponyms" (as they are more specific than it) and super-concepts are called "hypernyms". Several other relationships among concepts appear in WordNet, such as "synonym" or "antonym".

Compared with other lexical dictionaries, such as Framenet1 , WordNet is chosen as the dictionary for the calculation of semantic similarity owing to the following reasons:

• WordNet interlinks not only word forms, for example, strings of letters, but also specific senses of words. As a result, words that are found in close proximity to one another in the network are semantically disambiguated.

• WordNet labels the semantic relations among words, whereas the groupings of words in a thesaurus does not follow any explicit pattern other than meaning similarity.

Basic Method

The basic matching process mainly includes the following five steps:

1. Short Text Segmentation: There are three methods, that is, sentence segmentation (e.g., use Decision Tree [15] to determine a word is end of sentence or not), word tokenization (e.g., substance appearance -disappearance → <substance, appearance, disappearance>), and word normalization and stemming (e.g., automates, automatic, automation → automat).

Sense Search:

For each word obtained, WordNet is used to look for their corresponding senses, including nouns, verbs, adjectives and adverbs. For example, the noun form of the word 'way' has twelve different senses. 60 3.3. Our Proposal 3. Sense Similarity: Lin's measure is used to calculate the semantic similarity of the senses of two words. With this measure, it is obvious that in WordNet, the higher the rate of sharing information, the more similar two concepts.

Word Similarity:

The maximum sense similarity of the two words is chosen as their word similarity.

Short Text Similarity

: Short text similarity is calculated based on word similarity. Assumed that two sentences:

A 1 , including words sequence A 11 , A 12 • • • A 1n and A 2 , including A 21 , A 22 • • • A 2m . s(A 1i , A 2j ) represents word similarity of A 1i and A 2j ,1 i n, 1 j m. The matrix M (A 1 , A 2
) can be built as following:

           s(A 11 , A 21 ) s(A 11 , A 22 ) • • • s(A 11 , A 2m ) • • • s(A 1i , A 21 ) s(A 1i , A 22 ) • • • s(A 1i , A 2m ) • • • s(A 1n , A 21 ) s(A 1n , A 22 ) • • • s(A 1n , A 2m )            (3.6)
Generally, the most similar words in A 2 for each word in A 1 are selected and then the average value is calculated as following:

s(A 1 , A 2 ) = n i=1 M ax m j=1 (s(A 1i ,A 2j )) n (3.7)

Improved Method with Word Weight

The basic matching approach asserts that all the words in the short-text play an equal role in calculating the semantic similarity. According to the analysis of the documents of TRIZ knowledge sources, words act differently on the short text similarity, for example, the word "transition" can be used to separate several short texts from others, while the words "a" or "an" are useless. As a result, word weight (word frequency), is proposed here to improve the results of the calculation. Given a word in a short text, its word weight is the ratio of the number of times it appears in the short text to the total number of times all words appear in this short text.

Semantic Similarity 61

There is an intuition that the more frequently the word appears, the higher weight it has, and obviously it plays a more important part in the short text comparison. Nevertheless, some words appear too frequently, but they are not good keywords to distinguish relevant and non-relevant short texts, such as the word "the". These words are given high weight without giving enough weight to the more meaningful words, for instance, the word "atmosphere".

Hence an improved method is proposed with the word weight calculated by tf * idf, which were proposed by Gerard Salton [6] and Karen Sparck Jones [16] respectively. tf , the word frequency, also called Local Term Weight, is defined as the number of times a word occurs in a document (short text in our application). For the word w in the particular short text s, the word frequency tf (w, s) can be obtained.

idf , the inverse document frequency, also called Global Term Weight, is based on counting the number of documents (short texts in our application) in the collection being searched that are indexed by the term. According to this research, idf (w, S) is defined as following:

idf (w, S) = log |S| |s ∈ S : w ∈ s| (3.8)
with |S| : cardinality of S (the total number of short texts in the corpus) and s ∈ S : w ∈ s : number of short texts where the word w appears (i.e., tf (w, s) = 0 ).

Finally, the tf * idf is calculated as

tf * idf (w, s, S) = tf (w, s) × idf (w, S) (3.9)
Given two short texts A 1 and A 2 , the most similar words in A 2 for each word in A 1 are selected. However, it is not enough for us to estimate the similarity between A 1 and A 2 without considering the inverse situation, that is, obtain the most similar words in A 1 for each word in A 2 . Taking this into account, the semantic matching with word weight is proposed:

s(A 1 , A 2 ) = n i=1 ww 1i ×M ax m j=1 (s(A 1i ,A 2j )) n + m i=1 ww 2i ×M ax n j=1 (s(A 2i ,A 1j )) m (3.10)
where s(A 1i , A 2j ) represents word similarity of A 1i and A 2j ,1 i n, 1 j m, and s(A 2i , A 1j ) represents word similarity of A 2i and A 1j ,1 i m, 1 j n, calculated 62 3.4. Summary as stated in basic matching. ww 1i : the word weight of the ith word in A 1 , and ww 2i : the word weight of the ith word in A 2 . Both of them could be calculated by using the methods tf * idf .

Summary

This chapter introduces the methods for calculating semantic similarity. Firstly, a literature review about existing methods is presented, and then the applications of semantic similarity in the domain of inventive design are given. Finally, two proposed methods are explained in detail. In Chapter 4, the basic method (Section 3.3.2) will be used to match the parameters in the process of using inventive principles, and in chapter 5, the improved method (Section 3.3.3) will be used to define the missing links among the TRIZ knowledge sources.

Chapter 4 Automatic Match between Specific Parameters and Generic Engineering Parameters

While TRIZ is increasingly developing both in research and education, new users always encounter difficulties in their first attempts to practice it. In such situation, Altshuller's original matrix (Section 2.1.5.1) often appears as an "easy-to-begin-with" tool. While not being representative of what TRIZ really is, it continues to seduce new users, teachers and trainers.

Several approaches to automate the use of the contradiction matrix have been proposed in literature, and research on the automatic match between specific parameters of the artefact being considered and Altshuller's generic engineering parameters remains a topic of interest. As a result, the matching of specific parameters and generic engineering parameters (Section 2.1.5.1) is chosen as the first step of the automatic ontology-based mechanism.

In this chapter, firstly, the existing problems of using the contradiction matrix are given, and the inventive principles ontology, which formalizes the process of using it, is presented. Then the proposed methods of matching specific parameters and generic engineering parameters based on semantic similarity and case-based reasoning are introduced in detail. Finally, the summary and conclusion about this chapter are given.

The Problem

The Problem

The first TRIZ problem solving technique was a collection of inventive principles aimed at eliminating technical contradictions. To make the inventive principles applicable in a systematic way, the creator of TRIZ, Altshuller formulated thirty-nine generic engineering parameters (Appendix A), such as "the weight of a moving object" or "speed" which are generalizations of specific technical parameters [1].

As shown in Figure 4.1, TRIZ provides a tool called contradiction matrix (Appendix B), which presents all parameters in the form of a matrix. It consists in thirty-nine generic engineering parameters on the first line and column of it. Each cell of the matrix shows the inventive principles that can be used to solve that particular contradiction. Along the vertical axis of this matrix, the generic engineering parameters that have to be improved are specified. Along the horizontal axis, the parameters that deteriorate because of the improvement are specified. These parameters can be looked up along the vertical and horizontal axes and the matrix suggests up to four sorted principles that can be used to solve the contradiction.

In real-world problems, most of the times, contradictions are established in terms of parameters that are inherent to the artefact that is being developed, and there is a semantic gap to fill between those parameters and the generalized ones. An abstraction effort needs to be provided to choose the best generic engineering parameter, and in this way, be able to use the contradiction matrix.

Automatic Match between Specific Parameters and Generic Engineering Parameters 67

Example: In the framework of an inventive design project proposed to a team of engineering students at INSA de Strasbourg, there was the study of the improvement of a barbecue grill. The students have retained the following contradiction to solve: if the number of parts in the wire mesh is high, the mastery of the beef doneness is satisfying but the weight of the grill is unsatisfying. On the other hand, if the number of parts is low, the mastery of the beef doneness is unsatisfying but the weight is satisfying. So there are two parameters: SP 1, the weight; SP 2, the mastery of the beef doneness.

SP 1 is directly associated with the 2nd generic engineering parameter "weight of a stationary object"; but for SP 2, the association with the 35th generic engineering parameter "adaptability or versatility" is not intuitive.

As explained above, the generic engineering parameters in the contradiction matrix are abstract and built independently of specific applications. In real applications, it is difficult for users to match specific parameters to the thirty-nine generic engineering parameters because it requires an extensive knowledge of different engineering domains.

The Inventive Principles Ontology

In order to facilitate the process of using the contradiction matrix and the inventive principles, the inventive principles ontology is built to formalize the concepts and their relationships.

The framework of the Inventive Principles Ontology is shown in Figure 4.2. Feature is defined to represent the parent concept of PrimaryFeature (or Generic Engineering Parameter) in the contradiction matrix, such as "power", and AppliedFeature (or Specific Parameter) in the specific application, such as "electrical energy".

The contradiction matrix consists of thirty-nine P rimaryF eatures on the vertical axis and horizontal axis and 39 * 39 Item, which shows all the potential InventiveP rinciples used to solve a particular contradiction. Every two P rimaryF eatures correspond to an Item: one acts as positive F eature, the other as negative F eature. Each Item can have i InventiveP rinciples

(i = 0 • • • n) while each InventivePrinciple can have j SubInventivePrinciples (j = 1 • • • m).
Each SubInventiveP rinciple also refers to two concepts: PrimarySubIP (1:1) -the initial description of the SubInventivePrinciple, such as "IP38 (b) -replace enriched air with pure oxygen"; and AppliedSubIP (1 : application, such as "Treat wounds in a high pressure oxygen environment to kill anaerobic bacteria and aid healing".

i, i = 0 • • • n) -the
The interpretations from AppliedF eature to P rimaryF eature, and from Primary-SubIP to AppliedSubIP need to be implemented manually; and this fact requires a large amount of TRIZ experience and knowledge covering a wide spectrum of domains. As a result, this research focuses on automating, as much as possible, this process in order to provide assistance to TRIZ users [2,3].

Table 4.1 shows the definition of several classes and properties in the inventive standards ontology. subClassOf is the relation existing between super-classes and sub-classes, for example, PrimaryFeature and AppliedFeature are subclasses of Feature. Moreover, the properties can also be encoded in OWL syntax, in which the owl:DatatypeProperty means that the range of the property is data, such as, the range of description is string, and the owl:ObjectProperty means that the range is object, for example, hasPositiveAppliedFeature has the domain of Problem and the range of AppliedFeature.

Automatic Match between Specific Parameters and Generic Engineering Parameters 69

Table 4.1: The OWL encoding classes and properties in the inventive principles ontology.

Classes

Properties <owl:Class rdf:ID="Feature"> <rdfs:subClassOf> <owl:Class rdf:ID="owl:Thing"> </rdfs:subClassOf> </owl:Class> <owl:DatatypeProperty rdf:ID="description"> <rdfs:domain rdf:resource="#Feature"/> <rdfs:range rdf:resource= "http://www.w3.org/2001/XMLSchema #string"/> </owl:DatatypeProperty> <owl:Class rdf:ID="PrimaryFeature"> <rdfs:subClassOf> <owl:Class rdf:ID="Feature"/> </rdfs:subClassOf> </owl:Class> <owl:ObjectProperty rdf:ID="hasPositiveAppliedFeature"> <rdfs:domain rdf:resource="#Problem"/> <rdfs:range rdf:resource= "#AppliedFeature"/> </owl:ObjectProperty> <owl:Class rdf:ID="AppliedFeature"> <rdfs:subClassOf rdf:resource="#Feature"/> <owl:disjointWith> <owl:Class rdf:ID="PrimaryFeature"/> </owl:disjointWith> </owl:Class>

The Basic Method based on Semantic Similarity

A method is explored to calculate the semantic distance between short texts and use it to fill the semantic gap between specific parameters and generic engineering parameters, and in this way, facilitate the process of using the contradiction matrix. The matching process is described in Section 3.3.2, and so in this section, only the preprocess and experiment for matching specific parameters and generic engineering parameters are considered.

Preprocess

During the matching between specific parameters and generic engineering parameters, the preprocess mainly includes two steps:

• Subjective Words Deletion: The specific parameters are usually obtained from the specific application, and so compared with abstract generic engineering parameters, there exist many useless subjective words (often noun, and are used to represent specific objects), such as the specific object "pipe" in the specific parameter "change the size of pipe". In order to match to the generic engineering parameters, these subjective words need to be eliminated.

• Semantic Extension: After the first step, the parameters to be matched generally consist of two or several words, based on which it is difficult to identify the groups of short texts with different semantic meaning. Consequently, it is necessary to extend these words with their similar words based on WordNet. For example, for the words "change size", the word "height" can be added according to the requirement of the specific application.

The Matching Process

The matching between specific parameters and generic engineering parameters can be divided into five phases:

1. Short Text Segmentation.

2. Sense Search.

3. Sense Similarity.

4. Word Similarity.

Short Text Similarity.

The detailed information for each phase is described in Section 3.3.2.

Experiments

To test the accuracy of the proposed semantic similarity measure between two short texts, the projects that were solved by engineering students at INSA de Strasbourg are used.

In this framework, one hundred and fifty-six specific parameters of these projects and the thirty-nine Altshuller's generic engineering parameters have been matched.

Automatic Match between Specific Parameters and Generic Engineering Parameters 71

In the following, result1 represents the set of matches done by the students and result2, the set of results coming from the automatic semantic similarity calculation. For example, in the project about the improvement of a "pan", the students manually matched the specific parameter "several different foods to cook" to the generic engineering parameters "26. Quantity of substance/matter" and "35. Adaptability or versatility". With this method, this specific parameter is matched with "23. Loss of substance" and "26. Quantity of substance/matter" automatically.

The results have been identified as six different situations:

a. result1 = result2: result1 equals to result2 exactly.

b. result1 ⊂ result2: result2 contains result1.

c. result1 ∩ result2 = ∅: There is an intersection between result1 and result2.

d. result1 ⊃ result2: result1 contains result2.

e. result1 ∩ result2 = ∅: There is no intersection between result1 and result2.

f. result2 = ∅: result2 is empty.

The ratio of each kind of comparison results to the total number in our experiments is shown in Table 8.1.

Table 4.2:

The ratio of each kind of comparison result to the total number.

Type of comparison Ration

a. result1 = result2 19% b. result1 ⊂ result2 21% c. result1 ∩ result2 = ∅ 3% d. result1 ⊃ result2 4% e. result1 ∩ result2 = ∅ 45% f. result2 = ∅ 8%
Intuitively, the set a) should be as big as possible, while the sets e) and f) being as small as possible. Results of sets b), c) and f) might be acceptable to some extent, whose ratios might be decreased to improve the ratio of set a).

The results in Table 8.1 are not concluding: set a) represents only 19% of the whole, while sets e) and f) represent 45% and 8% respectively. It means that for 53% projects, 4.3. The Basic Method based on Semantic Similarity the results of the automatic approach do not correspond to the matches done by the students. Only for 19% projects, all the exact matches are obtained. For the rest, more or less correct solutions are obtained.

The analysis of this situation leads to three reasons:

• The inaccurate description of the projects: In these experiments, it is assumed that all elements in result1 are correct; that is, that the students have done the matching correctly, in the same way as a real TRIZ expert would have done. In fact, there are differences among the views of different people about the same project, and not all the students describe the projects accurately. As a result, there are some obvious mistakes in the matches retained by the students. This is also the main reason why the size of set e) is so big.

• The restriction of the dictionary: WordNet is used as our dictionary. Even though compared with other dictionaries, WordNet has the advantage of the size of its terms and the variety of its semantic relationships, it only depicts concepts and their relationships in general language, to be opposed to technical or specialized language. For example, the general concepts "water", "liquid" and their hyponyms and hypernyms can be found in WordNet while a specialized notion, such as the "Reynold's number", does not appear in the dictionary. That is the reason for the appearance of the set of type f).

• Problems with our method: The method for calculating semantic similarity between short texts is explored. This calculation is done without considering contexts. By "context", it means the area or field of the project. For instance, in the mechanical case, the specific parameter "stress" should be matched with "10. Force (Intensity)", while in other areas, the same parameter "stress" is usually matched with "11. Stress or pressure". Without considering contexts, our method does not have a good performance.

To cope with these disadvantages, an improvement of this method is proposed in next section.

Automatic Match between Specific Parameters and Generic Engineering Parameters 73

The Improved Method: Semantic Similarity +

Case-based Reasoning

Case-based reasoning (CBR) [4] is a well-known methodology in Artificial Intelligence (AI). The main idea of CBR is to adapt solutions that were used to solve old problems and use them for solving new problems or cases, that is, people reuse past problem-solving experiences to deal with a new case.

When CBR is used to solve a new problem, firstly the new problem is standardized according to the case representation. Then similar cases are retrieved from the case library, and the solution of the most similar problem is suggested for the new problem. If necessary, this suggested solution is revised according to previous experience, domain knowledge and the actual situation of the new problem. After obtaining the confirmed solution, the new case, composed of the new problem and its confirmed solution, is stored into the case library.

The cycle of CBR comprises five activities:

• Characterize the new problem and assign an index.

• Retrieve the most similar case(s) to the new problem from the case library.

• Reuse the case(s) to attempt to solve the new problem.

• Revise or adapt the suggested solution to satisfy the new problem if necessary.

• Retain the new problem and its confirmed solution as a new case.

The cyclic process of CBR is depicted in Figure 4.3.

According to the above five activities, a specific case-based reasoning framework is constructed to improve the method presented in Section 4.3. Specifically, on the one hand, the semantic similarity between short texts is calculated to return the matched generic engineering parameters. On the other hand, the solution to the most similar previous problem is retrieved and returned to users as a suggested solution based on casebased reasoning simultaneously. The whole flowchart of the improved method is described in Figure 4

Case Representation

Case representation is an important aspect in designing efficient CBR systems. In this application, a case is represented with the following three attributes:

• The context number.

• The match from the specific parameter (that is, one of the parameters of the artefact involved in the contradictions) to a generic engineering parameter (these matches are chosen and reused as the previous experience because they have been used under the same specific context), as shown in Figure 4.5.

In order to make a comprehensive classification of contexts, the International Patent Classification (IPC) 1 , established by the Strasbourg Agreement 1971, that provides a hierarchical system of language independent symbols for the classification of patents and utility models according to the different areas of technology to which they pertain, is used. All the contexts are divided into eight groups: Each group is composed of several classes, and these classes are made up of numerous subclasses, organized in a hierarchy of four levels. In this method, the first three levels are chosen as the classifications of contexts. For example, there is, "H. Electricity", including all the materials and phenomena concerning electricity, and its corresponding Subclasses, such as "H01. Basic Electric Elements" and this subclass also has several Nested-Subclasses, for example, 'H01C. Resistors'.

When the user builds the representation of a case, he needs to choose a context index number for it, but sometimes, he may not find an exact match. If it is the case, the user should look for terms of approximately the same meaning, for terms of either broader or narrower scope, or for terms that represent a different approach to the subject; i.e., the essential function or effect of the device or the use or application to which the device is put.

All past cases are represented according to the above framework, and then index them by the unique identifier (the context number) to facilitate the retrieval.

It is assumed that a specific parameter with similar context can correspond to several generic engineering parameters, that is, for a new problem, several different suggested results might be obtained based on case-based reasoning.

Case Retrieval

It includes the retrieval of past similar cases and the selection of the best case. Firstly, a context number is chosen for the new case. Then we query the case library by comparing the specific parameter of the new case and the previous case. For cases whose specific parameter is the same as the new case, we next compare their context numbers, and decide whether they have a similar context. Finally, the solutions of cases with the similar context, that is their matches between the specific parameter and the generic engineering parameter, are returned to users as suggested results.

The process of searching the most similar context plays an important role in the improved method. Firstly, we construct a group-class-subclass tree to represent the classification of contexts with different granularities, larger for groups and smaller for subclasses. For a given case, we choose the index of the subclass -the minimal granularity to represent it. Then we apply a bottom-up approach of the retrieval of the group-class- subclass tree, as shown in Figure 4.61 and search for the similar context according to the following steps:

1. Retrieve cases belonging to the same subclass, if they exist, go to step 4, else go to step 2.

2. Use Depth First Search (DFS) [5] to retrieve the cases belonging to the subclasses who have the same parent -classes with the given index of the subclass of the context, if they exist, go to step 4, else go to step 3.

3. Use DFS to retrieve cases belonging to the classes who have the same parent -group with the given index of the subclass of the context, if they exist, go to step 4, else return "no case with similar context".

4. Return the retrieved cases.

According to the above four steps, we retrieve at least the first minimal level of the group-class-subclass tree. Generally, this program will be finished as soon as the first similar case is found because the bottom-up retrieval along the group-class-subclass tree ensures that the first obtained case is the most similar case.

The improved Method: Semantic Similarity + Case-based Reasoning

Let us consider a new case "NC" in Figure 4.6. If the user chooses "Subclass 3" as the context, step 1 of the algorithm just returns "Case 3" and "Case 4" as the most similar cases. On the contrary, if the user chooses "Subclass 4" as the context, steps 1 and 2 do not find any similar cases; our bottom-up search does not return any similar case. Another situation would be if the user chooses "Subclass 7" as the context. In this situation, we need to generalize to the upper levels ("Class 6" and "Group 4") to retrieve "Case 6" as the most similar one to the NC.

We take the matching of the specific parameter "size" in the new case "instantaneous water heater" as an example. Suppose that the context of this case is "Subclass 7", and according to what is stated above, we can obtain a similar case "Case 6" with the matching between "size" and "volume of moving object" because it belongs to "Group 4" as the new case does. The matching between "size" and "volume of stationary object" in "Case 5" cannot be returned because it has a different parent even in the group-level.

Case Adaptation and Verification

The goal of this step is to analyze items requesting adaptation and implement adaptation. The obtained similar matching between specific parameters and generic engineering parameters is considered as suggested result, which will be returned to the TRIZ user together with the semantic matching results.

The goal of this part is to retain the new case. As the new problem has a new solution, it can be described according to the case representation and added into the case library. In addition, with the rapid increase of the number of cases, the case with repeated matches under a similar context should be deleted because it may result in redundant cases. The purpose is to make sure that the size of the case library would not increase continuously to affect the retrieval speed and make each problem more correct with higher accuracy.

Case Library

Each case keeps track, in fact, of a "translation" such as specific parameter -> generic engineering parameter, valid in a certain context. We would like to have this context as general as possible. In other words, the idea is to find the more general context where the two short texts (that represent the specific and generic engineering parameters) have the same sense. This is why we launch periodically this maintenance task of the case library. If all the leaves of the sub-tree of contexts have cases with the same translation associated to them, we can assume that those translations are valid in the more general common context.

For example, let us consider the following leaves in the taxonomy of Figure 4.6; "Subclass 1", "Subclass 2", "Subclass 3". These three contexts have cases associated with them. Let us suppose that "Case 1", "Case 2" and "Case 3" have the same translation "SP1 -> GEP1". In this case, we can propose the user to validate these three translations in the context "Group 1" (the more general common context for the leaves we have considered).

Evaluation

As stated above, the performance of the improved method greatly depends on the experience and results of the previous cases, that is, a large number of cases, in which the inventive problems are solved by using the contradiction matrix and inventive principles, are necessary.

Summary

Consequently, in order to construct the case library, the prototype of our methods (shown in Chapter 7), will be firstly integrated into the software developed by the laboratory LGECO (that will be discussed in Section 7.1). On one hand, this software will provide valuable assistance to the users all over the world trained in the methodology and working on a design project in different domains, and on the other hand, this user experience makes it possible to accumulate a large amount of users' cases for our case library. As shown in Figure 4.7, with the increasing number of users' cases and feedback, the performance of our methods will be improved significantly.

Summary

In this chapter, in order to facilitate the use of Altshuller's contradiction matrix in an inventive design study, methods of matching specific parameters and generic engineering parameter based on semantic similarity and case-based reasoning are introduced in detail. The inventive principles ontology is firstly built to formalize the process of using the contradiction matrix, and then according to the characteristics of our specific application (filling the semantic gap between the specific parameters and the generic engineering parameters), the method of calculating semantic similarity presented in Section 3.3.2 is used.

In order to improve the performance of this method, the results from previous projects are returned to users as suggested solutions according to a case-based reasoning approach. As we can observe from our first attempt to test the developed methodology, some relevant potential associations between a specific term and one or several of the thirty-nine generic engineering parameters are proposed to users. The improved method also benefits from previous experiences and therefore offers a constant improvement due to CBR techniques.

However, the resolution of inventive problems only with forty inventive principles can not always provide comprehensive and efficient recommendations, and it is necessary to apply other TRIZ knowledge sources, such as seventy-six inventive standards. Therefore, next chapter introduces a method of searching heuristic abstract solution from different knowledge sources based on semantic similarity and ontology reasoning.

Chapter 5

Ontology-based Search for Heuristic Abstract Solutions

During the development of TRIZ, different knowledge sources were established in order to solve different types of inventive problems, such as the forty inventive principles for eliminating the technical contradictions. These knowledge sources, that have different levels of abstraction, are all built independently of the specific application field, making their use difficult, as an extensive knowledge about different engineering domains is required.

In order to facilitate the use of the TRIZ knowledge sources, this chapter presents a new inventive problem solving approach based on ontologies. In this approach, the TRIZ users start solving an inventive problem with the TRIZ knowledge source of their choice to obtain an abstract solution. According to the selected items of that first knowledge source, the similar items of other knowledge sources are obtained based on the semantic similarity calculated in advance. Considering that all the TRIZ knowledge sources are described as short-texts, the missing links among the TRIZ knowledge sources are defined based on short-text semantic similarity. At the same time, an ontology reasoning mechanism deployed on Protégé and Jess, is used to provide heuristic solutions dynamically for TRIZ users.

The Problem: Missing Links among the TRIZ Knowledge Sources

The Problem: Missing Links among the TRIZ

Knowledge Sources

In order to present objects, ideas, situations and relationships in the inventive problem solving process, as stated in [1], the TRIZ experts build models for specific applications from their own point of view. The knowledge sources for resolution are situated at different levels of abstraction and at different levels of "closeness to reality". In fact, inventive principles, for example, even if they seem to refer to concrete reality (for instance, "inert atmosphere") are conceptually more abstract than inventive standards, which refer to concrete substances or fields. The imagination effort by the user to apply an inventive principle is much more important than the one to be done if he wants to use an inventive standard.

More than that, even in the same knowledge source the description may be different or incompatible, as stated in [2]. For example, even though both Inventive Standard 1.1.5 -"Transition to SFM by using external environment with additives" and Inventive Standard 1.1.3 -"Transition to external complex SFM" indicate that the external substances or fields can be introduced to improve the existing model, their descriptions are different.

In order to formalize the set of knowledge bases of TRIZ and make it coherent and complete, [3] analyzes the similarity among inventive principles based on the text descriptions of examples using them. Six out of forty principles are defined as "Obscure Principles", which are hard to be analyzed by automatic classification systems. In addition, two kinds of similarities between principles are defined in this paper: text similarity and meaning similarity.

In this research, the approaches of measuring short-text semantic similarity are used to compare, analyze, and match the items in the different knowledge sources ontologies. Figure 5.1 shows an example of correspondences of items in the knowledge sources presented above. The higher the value of similarity, the more similar the two items. For example, Inventive Standard 5.3.1 -"Changing of phase state" is more similar to Inventive Standard 1.1.5 -"Transition to SFM by using external environment with additives" (Sim: 0.75) than Inventive Principle 39 -"Inert atmosphere" (Sim: 0.15).

The Framework

As shown in Figure 5.2, the proposed method is made up of a preprocess and a main process.

The preprocess includes two steps:

• Step p1: Use semantic relatedness methods to find the missing links among the TRIZ knowledge sources, and store them.

• Step p2: Establish the TRIZ knowledge sources ontologies and design ontology reasoning rules. Ontologies are used to formalize the main concepts in the TRIZ knowledge sources, and Semantic Web Rule Language (SWRL) [4] is used to describe the reasoning rules.

After the preprocess, the main process consists of three steps:

• Step 1: Select items from one of the TRIZ knowledge sources for an abstract solution, for example, using inventive principles to eliminate a technical contradiction.

• Step 2: For the selected items in Step 1, search the similar items of other knowledge sources on the basis of the similarities stored in the database. • Step 3: According to the TRIZ knowledge sources ontologies, choose the appropriate reasoning rules from the rule set, and implement ontology reasoning to provide the heuristic abstract solutions.

Semantic Relatedness Methods for Defining the

Missing Links among the TRIZ Knowledge Sources

In this section, the process of using the method proposed in Section 3.3.3 to define the missing links among the TRIZ knowledge sources is introduced. Taking into account that the detailed matching process has been described in Section 3.3.3, only the preprocess and the obtained semantic similarity among the TRIZ knowledge sources are considered.

Preprocess

Before the semantic matching, data preprocess is carried out to reduce redundancy and minimize imprecision. Different kinds of knowledge sources need different preprocesses:

Inventive Principles: Compared with the distinct principles, additional information, such as the similar words, need to be added to the obscure principles to facilitate their matches with inventive standards.

Inventive Standards: As analyzed in Section 2.1.5.2, the inventive standards of Cclass and D-class are identified as new inventive principles and patterns of evolution respectively, so only the seventy-one inventive standards of A-class and B-class (Appendix D.2) are matched with the given inventive principles according to the semantic similarity methods proposed in Section 3.3.3, and correspondingly, in Section 5.4, the reasoning rules only about these inventive standards are built.

Separation Methods: The eleven separation methods, separation in space, time, upon condition, and between parts and the whole, are described abstractly, and the output of using these methods might not yield to a design concept or physical function. For example, Separation Method 1 : "Separation of conflicting properties in space" suggests the users to separate the conflicting properties in space dimension, but which properties to be divided are not indicated. As a result, the complementary information about using these methods should be added to enrich the separation methods, such as the users' cases.

The Matching Process

Taking into account the characteristics of the TRIZ knowledge sources to be matched, such as, high appearance of words with similar meaning, the improved method with word weight is used to calculate the semantic similarity among the TRIZ knowledge sources. The detailed process is described in Section 3.3.3.

The Semantic Similarity Among the TRIZ Knowledge

Sources

Considering that forty inventive principles are the most-used TRIZ knowledge source, the match starts with inventive principles and the semantic similarity is divided into two kinds: the similarity between inventive principles and inventive standards, and the similarity between inventive principles and separation methods.

Semantic Similarity Between Inventive Principles and Inventive Standards

There are three possibilities for the obtained results, that is, A-class, B-class and A and B-class. For a given inventive principle, we firstly determine if it corresponds to the inventive standards 5.3. Semantic Relatedness Methods for Defining the Missing Links among the TRIZ Knowledge Sources In addition, some of the obtained five A-class similar inventive standards may belong to B-class because the semantic similarities are calculated between the forty inventive principles and all the inventive standards of A-class and B-class.

For example, Table 5. • InventiveStandard 59: 5.1.2. Dividing of product.

• InventiveStandard 29: 2.4.7. Using physical effects.

• InventiveStandard 64: 5.3.2. "Dual" phase state of substance (Phase transition 2).

• InventiveStandard 65: 5.3.3. Using phenomena accompanying a phase transition (Phase transition 3).

• InventiveStandard 66: 5.3.4. Transition to dual-phase state (Phase transition 4).

• InventiveStandard 67: 5.3.5. Using interaction between phases of the system.

• InventiveStandard 36: 4.1.2. Using of copies.

• InventiveStandard 44: 4.3.3. Using resonance oscillation of attached object.

• InventiveStandard 41: 4.2.4. Transition to measurement SFM by using external environment properties.

• InventiveStandard 62: 5.2.1. Using present fields (pluralistically).

Semantic Similarity Between Inventive Principles and Separation Methods Analogously, two most similar separation methods are obtained for each inventive principle. For example, InventiveP rinciple 2 is matched with:

• SeparationM ethod 7: Substitution of the phase state of a system's part or external environment.

• SeparationM ethod 1: Separation of conflicting properties in space.

As stated in Section 5.3.1, inventive principles and separation methods are established in different levels of abstraction, and the accuracy of the obtained similarity depends partly on the amount of the information provided by the extensive users' cases. Consequently, only the matches among inventive principles and inventive standards are considered in the prototype presented in Chapter 7.

The semantic similarity measures among the TRIZ knowledge sources are given in Appendix F.

Ontology Reasoning for Searching Heuristic Abstract Solutions

According the semantic similarity among the TRIZ knowledge sources, when the user works with a knowledge source, the similar items of other knowledge sources can be obtained. Furthermore, from these similar items, the heuristic abstract solutions, which provide the complementary information for solving the problem, can be generated based on ontology inference. Considering that inventive standards, the most difficult TRIZ knowledge source, can provide most efficient recommendations, the process of using inventive standards to generate heuristic abstract solutions based on ontology inference is considered in this research. This section describes this process in detail. Firstly, the TRIZ knowledge sources ontologies are presented, including the inventive principles ontology (Section 4.2) and the inventive standards ontology. Then reasoning rules are set to describe the process of searching heuristic abstract solutions. Finally, the Jess rule engine [5] is used to execute the ontology inference.

The Inventive Standards Ontology

As shown in Section 1.1, the specific process of using Su-Field models to solve an inventive problem includes: building a Problem Model, mapping to a Generic Problem Model, finding a Generic Solution Model based on the corresponding inventive standard, and finally establishing and instantiating a Solution Model.

As shown in Figure 5.3, Problem is used to represent the concept of Problem Model. For a specific case, its Problem corresponds to a Generic_Problem_Model and a Generic_Solution_Model through the two properties correspondsTo_GPM and corre-spondsTo_GSM respectively. Generic_Problem_Model has one or two Substances and one Field, depicted by hasSubstance1_GPM, hasSubstance2_GPM and hasField_GPM, while Generic_Solution_Model uses hasSubstance1_GSM, hasSubstance2_GSM and hasField_GSM to describe these relations.

The transformation from Generic_Problem_Model to Generic_Solution_Model is implemented through the use of the appropriate InventiveStandard, which is described by chooses_IS. In order to detect the change of states before and after using InventiveStandard, three properties has_S1_Num_GPM, has_S2_Num_GPM and has_F_Num_GPM are defined to obtain the number of Substances and Field for Generic_Problem_Model, while three properties has_S1_Num_GSM, has_S2_Num_GSM and has_F_Num_GSM for Classes and Properties <owl:Class rdf:ID="Element"> <rdfs:subClassOf> <owl:Class rdf:ID="owl:Thing"> </rdfs:subClassOf> </owl:Class> <owl:Class rdf:ID="Substance"> <rdfs:subClassOf> <owl:Class rdf:ID="Element"/> </rdfs:subClassOf> </owl:Class> <owl:Class rdf:ID="Field"> <rdfs:subClassOf rdf:resource="#Element"/> <owl:disjointWith> <owl:Class rdf:ID="Substance"/> </owl:disjointWith> </owl:Class> <owl:DatatypeProperty rdf:ID="description"> <rdfs:domain rdf:resource="#Element"/> <rdfs:range rdf:resource="http://www.w3.org/2001/XMLSchema#string"/> </owl:DatatypeProperty> <owl:ObjectProperty rdf:ID="includes"> <rdfs:domain rdf:resource="#Element"/> <rdfs:range rdf:resource="#Element"/> </owl:ObjectProperty> are used to mark the type of the chosen inventive standard and the physical effects to be chosen respectively.

The property changesOnOrFor_Element is used to record the concrete object, including Substance and Field, chosen by the user in the specific case.

Ontology Reasoning Rules

As stated in Section 5.3, only five inventive standards of A-class and B-class are obtained as the final similar results, based on which the heuristic abstract solutions will be generated in Section 5.4.3.

Correspondingly, the reasoning rules are built according to these two classes:

• The rules for A-class inventive standards: The forty inventive standards are condensed and generalized into seven generalized standard solutions, and each kind of standards have similar modifications or evolutions on the Su-Field model. Consequently, it is possible to use the reasoning rules for seven generalized standard solutions to represent the modification of all the forty inventive standards.

• The rules for B-class inventive standards: The thirty-one inventive standards are identified as the implementation of existing inventive principles, and each inventive standard yields to a distinctive modification. As a result, for each inventive standard, the reasoning rules to describe its modification are set.

According to the analysis of these two kinds of inventive standards, the number of substances and fields can be used to identify the modification of inventive standards. So the properties has_S1_Num_GPM, has_S2_Num_GPM, has_F_Num_GPM are used to indicate the number of substances and field before the modification while has_S1_Num_GSM, has_S2_Num_GSM and has_F_Num_GSM are used to indicate the number of substances and field after the modification. The regular patterns about the number of substance and field are described as following:

• For Substance 1 :

-has_S1_Num_GPM =0: * has_S1_Num_GSM =1: The additive Substance 1 is used instead of the Substance 1 before the modification. * has_S1_Num_GSM =1: The additive Substance 1 is added to the Substance 1 before the modification. • For Substance 2 :

-has_S2_Num_GPM =0: * has_S2_Num_GSM =1: The additive Substance 2 is used instead of the Substance 2 before the modification. * has_S2_Num_GSM =1: The additive Substance 2 is added to the Substance 2 before the modification.

-has_S2_Num_GPM =0: * The additive Substance 2 is added to complete the incomplete problem model.

• For Field:

-has_F_Num_GPM =0: * has_F_Num_GSM =1: The additive Field is used instead of the Field before the modification. * has_F_Num_GSM =1: The additive Field is added to the Field before the modification.

-has_F_Num_GPM =0: * The additive Field is added to complete the incomplete problem model.

Taking General Solution 1 -"Complete an incomplete Su-Field model"as an example, its reasoning rules need to describe the corresponding modification, that is, firstly the missing components are detected, and then according to the missing component, several additive elements, substances or filed, are added into the Su-Field model after the ontology inference.

As shown in Table 5.3, there are three SWRL rules for General Solution 1. Rule 1 and Rule 2 describe the modification when the substances are missing (has_S1_Num_GPM =0 or has_S2_Num_GPM =0), based on which an additive substance is added to the Su-Field model after the ontology inference. Similarly, Rule 3 expresses the modification when the field is missing (has_F_Num_GPM =0), based on which an additive field is added to the Su-Field model after the ontology reasoning.

In the process of searching heuristic abstract solutions, there are forty SWRL rules (Appendix G) in total, including nine rules for the inventive standards of A-class and thirty-one rules for the standards of B-class.

Implementing the Search of Heuristic Abstract Solutions based on the Rule Engine

The Function of OWL -> Jess

During the process of OWL → Jess, the OWL knowledge is transferred to the Jess rule engine.

The classes in OWL knowledge base are mapped onto the Jess templates that are used to define the classes and the hierarchy of classes, similar to object-oriented way. For example, in the TRIZ knowledge sources ontologies, the classes Element, Substance and Field are transformed into Jess classes, as shown in Table 5.4. The deftemplate construct in Jess is employed to define the type of slots in a fact. The extends construct indicates the inheritance relationship between two templates defining facts. OWL:Thing ia a most top level class in OWL from which all other classes are directly or indirectly inherited.

In order to perform inference by means of Jess, these facts in TRIZ knowledge sources ontologies should be transformed into Jess facts. For example, as shown in Table 5.4, the fact that Water and Liquid are two instances of the class Substance, is transformed into the Jess facts by using the assert construct. Meanwhile, it is also necessary to transform the relationships facts between instances or between instances and data values into Jess facts. For example, two relations, that is, Water (an instance of Substance) has the description of "H 2 O" and Liquid (an instance of Substance) includes Water, are transformed into the Jess facts as shown in Table 5.4.

The Function of SWRL -> Jess

In this phase, the SWRL-based reasoning rules are transformed to Jess rules. The transformation is relatively straightforward. For example, the rules in Table 5.3 is transformed to Jess rules, as depicted in Table 5.5. (assert(has_S1_N um_GSM ?a 1)) (assertOW LP roperty has_S1_N um_GSM ?a 1) (assert(has_S2_N um_GSM ?a 1)) (assertOW LP roperty has_S2_N um_GSM ?a 1) (assert(has_F _N um_GSM ?a 1)) (assertOW LP roperty has_F _N um_GSM ?a 1)) Importing Jess files including Jess facts and Jess rules, the process of searching 100 5.5. Summary heuristic abstract solutions are implemented by the inference engine. The engine applies the rules to facts in the fact base, and activates the rules whose premises are satisfied, fires the activated rules and executes the actions specified in the head of these activated rules. The process is terminated until no rules are in the rule base, and then the heuristic abstract solutions are obtained. Figure 5.4 depicts the inference results for an incomplete problem model without Field, in which a heuristic abstract solution-"an additive Field is added" is obtained. The detailed process of using this method to solve a specific problem is depicted in Section 7.4.3.

The Execution of the Jess Inference Engine

Summary

This chapter introduces a method for searching heuristic abstract solutions to inventive problems based on semantic similarity and ontology inference.

Considering that all the TRIZ knowledge sources are described as short texts, the missing links among the TRIZ knowledge sources are defined based on short-text semantic similarity. Firstly, TRIZ users start solving inventive problems with the TRIZ knowledge source of their choice to obtain an abstract solution. According to the selected items of that first knowledge source, the similar items of other knowledge sources are obtained based on the semantic similarity calculated in advance. Then, with the help of these similar items, the ontology reasoning mechanism deployed on Protégé and Jess, is used to provide heuristic solutions dynamically for TRIZ users.

However, there is still a problem, that is, the interpretation of Solution Model in real life, which is the last step in Su-Field analysis. As stated in this chapter, an abstract solution is obtained finally according to this approach. Then, in order to obtain the concept solution, the interpretation of the abstract solution is generally implemented manually with the help of the physical effects knowledge base, which link generic technical functions with specific applications and systems. The physical effects compatible with the context of the specific problem should be chosen to assist the users to instantiate the Solution Model. So in order to facilitate this process, an ontology-based approach of using physical effects is proposed in next chapter.

Chapter 6

Ontology-based Approach for Using the Physical-Chemical-Geometrical Effects

Su-Field analysis, one of the inventive problem solving tools, can be used to analyze and improve the efficiency of a technical system. As one of the most important phases of Su-Field analysis, the last step is normally implemented manually with the help of the physical effects knowledge base, which links generic technical functions with specific applications and systems. The physical effects compatible with the context of the specific problem should be chosen to assist the users to instantiate the Solution Model. However, the physical effects and the specific problems are built at different levels of abstraction, and it is difficult for the users to choose one. In fact, given a certain function, too many physical effects are eligible while with the detailed context of the problem, no physical effect could be returned.

In order to facilitate the use of physical effects, this chapter firstly proposes a new way of representing physical effects by making explicit a state change, that is, the couple of two states before and after applying them. Then, knowledge about using physical effects is formalized based on ontologies. Finally, the reasoning process of using physical effects is performed with the support of the inference engine.

The Problem

In the survey of "Worldwide status of TRIZ perceptions and uses" implemented by Cavallucci in 2009 [1], two frequencies were obtained, that is, the frequency of TRIZ's main components (most unknown and most often used), as shown in Figure 6.1. According to these figures, we can observe that the pointers and the database of physical effects ranks highly in the list of the most unknown TRIZ components, and ranks lowly in the list of the most often used components. Compared with other TRIZ tools, most users do not know the pointers and effects and only use the pointers and effects occasionally when they deem it necessary.

There are many reasons for this situation, such as, the large number of physical effects and the description of the pointers at high level of abstraction. With the traditional method, the search of physical effects is normally implemented manually with the help of the pointers to physical effects -consists in linking a generic technical functions with specific applications and systems, the pointers to physical effects that are compatible with the context of the specific problem should be chosen to complete the Su-Field model and assist the users to interpret the Solution Model in the real world. However, the pointers to physical effects and the specific problems are built at different levels of abstraction. It is therefore difficult for users to choose among too many eligible pointers to physical effects given a certain function while with a detailed context of the problem, it is possible that no pointer to a physical effect be returned [2].

Accordingly, there is a need for a new manner of modeling physical effects and a method that does not rely on the pre-stored pointers and that can process the user's retrieval more dynamically. In this chapter, a new way of representing physical effects, that is, as a couple of two states before and after applying them is proposed. Then, based on ontology, the knowledge of using physical effects is formalized in OWL and the rules for retrieving physical effects are interpreted and represented in SWRL. After mapping the knowledge and constraints of using physical effects onto Jess facts and rules, the reasoning processes are performed by Jess rule engine to return the heuristic physical effects to the users.

The Formalization of Physical Effects

During the process of Su-Field analysis, while the inventive standards do not produce recommendations in terms of what physical substances or fields should be used, the collection of physical effects provides the mapping between technical functions and available natural laws.

In the standard method, the physical effects are searched with the help of pointers, that is, each physical effect should be mapped to its corresponding pointers. However, the pointers to physical effects and the specific problems are built at different levels of abstraction. It is therefore difficult for users to choose among too many eligible pointers to physical effects given a certain function, while with a detailed context of the problem, it is possible that no pointer to a physical effect be returned. Accordingly, the physical effects need to be re-formalized to be at the same level of abstraction with the specific problems. The intuition is that if the physical effects can be formalized with two states, which is similar with Su-Field model, then the match between the solution model of the specific problem and the physical effects should be much easier.

As stated above, the basic idea is that each physical effect is formalized as a couple of two states, before and after applying it. Firstly, the physical effects (PE) are divided into three types, that is, PE about substances, PE about fields and PE about parameters1 according to the object they are related to, for example, a physical effect used to absorb gas is considered as PE about substances. Considering that the classifications of substance and field are much more clear and well-defined than the hierarchy of parameters of system, only the physical effects about substances and fields are taken into account in this research.

The Formalization of Physical Effects

Then, according to the different ways to change, two sub-types are defined respectively for each kind of physical effects:

• PE about substances -PE to add substances -PE to modify substances

• PE about fields -PE to add fields

-PE to modify fields

The physical effects of the types "PE to modify substances/fields" are modeled as shown in Figure 6.2(a). For the physical effects of the types "PE to add substances/fields", two additional states "Incremental substance" and "Incremental field" are added respectively to describe the incremental part after applying the physical effects, as depicted in Figure 6.2(b). Generally, each physical effect may correspond to several kinds of change of states, and so this formalization of physical effects is implemented based on the accurate analysis of physical effects with the help of domain experts. For example, "PE81-Evaporation: by Generally, each physical effect may correspond to several kinds of change of states, and so this formalization of physical effects is implemented based on the accurate analysis of physical effects with the help of domain experts. For example, "PE81-Evaporation: by using the physical effect Evaporation, vapor can be generated from liquid", described as using the physical effect Evaporation, vapor can be generated from liquid", described as Figure 6.3, and its corresponding formalization is shown in Figure 6.4.

Ontology Reasoning for Searching Heuristic Physical Effects

This section describes the process of searching heuristic physical effects in detail. Firstly, in order to formalize the knowledge of using physical effects, two ontologies -the inventive standards ontology (Section 5.4.1) and the physical effects ontology are built. Then, the reasoning rules are set to describe this heuristic process. Finally, the ontology inference is carried out to provide the heuristic physical effects. According to the four behavior concepts presented in Section 6.2, Sub_PE corresponds to Add_SPE and Modify_SPE and Field_PE to Add_FPE and Modify_FPE. For Add_SPE and Add_FPE, there are two additional properties hasIncrementalStateSub and hasIncrementalStateField to describe the incremental Substance or Field.

The Physical Effects Ontology

As shown in Figure 6.5, the hierarchy of Substance with three-level (SClass1, SClass2 and SClass3 ) and Field with four-level (FClass1, FClass2, FClass3 and FClass4 ) are defined respectively. The property includes is used to define the relations "parentchild" (hypernym-hyponym) between two objects, such as, "Liquid" and "Water", and it consists of two sub-properties: includes_Sub for Substance and includes_Field for Field. For each sub-property, several sub-subsidiary properties are defined to represent the relations in different levels, for example, two sub-subsidiary properties includes_SC1 and includes_SC2 represent the relations of SClass1 and SClass2, SClass2 and SClass3. According to this hierarchy, Substance and Field are instantiated, and the detailed classifications are shown in Appendix H. We take the physical effect "PE81-Evaporation" as an example, its corresponding concepts and relationships are built in the physical effects ontology. There are several types of change during the process of the evaporation, such as, add "Vapor" or change "Pressure". Supposed that its initial state includes Substance "Liquid" and the final state is comprised of "Liquid" and "Vapor". As a result, the physical effect "PE81-Evaporation" is considered as an Add_SPE, and its values of the properties hasInitialStateSub, hasFinalStateSub and hasIncrementalStateSub are "Liquid", "Liquid and Vapor" and "Vapor". Table 6.1 shows several OWL encoding classes and properties in the physical effects ontology.

Ontology Reasoning Rules

The rules for searching heuristic physical effects are divided into two classes: forty-two IS (Inventive Standard) rules and eight PE (Physical Effect) rules. The inference with the IS rules yields to several abstract types of physical effects, and the PE rules are used to 110 6.3. Ontology Reasoning for Searching Heuristic Physical Effects Table 6.1: The OWL encoding classes and properties in the physical effects ontology.

Classes and Properties <owl:Class rdf:ID=" Physical_Effect "> <rdfs:subClassOf> <owl:Class rdf:ID="owl:Thing"/> </rdfs:subClassOf> </owl:Class> <owl:Class rdf:ID="Field_PE"> <rdfs:subClassOf> <owl:Class rdf:ID="Physical_Effect"/> </rdfs:subClassOf> </owl:Class> <owl:Class rdf:ID="Add_FPE"> <rdfs:subClassOf rdf:resource="#Field_PE"/> </owl:Class> <owl:Class rdf:ID="Modify_FPE"> <rdfs:subClassOf rdf:resource="#Field_PE"/> <owl:disjointWith> <owl:Class rdf:ID="Add_FPE"/> </owl:disjointWith> </owl:Class> <owl:DatatypeProperty rdf:ID="hasKeyword_PE"> <rdfs:domain rdf:resource="#Physical_Effect"/> <rdfs:range rdf:resource="http://www.w3.org/2001/XMLSchema#string"/> </owl:DatatypeProperty> <owl:ObjectProperty rdf:ID="hasInitialStateField"> <rdfs:domain rdf:resource="#Field_PE"/> <rdfs:range rdf:resource="#Field"/> <rdfs:subPropertyOf> <owl:ObjectProperty rdf:ID="hasInitialState"/> </rdfs:subPropertyOf> </owl:ObjectProperty> find the concrete physical effects to instantiate the Solution Model.

Ontology-based Approach for Using the Physical-Chemical-Geometrical Effects 111

Table 6.2: The IS rules and explanation for General Solution 1.

Name

IS rules and explanation Rule 1 P roblem(?x) ∧ InventiveStandard(?y) ∧ chooses_IS(?x, ?y)∧ has_keyid_of _type(?y, 1) → chooses_P E_T ype(?x, 0) If the chosen Inventive Standard belongs to the type 1, the PE of type 0 will be chosen. Rule 2 P roblem(?x) ∧ InventiveStandard(?y) ∧ chooses_IS(?x, ?y)∧ has_keyid_of _type(?y, 1) → chooses_P E_T ype(?x, 2) If the chosen Inventive Standard belongs to the type 1, the PE of type 2 will be chosen.

IS Rules

As stated Section 5.3.3, the user needs to choose one from five similar inventive standards of A-class and B-class obtained based on the method of calculating semantic similarity.

According to the type of chosen inventive standard, its related types of physical effects are generated based on the inference with the IS rules in this section. Accordingly, forty-two IS rules (Appendix I.1) are divided into two classes:

• Eight IS rules for A-class inventive standards: Forty inventive standards are condensed and generalized into seven generalized standard solutions, and each kind of standards correspond to the same types of physical effects. As a result, the inference rules are set according to seven generalized standard solutions instead of all the forty inventive standards. There are at least one IS rule for each generalized standard solution.

• Thirty-four IS rules for B-class inventive standards: There are thirty-one inventive standards identified as the implementation of existing inventive principles, and each standard corresponds to distinctive types of physical effects. So the IS rules are set for each inventive standard.

For example, there are two IS rules for General Solution 1, as shown in Table 6.2. The obtained values (0,1,2,3) of the property chooses_PE_Type are: 0 -add substance, 1modify substance, 2 -add field, 3 -modify field.

Generally, several kinds of physical effects are obtained in this step, and the user needs to provide more concrete information in order to find the appropriate physical effects in 6.3. Ontology Reasoning for Searching Heuristic Physical Effects Table 6.3: The PE rules and explanation for the physical effects Add_SPEs.

Name

PE rules and explanation Rule 1 P roblem(?x) ∧ Generic_P roblem_M odel(?y)∧ Generic_Solution_M odel(?z) ∧ correspondsT o_GP M (?x, ?y)∧ correspondsT o_GSM (?x, ?z) ∧ chooses_P E_T ype(?x, 0)∧ Substance(?a) ∧ changesOnOrF or_Element(?z, ?a) ∧ Add_SP E(?b)∧ hasIncrementalSubstance(?b, ?a) → chooses_P E(?x, ?b) If a certain substance needs to be added, all the PEs which can add this substance, are obtained. Rule 2 P roblem(?x) ∧ Generic_P roblem_M odel(?y)∧ Generic_Solution_M odel(?z) ∧ correspondsT o_GP M (?x, ?y)∧ correspondsT o_GSM (?x, ?z) ∧ chooses_P E_T ype(?x, 0)∧ Substance(?a) ∧ changesOnOrF or_Element(?z, ?a) ∧ Substance(?c)∧ includes_Sub(?a, ?c) ∧ Add_SP E(?b)∧ hasIncrementalSubstance(?b, ?c) → chooses_P E(?x, ?b) If a certain substance needs to be added, all the PEs which can add this substance or its children substances, such as, liquid and water, are obtained. the next step. Two types of information need to be provided: on the one hand, only one type of physical effect needs to be chosen from the obtained set, and on the other hand, the level of abstraction for the problem description needs to be set according to the classifications of Substance and Field (Appendix H) in the Su-Field Model Ontology. For example, the user wants to "add pure gas" rather than "add mixed gas".

PE Rules

According to the physical effects Ontology, the PE rules are set to search the most appropriate physical effects for the special case. As presented in Section 6.3.1, there are four classes of physical effects, that is, Add_SPE, Modify_SPE, Add_FPE and Modify_FPE, and each class corresponds to two PE rules, that is, one for searching the physical effects related to the chosen object (Substance or Field) and the other for searching the physical effects related to the children objects of the chosen one. Eight PE rules (Appendix I.2) are designed for searching heuristic physical effects, for example, for the physical effects Add_SPEs, the PE rules and explanation are shown in Table 6.3.

Implementing the Query of Physical Effects based on the

Rule Engine

The process of searching heuristic physical effects is carried out through the use of Jess, which includes three tasks:

• The knowledge about the use of physical effects, including the inventive standards ontology and the physical effects ontology in OWL is transferred to Jess facts.

• The constraints about searching for heuristic physical effects in SWRL are transferred to Jess rules.

• Jess rule engine executes to generate the heuristic physical effects.

The Function of OWL -> Jess

This phase consists of two phases: on the one hand, the classes and their hierarchy in the inventive standards ontology and the physical effects ontology are mapped onto the Jess templates; on the other hand, the individuals and their relationships in the inventive standards ontology and the physical effects ontology are transformed into Jess facts.

Ontology Reasoning for Searching Heuristic Physical Effects

For example, as shown in Table 6.4, the class Physical_Effect and its classification are transformed into Jess classes, and an instance of Modify_FPE, that is, Mos-bauer_Absorption, and its relationship with gammaRayField are transferred to Jess facts.

The Function of SWRL -> Jess

There are fifty SWRL rules, including forty-two IS rules and eight PE rules, transferred to Jess rules. Taking the IS rules in Table 6.2 and the PE rules in Table 6.3 as examples, the corresponding Jess rules are shown in Table 6.5.

Ontology-based Approach for Using the Physical-Chemical-Geometrical Effects 115

As shown in Fig 9, several necessary individuals are built through Protégé or Protégé-OWL API in Java applications, such as, "Problem_1", "Generic_Problem_Model_1" and "Generic_Solution_Model_1". Then JESS will run its inference engine and generate some new knowledge, which is represented as JESS facts. After transferring to OWL format, the final results are shown in Fig 10, in which a heuristic physical effect "Evaporation" is related to "Problem_1" for generating gas.

The Execution of the Jess Inference Engine

In this phase, the Jess rule engine is executed to generate the heuristic physical effects. As shown in Figure 6.6, several necessary individuals are built through Protégé or Protégé-OWL API1 in Java applications, for example, "Problem_1", "Generic_Problem_Model_1" and "Generic_Solution_Model_1". Then Jess will run its inference engine and generate some new knowledge, which is represented as Jess facts. After transferring to OWL format, the final results are shown in Figure 6.7, in which a heuristic physical effect "Evaporation" is related to "Problem_1" for generating gas. The detailed process of using this method to solve a specific problem is depicted in Section 7.4.4.

Summary

This chapter firstly proposes a new way of representing physical effects using the change of two states, that is, the couple of two states before and after applying physical effects. Then, the physical effects ontology is built in OWL (Ontology Web Language), and the constraint knowledge of using physical effects is formalized in SWRL (Semantic Web Rule Language). Finally, the reasoning process of using physical effects is performed with the support of Jess (Java Expert System Shell) rule engine.

This research facilitates the use of physical effects and can improve the available status of using effects to a certain extent. Compared with the classical "indexing" way used in most commercial software, the formalization based on ontologies provides conceptual resources for knowledge based system (KBS) and makes it possible to automate the process of the resolution of inventive problems. It also permits the tracking of the different applications to study and compare them, and, in this way, the improvement of the whole methodology.

State of the Art

TRIZ to solve inventive problems.

The CREAX1 Function database organizes a database of physical effects by function, and uses a web-based application to support the search of physical effects. It responds to user-entered query by processing the query to a combination of two pre-stored key words, which is quite limited in the specific applications. The Invention Machine's GoldFire2 implements the search of scientific physical effects based on semantic methods. With the good performance of the proposed semantic methods and the standard representative way of problem and solution, the results are almost satisfied. However, the hierarchy of the effects is stable and any modification of the classification need to be maintained manually.

Our research group, including an expert practitioner of IDM3 /TRIZ, has been working for several years on the formalization of basic concepts and the knowledge bases of the methodology, as well as on the extension of it to complex problems. The work within a consortium with Arcelor-Mittal, Alstom and EADS has permitted the funding of the development of a software tool based on these research works. The industrial partners provided a number of examples and cases to test the tool, refine it and finally evaluate it.

The software developed by LGECO, called STEPS, is able to effectively accompany the user from the formulation phase of the problem to its resolution, taking into account the specific context of its application. However, using STEPS to solve a specific problem requires extensive knowledge of different engineering domains and is not currently supported by it. Consequently, the resolution mainly depends on the experience and knowledge of the user. STEPS exists in two versions. A commercial one, managed by a start-up company, Time To Innovate4 ; and a research version, property of the consortium and INSA de Strasbourg, which implements the ontology of the TRIZ/IDM concepts we have created [1,2]. Both versions of STEPS can provide valuable assistance to the user through the intelligent analysis based on the statistic data.

In order to facilitate the inventive problem solving process, a prototype called IngeniousTRIZ was developed based on semantic similarity and ontology reasoning during these research works. On the one hand, according to the TRIZ knowledge sources ontologies, the prototype offers to the users the relevant knowledge sources of the model they are building. On the other hand, the prototype has the ability to fill the models of the other knowledge sources automatically.

Through IngeniousTRIZ, the users start solving inventive problems with forty inventive principles, which are simple to use, to obtain an abstract solution. Then according to the selected inventive principle, the similar inventive standards, which are complicated to use, are obtained based on the semantic similarity calculated in advance. With the help of these similar inventive standards, the Su-Field models are built to generate the abstract solutions based on ontology inference. Finally, in order to interpret the abstract solution in real life, the heuristic physical effects are obtained based on ontology inference to generate the appropriate concept solutions.

The Environment and Tools

The prototype IngeniousTRIZ is developed in the Windows operating system, and the used software and libraries are shown in Table 7.1.

• Java Platform(Java SE) Java 1 , created by by James Gosling from Sun Microsystems in 1991, is an object-oriented programming language with a built-in application 1 http://www.oracle.com/us/technologies/java/overview/index.html.

The Environment and Tools

programming interface (API) 1 that can handle graphics and user interfaces and that can be used to create applications or applets. The first publicly available version of Java (Java 1.0) was released in 1995, and the current version is Java 1.7 which is also known as Java 7. From the Java programming language the Java platform evolved.

The Java platform is not specific to any one processor or operating system, but rather an execution engine (called a virtual machine) and a compiler with a set of libraries that are implemented for various hardware and operating systems so that Java programs can run identically on all of them. The Java platform is divided into four types in terms of the purpose of use:

-Java ME 2 : Java Platform, Micro Edition, is a Java platform designed for embedded systems (mobile devices are one kind of such systems). Target devices range from industrial controls to mobile phones (especially feature phones) and set-top boxes.

-Java SE 3 : Java Platform, Standard Edition, is a widely used platform for development and deployment of portable applications for desktop and server environments.

-Java EE 4 : Java Platform, Enterprise Edition, is Oracle's enterprise Java computing platform. The platform provides an API and runtime environment for developing and running enterprise software, including network and web services, and other large-scale, multi-tiered, scalable, reliable, and secure network applications.

-JavaFX 5 : JavaFX is the next step in the evolution of Java as a rich client platform. It is designed to provide a lightweight, hardware-accelerated Java UI platform for enterprise business applications.

Taking into account the development with portable application in this research, Java Platform(Java SE) 1.7 is used.

• NetBeans 1 NetBeans is an integrated development environment (IDE) for developing with Java. NetBeans IDE provides first-class comprehensive support for the newest Java technologies and latest Java enhancements before other IDEs, and it is the first IDE providing support for JDK 7. Furthermore, the design of the graphical user interface (GUI) for Java EE, Java SE and Java ME applications can be implemented quickly and smoothly by dragging and positioning GUI components from a palette into the NetBeans Editor. Especially for Java SE applications, NetBeans GUI Builder automatically takes care of correct spacing and alignment, while supporting in-place editing, as well. Taking into account the characteristics of the prototype IngeniousTRIZ, that is, a frame-based Java SE application, NetBeans 7.2 is chosen as the environment for its development.

• MySQL 2 The history3 of the MySQL database goes back to 1979 when TcX, the company that developed MySQL, started working with database programs. In May 1996, MySQL version 1.0 was released to a limited group of four people, and in October 1996, MySQL 3.11.1 was released to the public as a binary distribution for Solaris. A month later, a Linux binary and the source distribution were released. The MySQL release included an ODBC driver in source form, which also included many free MySQL clients ported to MySQL. Nowadays, the MySQL database has become the world's most popular open source database because of its consistent fast performance, high reliability and ease of use.

In order to connect to the MySQL database from the Java program, JDBC (Java Database Connectivity) 4 , a Java API that provides a set of interfaces, is used. JDBC programming consists of the following six steps:

-Register the driver. When the driver class is initialized, it is automatically registered with JDBC drivers.

-Make a connection to the MySQL database using Connection object. Driver-Manager class or DataSource object can be used to create Connection object.

-Create Statement object. This act as a channel to send SQL queries. Alternatively PreparedStatement, the pre-compiled SQL statement, can be used.

-Send SQL statements to the MySQL database.

-Process the result returned from SQL statement. For example, SELECT statement returns number of records, and ResultSet object is used to store the obtained records.

-Close all resources like ResultSet, Statement and Connection objects.

In this application, a database called "IngeniousTRIZ", which consists of nine tables, is built as shown in Table 7.2.

• WordNet1 WordNet, proposed by George A. Miller in 1980s, is a large lexical database of English. It groups English words into sets of synonyms called synsets, provides short, general definitions, and records the various semantic relations between these synonym sets. The purpose is twofold: to produce a combination of dictionary and thesaurus that is more intuitively usable, and to support automatic text analysis and artificial intelligence applications.

WordNet distinguishes between nouns, verbs, adjectives and adverbs because they follow different grammatical rules. Every synset contains a group of synonymous words or collocations (a collocation is a sequence of words that go together to form a specific meaning, such as "car pool"); different senses of a word are in different synsets. The meaning of the synsets is further clarified with short defining glosses (Definitions and/or example sentences). A typical example synset with gloss is: good, right, ripe -(most suitable or right for a particular purpose; "a good time to plant tomatoes"; "the right time to act"; "the time is ripe for great sociological changes").

In order to use WordNet in Java program, JWNL (Java WordNet Library)1 , a pure java implementation of WordNet API, provides functionality not only for data access, but also for relationship discovery and morphological processing.

• Protégé2 Protégé, developed by Stanford University, is a free, open-source platform that provides a growing user community with a suite of tools to construct domain models and knowledge-based applications with ontologies. Protégé can be extended by the way of a plug-in architecture and a Java-based Application Programming Interface (API) for building knowledge-based tools and applications.

The Protégé-OWL editor3 is an extension of Protégé that supports OWL, which is the most recent development in standard ontology languages, endorsed by W3C to promote the Semantic Web vision. The Protégé-OWL editor enables users to4 :

-Load and save OWL and RDF ontologies.

-Edit and visualize classes, properties, and SWRL rules.

-Define logical class characteristics as OWL expressions.

-Execute reasoners such as description logic classifiers.

-Edit OWL individuals for Semantic Web markup.

In the Protégé-OWL editor, there exists various plug-ins offering a variety of additional features, such as extra ontology management tools, multimedia support, querying and reasoning engines, problem solving methods, etc. The following are the plug-ins that we use in this research: -OntoViz 1 : The OntoViz Tab allows you to visualize Protégé ontologies with the help of a highly sophisticated graph visualization software called Graphviz from AT&T. The types of visualizations are highly configurable and include picking a set of classes or instances to visualize parts of an ontology, displaying slots and slot edges, and specifying colors for nodes and edges.

-OWLViz 2 : The OWLViz Tab enables the class hierarchies in an OWL ontology to be viewed and incrementally navigated, allowing comparison of the asserted class hierarchy and the inferred class hierarchy.

-SWRLTab 3 : The SWRLTab is a development environment for working with SWRL rules in Protégé-OWL. It supports the editing and execution of SWRL rules and includes a set of libraries that can be used in rules, mainly including SWRL Editor, SWRL APIs, SWRL Built-in Libraries, SWRL Jess Bridge, etc.

As presented in Chapter 4, 5 and 6, three ontologies, that is, the inventive principles ontology, the inventive standards ontology, and the physical effects ontology are built in Protégé. For example, Figure 7.1 shows the framework of the inventive standards ontology in Protégé.

In order to manipulate the OWL ontologies in this Java application, the following two interfaces are used:

-Protege-OWL API 1 The Protege-OWL API is an open-source Java library for the Web Ontology Language (OWL). This API provides classes and methods to load and save OWL files, to query and manipulate OWL data models, and to perform reasoning based on Description Logic Engines. Furthermore, the API is optimized for the implementation of graphical user interfaces. The API is designed to be used in two contexts: * For the development of components that are executed inside of the Protege-OWL editor's user interface * For the development of stand-alone applications (e.g., Java applications or Eclipse plug-ins)

-OWL API 2 The OWL API is a Java API and reference implementation for creating, manipulating and serializing OWL Ontologies. The latest version of the API is focused towards OWL 2.

As shown in Figure 7.2, OWL-API is used to build the instances and their properties, SWRL Rule Engine API3 to execute the ontology reasoning, and Protege-OWL API to read the inferred results, which are stored in new ontologies after the inference.

• Jess 4 Jess is a rule engine and scripting environment written entirely in Sun's Java language by Ernest Friedman-Hill. Because its powerful scripting language gives access to all of Java's APIs, Jess is selected as the reasoning engine in this application. Figure 7.3 shows the process of ontology inference with Jess.

The Framework of IngeniousTRIZ

As shown in Figure 7.4, the window-based IngeniousTRIZ consists of eleven continuous windows to solve the problem and three windows to store the results. This prototype integrates the approaches presented in Chapter 3, 4, 5 and 6 and therefore, each approach corresponds to several steps as follows: Step6 Su-Field Analysis-1

Step8 Su-Field Analysis-3

Step9 Heuristic Physical Effects-1

Step7 Su-Field Analysis-2

Step10 Heuristic Physical Effects-2

Step11 Heuristic Physical Effects-3

Heuristic Concept Solution-3 -Step1: Contradiction Description In order to solve the contradiction in the specific problem, the user is guided to input the related parameters, such as, two specific parameters.

-Step2: Contradiction Formalization Based on the information obtained in Step1, the contradiction is displayed in the table form, which makes easier for the user to implement the following analysis.

-Step3: SP->GEP In order to search inventive principles through the contradiction matrix, two specific parameters need to be matched with thirtynine generic engineering parameters respectively. The user can choose them manually, or with the help of semantic search. According to the proposed methods in Chapter 4, five most similar generic engineering parameters are obtained for each specific parameter.

-Step4: Inventive Principle With the help of the contradiction matrix, a series of inventive principles are obtained. The user can check the detailed information of each inventive principle.

-Concept Solution After obtaining the appropriate inventive principle, the user can stop this process, design concept solution, and store the related information from Step1 to Step4.

• Step4->Step8: The search of heuristic abstract solutions. The sequence diagram for Step4->Step8 is shown in Figure 7.6.

-Step5: Semantic Inventive Standard According to the specific problem, the user needs to select an appropriate inventive standard, which is the most similar one with the obtained inventive principle. The user can choose them manually, or with the help of semantic search, through which, the five most similar inventive standards can be obtained automatically.

-Step6: Su-Field Analysis-1 As the first step of Su-Field analysis, the user is guided to provide the elements (substances and field) of the Problem Model.

-Step7: Su-Field Analysis-2 According to the chosen inventive standard, the corresponding types of modification are returned. The user needs to select one from them.

-Step8: Su-Field Analysis-3 Based on the information provided above, the ontology inference is executed on the inventive standards ontology to generate heuristic abstract solutions, which is displayed in the form of the Solution Model.

-Heuristic Concept Solution After obtaining the heuristic abstract solutions, the user can stop this process, design heuristic concept solution, and store the related information from Step1 to Step8.

TRIZ User

Step9: Heuristic Physical Effects-1 Step8: Su-Field Analysis-3

Step10: Heuristic Physical Effects- -Step9: Heuristic Physical Effects-1 In this step, the type of physical effects is selected. Several suggested types of physical effects are returned based on the chosen inventive standard, and the user can select one from them.

-Step10: Heuristic Physical Effects-2 The user needs to provide the level of granularity for the element to be changed or added in the Solution Model.

-Step11: Heuristic Physical Effects-3 The ontology reasoning is executed on the inventive standards ontology and the physical effects ontology to generate the heuristic physical effects.

-Heuristic Concept Solution After obtaining the heuristic abstract solutions and physical effects, the user finishes the whole process, designs heuristic concept solution, and stores the related information from Step1 to Step11.

IngeniousTRIZ: The Case of the "Diving Fin"

In order to verify the functions of the proposed prototype, the detailed process of using IngeniousTRIZ to solve the specific problem of the "Diving Fin" is introduced in this section.

The Problem

Even if the use of diving fins becomes popular, there are still some problems with them.

The divers need to make a great effort to push water, which often makes them tired. Generally, the surface of the diving fin should be small for offering minimal resistance to water in order to minimize the effort of the diver and, at the same time, it should also be big in order to push water more efficiently.

In the following steps, the user is guided to solve this problem step by step in the prototype IngeniousTRIZ.

Abstract Solution from Inventive Principles (Step1 -> Step4)

In the phase from Step1 to Step4, the user works with the contradiction matrix and inventive principles to obtain the abstract solution.

Firstly, in order to launch the prototype, the user can easily double click the file IngeniousTRIZ.jar or use the command Java -jar IngeniousTRIZ.jar in the Command Prompt Window.

• Step1 Contradiction Description: As shown in Figure 7.8, the user needs to provide the parameters and values for the technical contradiction1 in the case of the "Diving Fin".

• Step2 Contradiction Formalization: According to the information in Step1, the contradiction in the case of the "Diving Fin" is represented in the table form, which is much easier for the user to analyze the contradiction. As presented in Figure 7.9, there is a contradiction between "Kicking efficiency" and "Ease of use", that is, if

• Step3 SP->GEP: In order to use contradiction matrix, the specific parameters need to be matched with thirty-nine generic engineering parameters. Based on the methods proposed in Chapter 4, five most similar generic engineering parameters are obtained through the semantic search for each specific parameter. As shown in Figure 7.10 (a) and (b), the two specific parameters in the case of the "Diving Fin" correspond to five similar generic engineering parameters respectively:

-SP1: kicking efficiency * GEP9: speed * GEP19: use of energy by moving object * GEP33: ease of operation * GEP34: ease of repair * GEP39: productivity -SP2: ease of use * GEP19: use of energy by moving object * GEP20: use of energy by stationary object * GEP32: ease of manufacture * GEP33: ease of operation * GEP34: ease of repair Taking into account the characteristics of this case, "SP1: kicking efficiency" is matched with "GEP19: use of energy by moving object" while "SP2: ease of use" is matched with "GEP33: ease of operation".

• Step4 Inventive Principle: In this step, a series of inventive principles are obtained according to the two selected generic engineering parameters in Step3. For this case, 4 inventive principles are displayed as shown in Figure 7.11:

-Inventive Principle 32 : Color changes (a) Change the color of an object or its external environment.

(b) Change the transparency of an object or its external environment.

-Inventive Principle 28 : Mechanics substitution (c) Change from static to movable fields, from unstructured fields to those having structure.

(d) Use fields in conjunction with field-activated (e.g. ferromagnetic) particles.

-Inventive Principle 13 : "The other way round" (a) Invert the action(s) used to solve the problem (e.g. instead of cooling an object, heat it).

(b) Make movable parts (or the external environment) fixed, and fixed parts movable).

(c) Turn the object (or process) "upside down".

-Inventive Principle 12 : Equipotentiality (a) In a potential field, limit position changes (e.g. change operating conditions to eliminate the need to raise or lower objects in a gravity field). Then the user needs to choose one from them, and in this case, for example, Inventive Principle 13 is selected, and the detailed description is displayed in the text box. Finally, when click the button "Next", a message box is launched to ask whether the user continues to work with semantic inventive standards or not, as shown in Figure 7.12. If the user choose "Yes", Step5 is launched, and or else, the window "Concept Solution" to save the results from Step1 to Step4 is launched.

• Concept Solution: Based on the information obtained above, the user can design concept solution, for example, "change some fixed parts of diving fin movable", as shown in Figure 7.13 (a), and then save all the results in a document, as described in Figure 7.13 (b).

Heuristic Abstract Solution from Inventive Standards (Step4 -> Step8)

In the phase from Step4 to Step8, the user is guided to work with semantic inventive standards based on the chosen inventive principles. In order to implement Su-Field analysis, the user needs to choose one from the obtained five similar inventive standards. In the case of the "Diving Fin", Inventive Standard 5.1.1.5 is selected.

• Step6 Su-Field Analysis-1: With the help of the chosen inventive standard, the user is guided to implement Su-Field analysis from this step. As shown in Figure 7.15, the user needs to provide the basic information about the Problem Model, including two substances and a field. This information will be used to instantiate the inventive standards ontology in the process of ontology reasoning.

• Step7 Su-Field Analysis-2: In this step, several related types of transformation are obtained based on the chosen inventive standards, from which the user needs to choose appropriate one for the specific case. There are two ways of representing the transformation, that is, the transformation of Su-Field models and the text description, as shown in Figure 7.16 (a) and (b). In the case of the "Diving Fin", only one type of transformation -"A normal additive is to be introduced in very small quantities and concentrated in certain parts of the object." is obtained and used for the following ontology inference, as shown in Figure 7.16 (b).

Step8 Su-Field Analysis-3: Based on the obtained information from Step5 to Step7, the ontology inference, as presented in Figure 7.17 As shown in Figure 7.18, for the case of the "Diving Fin", the elements (substances and field) in the Solution Model are generated after ontology inference. At the same time, the heuristic abstract solutions are created automatically in terms of two aspects:

-The modification in Substance1-"Water": A normal additive in very small quantities and concentrated in certain parts of "Water" is to be introduced.

-The modification in Substance2-"Diving Fin": A normal additive in very small quantities and concentrated in certain parts of "Diving Fin" is to be introduced.

Assuming that water cannot be modified in this case, we need to modify the diving fin by bringing in some additives. There are several kinds of modifications of the diving fin, for example, the change of its structure and its shape by using liquid or gas. In order to modify the diving fin, the user can choose to do it manually by closing this prototype, and also can continue to work with the heuristic physical effects, which can help the user to instantiate the Solution Model in real life. As a result, when the user clicks the button "Next", a message box is launched to ask whether the user continues to work with heuristic physical effects or not, as shown in Figure 7.19. If the user choose "Yes", Step9 is launched, and or else, the window "Heuristic Concept Solution" to save the results from Step1->Step8 is launched.

• Heuristic Concept Solution: According to the obtained heuristic abstract solutions, the user can design heuristic concept solution, such as "Introduce an additive in very small quantities to be concentrated in certain parts of the diving fins", as shown in Figure 7.20 (a), and then save all the results in a document, as depicted in Figure 7.20 (b).

The Search of Heuristic Physical Effects (Step8-> Step11)

In the phase from Step8 to Step11, the user works with the heuristic physical effects to instantiate the obtained heuristic abstract solution in real life. • Step9 Heuristic Physical Effects-1: In this step, four ways to improve the Problem Model are given, that is, "Add a Substance", "Modify a Substance", "Add a Field" and "Modify a Field", and the user needs to select appropriate one from them.

According to the inventive standards chosen above, a heuristic ways is indicated to facilitate this process. For the case of the "Diving Fin", as presented in Figure 7.21, "Modify a Substance" is suggested to modify the Problem Model.

• Step10 Heuristic Physical Effects-2: In order to implement the ontology reasoning, the user also needs to provide the level of granularity for the element (substance or field) to be modified. Figure 7.22 (a) and (b) show the level of granularity for substance and field respectively, and this case corresponds to the first one.

• Step11 Heuristic Physical Effects-3: Based on the obtained information from Step9 and Step10, the ontology inference, presented in Figure 7.23 (a), is executed to generate the heuristic physical effects, which are stored in a new-built ontology.

The user needs to choose one from the list according to the specific problem

The level of granularity of Substance The user can select an appropriate physical effect for solving the specific problem. As shown in Figure 7.23 (b), for the case of the "Diving Fin", two physical effects-"Thixotropy" and "Acoustic Vibrations" are obtained1 , and their detailed description is displayed in the text box with images to show the functional process.

Assuming that the physical effect "Thixotropy" is chosen to solve this problem, and the user can easily save the results by clicking the button "Save", and or else, he/she can exit the prototype with out saving the results by clicking the button "Exit".

• Heuristic Concept Solution: As presented in Figure 7.24 (a), the user can design and explore the heuristic concept solution according to the obtained heuristic abstract solution and physical effect as following:

Heuristic Concept Solution: The effect "Thixotropy" is the property of certain gels or fluids that are thick (viscous) under normal conditions, but flow (become thin, less viscous) over time when shaken, agitated, or otherwise stressed. Taking this into account, we use a shear thickening liquid to modify the diving fin, and propose the concept of tubular shear thickening fins as shown in Figure 7.25. This concept is a 7.5. Summary truly inventive concept since there are no-existing widely distributed and produced products in industry based on this principle (only prototypes, patented fabrics, dampers in the truck industry, etc.).

Finally, all the obtained results are saved in a document, as depicted in Figure 7.24 (b).

Summary

In order to verify the methods proposed in Chapters 3, 4, 5 and 6, the prototype IngeniousTRIZ was developed. In this chapter, firstly a general introduction about the development of this prototype is given. Then the environment and tools for its development are illustrated in detail. Finally, the framework of the prototype is introduced and taking the case of the "Diving Fin" as an example, the detailed process of using IngeniousTRIZ to solve problem is explained step by step. The implementation of the prototype IngeniousTRIZ, not only verifies the proposed methods in this research, but also stimulates the development of a new research area in the field of inventive design, that is, the use of an automatic ontology-based mechanism for solving inventive problems.

Chapter 8

Conclusions and Perspectives

In recent decades, modern innovation theories and methods proposed various knowledge sources to solve different types of inventive problems. The TRIZ knowledge sources are all built independently of the specific application field, and their levels of abstraction are very different, resulting in an additional difficulty of interpretation. These research works aim at facilitating and automating the process of solving inventive problems based on semantic similarity and ontology techniques. At first, the TRIZ knowledge sources are formalized based on ontologies, such that heuristic inference can be executed to search for specific solutions. Then, methods for calculating semantic similarity are explored to search and define the missing links among the TRIZ knowledge sources.

In order to solve inventive problems, the TRIZ user firstly chooses a TRIZ knowledge source to work for an abstract solution. Then, the items of other knowledge sources, which are similar with the selected items of the first knowledge source, are obtained based on semantic similarity calculated in advance. With the help of these similar items and the heuristic physical effects, other specific solutions are returned through ontology inference.

The prototype "IngeniousTRIZ" is developed based on semantic similarity and ontology inference to support this automatic process of solving inventive problems.

In the following sections, the research works of each chapter is firstly summarized, and then, the major contributions of our research are presented. Finally, in order to improve this research in the future, several prospective points are proposed.

Conclusions

Conclusions

In Chapter 1, the background of this research has been firstly introduced, that is, the proposed TRIZ knowledge sources and models were built at different levels of abstraction and their use required extensive knowledge about different engineering domains. In order to facilitate the use of TRIZ, an Automatic Ontology-based Mechanism for Solving Inventive Problems has been proposed. Then, the motivations and research scope of this thesis have been explained in detail. Finally, the structure of this thesis has been presented.

Chapter 2 has presented a literature review on TRIZ and the comprehensive knowledge about Ontology-based Knowledge Modeling and Ontology Reasoning. In the TRIZ part, we have presented the definition of TRIZ, illustrated the application steps of TRIZ solving inventive problem and introduced the basic tools of TRIZ, including the contradiction, the TRIZ knowledge sources and the physical-chemical-geometrical effects. In the parts of Ontology Modeling and Reasoning, an overview of their basic methods and concepts has been provided, and then the current situation of their applications in the field of inventive design has been given.

In Chapter 3, two methods have been presented, which were used to match the parameters in the use of inventive principles and to define the missing links among the TRIZ knowledge sources respectively. Firstly, a literature review about semantic similarity has been presented, and then the related methods of calculating semantic similarity in the domain of inventive design have been given. Finally, the proposed methods in this thesis have been introduced in detail.

In Chapter 4, in order to facilitate the use of Altshuller's contradiction matrix in an inventive design study, the methods of matching specific parameters and generic engineering parameters based on semantic similarity and case-based reasoning have been introduced in detail. The inventive principles ontology has been firstly built to formalize the process of using the contradiction matrix, and then according to Chapter 3, the methods of calculating semantic similarity have been used to fill the gap between specific parameters and generic engineering parameters.

In order to improve the performance of this method, the results from previous projects have been returned to users as suggested solutions according to a case-based reasoning approach. As we can observe from our first attempt to test the developed methodology, some relevant potential associations between a specific term and one or several of the thirty-nine generic engineering parameters have been proposed to users. The improved method has also benefited from previous experiences and therefore offered a constant improvement due to CBR techniques.

However, the resolution of inventive problems only with forty inventive principles can not always provide comprehensive and efficient recommendations, and it is necessary to apply other TRIZ knowledge sources, such as seventy-six inventive standards.

As a result, Chapter 5 has introduced a method of searching heuristic abstract solutions from different knowledge sources based on semantic similarity and ontology reasoning. Considering that all the TRIZ knowledge sources are described in short-text, the missing links among the TRIZ knowledge sources have been defined based on the methods of calculating short-text semantic similarity, which have been introduced in Chapter 3. Meanwhile, the ontology reasoning mechanism deployed on Protégé and Jess, has been used to provide heuristic solutions dynamically for TRIZ users. Firstly, TRIZ users started solving inventive problems with the TRIZ knowledge source of their choice to obtain an abstract solution, and then, according to the selected items of that first knowledge source, the similar items of other knowledge sources were obtained based on the semantic similarity calculated in advance. With the help of these similar items, heuristic abstract solutions have been returned through the ontology inference on Jess.

As the last step of solving inventive problems, especially for Su-Field analysis, the obtained abstract solutions need to be instantiated in real life with the help of physical effects, that is, the physical effects compatible with the context of the specific problem should be chosen to assist the users to instantiate the abstract solution. However, the physical effects and the specific problems are built at different levels of abstraction, and it is difficult for the users to choose, that is, given a certain function, too many physical effects are chosen while with the detailed context of the problem, no physical effect is returned.

In order to facilitate the use of physical effects, Chapter 6 has firstly proposed a new way of representing physical effects using the change of two states, that is, the couple of two states before and after applying them. Then, knowledge about using physical effects has been formalized based on ontology, and constraint knowledge, such as the conditions to use each kind of physical effects, has been formalized by using the rule language. Finally, the reasoning process of using physical effects has been performed with the support of the 8.2. Contributions Jess rule engine.

In order to verify the methods proposed in Chapter 3, 4, 5 and 6, the prototype IngeniousTRIZ has been developed. In Chapter 7, firstly a general introduction about the development of this prototype has been given. Then the environment and tools for the development and the framework of the prototype have been introduced in detail. At last, taking the case of the "Diving Fin" as an example, the detailed process of using IngeniousTRIZ to solve the inventive problem has been explained step by step.

Contributions

By solving the research problems described in Section 1.2, an automatic ontology-based mechanism for solving inventive problems is proposed based on semantic similarity and ontology techniques in this thesis. The contributions of this thesis can be summarized into three aspects as following:

Firstly, the TRIZ knowledge sources are formalized based on ontology modeling. Through the detailed analysis for the process of using each knowledge source, the related concepts and their relations are defined in the ontologies. In comparison to traditional commercial software, the formalization based on ontologies provides conceptual resources for knowledge based systems (KBS) and makes it possible to automate the process of solving inventive problems by using ontology reasoning. It also permits the tracking of different applications to study and compare them based on ontologies, and, in this way, the improvement of the whole methodology.

Secondly, different methods for calculating semantic similarity are proposed to facilitate the process of solving inventive problems. On the one hand, in order to search appropriate inventive principles in the contradiction matrix, the match between specific parameters and generic engineering parameters is implemented automatically based on semantic similarity, through which the efficiency and accuracy of choosing inventive principles to solve problems have been improved greatly. On the other hand, the missing links among the TRIZ knowledge sources are defined based on semantic similarity. Compared with the manual work, it uses semantic methods to compare the different abstract models or solutions and gives an additional analysis to eliminate the various interpretations of different TRIZ users.

Conclusions and Perspectives 159

Thirdly, in order to facilitate the resolution of inventive problems with TRIZ, an automatic mechanism of solving inventive problems is proposed based on semantic similarity, ontology modeling and ontology inference. In this mechanism, the TRIZ users start solving an inventive problem with the TRIZ knowledge source of their choice to obtain an abstract solution. According to the selected items of that first knowledge source, the similar items of other knowledge sources are obtained based on the semantic similarity calculated in advance. With the help of these similar items and the heuristic physical effects, other specific solutions are returned through ontology inference.

For the industries today, this thesis presents a proposal for solving inventive problems with TRIZ in a different way from the existing approaches widely used in the domain of inventive design, since our research makes it possible to transform the solution obtained with one knowledge source to other that is obtained with another one that would not have been used by the TRIZ user, and therefore provides new options to solve inventive problems with diversified knowledge sources. By using this mechanism, on the one hand, more solutions for specific cases from different TRIZ knowledge sources can be obtained automatically, and so the efficiency of operating inventive practice in design and also in the Research and Development (R&D) department can be improved. On the other hand, the resolution with more than one TRIZ knowledge sources instead of a single source can yield to more stable and appropriate specific solutions, and so the risk of generating useless solutions could be reduced greatly.

Furthermore, with the help of this mechanism, it is not necessary for the designers in the industries to be trained for all the TRIZ knowledge sources. They can be trained to use a simple TRIZ knowledge source to solve inventive problems, and the heuristic solutions from other complicated knowledge sources can be obtained automatically through this mechanism. Consequently, it will increase the attractiveness for the companies to train and use TRIZ in the process of inventive design.

For the educational viewpoint, this research provides an intelligent support for solving inventive problems. An educator would argue our proposals by saying that easing the job of an engineer by automating part of his thinking process may result in a diminution of his potential skills acquisition. It is partly true, this is why we would not propose the systematic use of these methods if the goals were to increase engineering knowledge or improve analogy-reasoning skills of a population of students. Nevertheless, even in such circumstances, our automated procedure can be a "backup solution" to a lack of 8.3. Perspectives understanding of a given situation.

Perspectives

Evaluation

In order to demonstrate the applicability and usefulness of our approaches and the prototype, more applications in real industrial cases are necessary and important. As stated in this thesis, the process of using TRIZ to solve inventive problems is complex and several simple cases studies are not enough to completely evaluate and validate the applicability and efficiency of the automatic ontology-based mechanism for solving inventive problems.

Through the case study presented in Chapter 7, the progressiveness and appropriateness of the prototype for providing more relevant and heuristic knowledge for helping TRIZ users to innovate have been illustrated . But, the actual results show that we are far from to say that a really new innovation has been achieved with the help of our prototype (lack of industrial data).

As a result, all the proposed methods in this thesis will be integrated in the research version of the software STEPS for the industrial evaluation1 , and the detailed process of evaluation will consist of four steps:

• Step1. Define the parameters of the evaluation.

a. The purpose of the evaluation is to test the performance of the prototype IngeniousTRIZ, which will be integrated as a part of software STEPS.

b. The evaluator consist of direct evaluator-software users and indirect evaluatorsoftware developer.

c. The users will use IngeniousTRIZ to search concept solutions for specific problems.

d. The process of using IngeniousTRIZ is made up of three phases, and at the end of each phase, the corresponding results will be obtained:

-Abstract solution from inventive principles -> Obtained: Appropriate inventive principles.

-Heuristic abstract solution from inventive standards -> Obtained: Heuristic abstract solution.

-The search of heuristic physical effects -> Obtained: Heuristic physical effects.

The users will be guided to evaluate the results obtained in each phase according to the methods described in Step2.

e. The software developer will receive all information from the users, based on which the performance of using IngeniousTRIZ to solve specific problems can be calculated according to Step3.

• Step2. Design the methods used for the evaluation.

a. The criterion is the ability to help users to find accurate and complete solutions for specific cases efficiently.

b. Three parameters are defined to measure the performance of IngeniousTRIZ:

-Accuracy: to estimate whether the obtained results are accurate for solving the specific case. The score value ranges from 0.0 to 1.0, and the higher the value, the more accurate the results.

-Completeness: to estimate whether all potential solutions for specific case are obtained. The value is from 0.0 to 1.0, and the higher the value, the more complete the results.

-Efficiency: to estimate whether the time is well used during the resolution of specific problem. The value is from 0.0 to 1.0, and the higher the value, the more efficient the process of this prototype.

c. At the end of each phase, the users need to give a value for each parameter according to the following guidelines: a. The weight of three parameters W a (weight of accuracy), W c (weight of completeness) and W e (weight of efficiency) (W a + W c + W e = 1) can be set manually. For example, the similar generic engineering parameters need to be obtained as many as possible to match the specific parameters in different domains, so the weights are: W a = 0.3, W c = 0.5 and W e = 0.2.

b. The performance for each phase can be quantified according to the following formula:

V perf ormance-phase = n i=1 (W a S ai + W c S ci + W e S ei ) n (8.1)
where n is the number of users, and S ai , S ci and S ei are the score values of parameter accuracy, completeness and efficiency given be the ith user (1

≤ i ≤ n).
8. Conclusions and Perspectives 163 c. The performance for the whole prototype can be quantified as following:

V perf ormance = n i=1 1 3 3 j=1 (W j a S j ai + W j c S j ci + W j e S j ei ) n (8.2)
where W j a , W j c and W j e are weights of three parameter in the jth phase (1 ≤ j ≤ 3), and S j ai , S j ci and S j ei are the score values of three parameters for the jth phase given by the ith user (1 ≤ i ≤ n).

• Step4. Report and make decisions.

a. The software developer calculates the value of performance for each phase and also for the whole process.

b. Based on the obtained values, the performance of each phase and the whole process of the prototype IngeniousTRIZ can be identified as four types: According to this evaluation process, the human-made "translations" will be tested and compared with the results obtained with the proposed methods at a wider scale, and in this way, the relative importance of them with regards to the pertinence of the solutions obtained can also be measured.

Improvement

Although the automatic ontology-based mechanism for solving inventive problems proposed in this thesis can facilitate the process of using TRIZ effectively, there are still some improvements that can be made in the further research:

The first problematic factor is WordNet, which is the semantic dictionary used in the calculation of semantic similarity of short texts. The proposed methods will not always 8.3. Perspectives be able to satisfy the requirements of some specialized problem because of the limits of the WordNet dictionary. The interaction with users will solve this problem to a certain extent. For the moment, there is no strategy to lead the use of the SWRL rules to take profit of the knowledge about the specific case being solved. We intend, therefore, to provide a communication platform with users thanks to the development of SWRL rules describing the process of the interaction.

Secondly, during the process of searching heuristic abstract solutions, TRIZ users start solving inventive problems with forty inventive principles to obtain an abstract solution, and then, according to the selected inventive principles, the similar items of seventy-six inventive standards are obtained based on the semantic similarity calculated in advance. With the help of these similar items, the useful heuristic abstract solutions are obtained through the ontology inference. However, the direct transformation from solving technical contradictions to implementing Su-Field analysis, from the resolution with forty inventive principles to that with seventy-six inventive standards, results in a certain amount of missing information in the heuristic abstract solutions. For example, in the case of the "Diving Fin", only the specific parameters "kicking efficiency" and "ease of use" are considered to solve the technical contradiction, while the physical parameters of diving fins or using diving fins, such as the circumstance of its use and its components, are not considered, which often makes the user confused to modify the diving fin according to the obtained concept solution. One way to solve this problem is to bring in the analysis with eleven separation methods and physical contradictions to provide complementary information, that is, for the chosen inventive principles, not only the similar inventive standards are used to generate the heuristic abstract solutions, but also the similar separation methods are used to provide the information in physical level.

Last but not least, in order to solve various specific problems, the number and the content of the physical effects need to change dynamically according to the development of different kinds of fields, for example, Visual Effects recently becoming accessible owing to the appearance of the affordable animation and compositing software. The list of physical effects used in our research has been proposed several years ago, and in order to keep its dynamicity, we intend to use text mining techniques to extract the useful information for the physical effects from online resources -Wikipedia, such as, their initial and final states, and then instantiate them automatically through Protégé-OWL API.

Improving parameter

Worstening parameter (c) Make each part of an object fulfill a different and useful function.

• Inventive Principle 4 : Asymmetry (a) Change the shape of an object from symmetrical to asymmetrical.

(b) If an object is asymmetrical, increase its degree of asymmetry.

• Inventive Principle 5 : Merging (a) Bring closer together (or merge) identical or similar objects, assemble identical or similar parts to perform parallel operations.

(b) Make operations contiguous or parallel; bring them together in time.

• Inventive Principle 6 : Universality (a) Make a part or object perform multiple functions; eliminate the need for other parts.

• Inventive Principle 7 : "Nested doll"

(a) Place one object inside another; place each object, in turn, inside the other.

(b) Make one part pass through a cavity in the other.

• Inventive Principle 8 : Anti-weight (a) To compensate for the weight of an object, merge it with other objects that provide lift.

(b) To compensate for the weight of an object, make it interact with the environment (e.g. use aerodynamic, hydrodynamic, buoyancy and other forces).

• Inventive Principle 9 : Preliminary anti-action (a) If it will be necessary to do an action with both harmful and useful effects, this action should be replaced with anti-actions to control harmful effects.

(b) Create beforehand stresses in an object that will oppose known undesirable working stresses later on.

• Inventive Principle 10 : Preliminary action (a) Perform, before it is needed, the required change of an object (either fully or partially).

(b) Pre-arrange objects such that they can come into action from the most convenient place and without losing time for their delivery.

• Inventive Principle 11 : Beforehand cushioning (a) Prepare emergency means beforehand to compensate for the relatively low reliability of an object.

• Inventive Principle 12 : Equipotentiality (a) In a potential field, limit position changes (e.g. change operating conditions to eliminate the need to raise or lower objects in a gravity field).

• Inventive Principle 13 : "The other way round" (a) Invert the action(s) used to solve the problem (e.g. instead of cooling an object, heat it).

(b) Make movable parts (or the external environment) fixed, and fixed parts movable).

(c) Turn the object (or process) 'upside down'.

• Inventive Principle 14 : Spheroidality -Curvature (a) Instead of using rectilinear parts, surfaces, or forms, use curvilinear ones; move from flat surfaces to spherical ones; from parts shaped as a cube (parallelepiped) to ball-shaped structures.

(b) Use rollers, balls, spirals, domes.

(c) Go from linear to rotary motion, use centrifugal forces.

• Inventive Principle 15 : Dynamics (a) Allow (or design) the characteristics of an object, external environment, or process to change to be optimal or to find an optimal operating condition.

(b) Divide an object into parts capable of movement relative to each other.

(a) Conduct a process , or certain stages (e.g. destructible, harmful or hazardous operations) at high speed.

• Inventive Principle 22 : "Blessing in disguise" or "Turn Lemons into Lemonade" (a) Use harmful factors (particularly, harmful effects of the environment or surroundings) to achieve a positive effect.

(b) Eliminate the primary harmful action by adding it to another harmful action to resolve the problem.

(c) Amplify a harmful factor to such a degree that it is no longer harmful.

• Inventive Principle 23 : Feedback (a) Introduce feedback (referring back, cross-checking) to improve a process or action.

(b) If feedback is already used, change its magnitude or influence.

• Inventive Principle 24 : 'Intermediary' (a) Use an intermediary carrier article or intermediary process.

(b) Merge one object temporarily with another (which can be easily removed).

• Inventive Principle 25 : Self-service (a) Make an object serve itself by performing auxiliary helpful functions.

(b) Use waste resources, energy, or substances.

• Inventive Principle 26 : Copying (a) Instead of an unavailable, expensive, fragile object, use simpler and inexpensive copies.

(b) Replace an object, or process with optical copies.

(c) If visible optical copies are already used, move to infrared or ultraviolet copies.

• Inventive Principle 27 : Cheap short-living objects (a) Replace an inexpensive object with a multiple of inexpensive objects, comprising certain qualities (such as service life, for instance).

• Inventive Principle 28 : Mechanics substitution (a) Replace a mechanical means with a sensory (optical, acoustic, taste or smell) means.

(b) Use electric, magnetic and electromagnetic fields to interact with the object.

(c) Change from static to movable fields, from unstructured fields to those having structure.

(d) Use fields in conjunction with field-activated (e.g. ferromagnetic) particles.

• Inventive Principle 29 : Pneumatics and hydraulics (a) Use gas and liquid parts of an object instead of solid parts (e.g. inflatable, filled with liquids, air cushion, hydrostatic, hydro-reactive).

• Inventive Principle 30 : Flexible shells and thin films (a) Use flexible shells and thin films instead of three dimensional structures.

(b) Isolate the object from the external environment using flexible shells and thin films.

• Inventive Principle 31 : Porous materials (a) Make an object porous or add porous elements (inserts, coatings, etc.).

(b) If an object is already porous, use the pores to introduce a useful substance or function.

• Inventive Principle 32 : Color changes (a) Change the color of an object or its external environment.

(b) Change the transparency of an object or its external environment.

• Inventive Principle 33 : Homogeneity (a) Make objects interacting with a given object of the same material (or material with identical properties).

• Inventive Principle 34 : Discarding and recovering (a) Make portions of an object that have fulfilled their functions go away (discard by dissolving, evaporating, etc.) or modify these directly during operation.

(b) Conversely, restore consumable parts of an object directly in operation.

•

D. THE 76 INVENTIVE STANDARDS

Sub-class 1.1 Building of Su-Fields (if incomplete) (8 IS)

The major recommendations from this sub-class are:

• Make the Su-Field complete.

• Make it minimally workable by introducing an internal additive.

• Make it minimally workable by introducing an external additive.

• Use minimal -maximal mode (add more and remove the extras; add less and enhance locally).

Sub-class 1.2 Destruction of Su-Field (harms) (5 IS)

The major recommendations from this sub-class are:

• Introduce a third substance between the given two substances.

• Introduce a third substance from the super-system.

• Introduce a third substance that is a modification of one of the given two substances.

• Introduce a sacrificial substance.

• Introduce a field that counteracts the harmful field.

Class 2: Development of Substance-Field Models

This class is used for improving the efficiency of engineering systems by introducing minor modifications.

It offers concept solutions of how to improve and evolve systems. The major recommendations from this class are:

• Use of chain Su -Fields Rule P roblem(?x) ∧ Generic_P roblem_M odel(?y)∧ Generic_Solution_M odel(?z) ∧ correspondsT o_GP M (?x, ?y)∧ correspondsT o_GSM (?x, ?z) ∧ chooses_P E_T ype(?x, 3)∧

Introduction

Un nombre croissant d'industries ressentent le besoin de formaliser leurs processus d'innovation. Dans ce contexte, les outils du domaine de la qualité et les approches d'aide à la créativité provenant du "brain storming" ont déjà montré leurs limites. Afin de répondre à ces besoins, la TRIZ (Acronyme russe pour Théorie de Résolution des Problèmes Inventifs), développée par l'ingénieur russe G. S. Altshuller au milieu du 20 ème siècle, propose une méthode systématique de résolution de problèmes inventifs multi-domaines. L'approche de résolution de problèmes inventifs de la TRIZ se déroule en trois phases, comme illustré dans la Fig. 1.

1. La phase "Formulation", où l'expert utilise différents outils pour modéliser le problème sous la forme d'une ou plusieurs contradictions 1 ou d'autres modèles. 2. La phase "Recherche d'une solution abstraite", où l'accès aux différentes bases de connaissances permet d'obtenir un ou plusieurs modèles de solution. Généralement, les utilisateurs de la TRIZ doivent avoir une grande maîtrise des concepts associés à la méthodologie. Ils doivent être capables de choisir la solution abstraite en fonction du problème réel. 3. La phase "Instanciation", où la solution abstraite est instanciée à l'aide de la base de connaissances d'effets physiques, pour obtenir une ou plusieurs solutions conceptuelles dans le monde réel. Plusieurs modèles et bases de connaissances contribuent à la résolution de problèmes inventifs. Ils sont de nature différente: Ces travaux de recherche visent à faciliter et automatiser le processus de résolution de problèmes inventifs. Ils sont basés sur le calcul de similarité sémantique et font usage de différentes technologies provenantes de domaine de l'Ingénierie de Connaissances (modélisation et raisonnement basés sur les ontologies, notamment). Tout d'abord, des méthodes de calcul de similarité sémantique sont proposées pour rechercher et définir les liens prototype de conception doit avoir deux valeurs mutuellement exclusives de la même paramètre physique. manquants entre les bases de connaissance de la TRIZ. Ensuite, les sources de connaissance de la TRIZ sont formalisées comme des ontologies afin de pouvoir utiliser des mécanismes d'inférence heuristique pour la recherche de solutions spécifiques.

Pour résoudre des problèmes inventifs, les utilisateurs de la TRIZ choisissent dans un premier temps une base de connaissance et obtiennent une solution abstraite. Ensuite, les éléments des autres bases de connaissance similaires aux éléments sélectionnés dans la première base sont proposés sur la base de la similarité sémantique préalablement calculée. A l'aide de ces éléments et des effets physiques heuristiques, d'autres solutions conceptuelles sont obtenues par inférence sur les ontologies.

Enfin, un prototype logiciel est développé. Il est basé sur cette similarité sémantique et les ontologies interviennent en support du processus de génération automatique de solutions conceptuelles.

Recherche Doctorale

 Théorie

Comme le montre la Fig. 2, il existe trois approches théoriques (les boites de couleur verte) utilisées dans cette thèse: Le calcul de similarité sémantique, la modélisation des connaissances basée sur les ontologies et le raisonnement basé sur les ontologies. Le mécanisme proposé pour la résolution des problèmes inventifs basé sur la connaissance est composé de trois étapes (les boites de couleur orange):  L'utilisation des sources de connaissance de la TRIZ: Afin de faciliter l'utilisation des bases de connaissance de la TRIZ, une nouvelle méthode de résolution de problèmes inventifs est proposée basée sur des ontologies, comme explicité dans la Section 1. L'architecture de cette méthode est illustré dans la Fig. Troisièmement, afin de démontrer l'applicabilité de nos approches et du prototype, il sera nécessaire et important de les appliquer sur plus des cas industriels. Comme indiqué dans cette thèse, la résolution de problèmes inventifs avec TRIZ est complexe et plusieurs cas simples ne suffisent pas. En conséquence, toutes les méthodes proposées dans cette thèse seront intégrées dans le logiciel STEPS. Les recherches futures dans ce domaine consistèrent à comparer les traductions d'origine humaine avec les résultats obtenus par nos méthodes.

Conclusions et Perspectives

Figure 1 . 2 :

 12 Figure 1.2: The main successive models in TRIZ.

  and Medium-sized Enterprises RID: Research-Invention & Development

Figure 1 . 3 :

 13 Figure 1.3: The research scope of our study in LGECO and BFO/ICUBE.

Figure 1 .Figure 1 . 4 :

 114 Figure 1.4 presents three main technologies used in this thesis, that is, Semantic Similarity, Ontology-based Knowledge Modeling and Ontology Reasoning, and based on these technologies, the automatic ontology-based mechanism for solving inventive problem is made up of three steps:

Figure 1 . 5 :

 15 Figure 1.5: The main structure of this thesis.

Figure 2 . 1 :

 21 Figure 2.1: The approach of TRIZ solving inventive problem.

2. 1 .

 1 The Theory of Inventive Problem Solving -TRIZ

Figure 2 . 2 :Figure 2 . 3 :

 2223 Figure 2.2: The first scheme for the contradictions in the case of a "clothespin".contradiction_2:

Figure 2 . 4 :Modify Substance S 2 Figure 2 . 5 :

 24225 Figure 2.4: Complete a Su-Field model.

Figure 2 . 6 :

 26 Figure 2.6: Modify substance S 1 .

Figure 2 . 7 :

 27 Figure 2.7: Change existing field.

Figure 2 . 8 :

 28 Figure 2.8: Introduce counteractive field.

Figure 2 . 9 :Figure 2 . 10 :

 29210 Figure 2.9: Introduce another positive field.

2. 1 .

 1 Figure 2.11: The organization of the pointers to the physical-chemical-geometrical effects.

Figure 2 . 12 :

 212 Figure 2.12: The CREAX database.

2. 2 .Figure 2 . 14 :

 2214 Figure 2.14: The spectrum of information artifacts.

Figure 2 . 15 :

 215 Figure 2.15: The Protégé SWRLJESSTab visualization.

Figure 4 . 1 :

 41 Figure 4.1: The process of using the contradiction matrix.

Figure 4 . 2 :

 42 Figure 4.2: The inventive principles ontology.

Figure 4 . 3 :

 43 Figure 4.3: The CBR cycle.

Figure 4 . 4 :Figure 4 . 5 :

 4445 Figure 4.4: The whole flowchart of the improved method.

  A. Human Necessities B. Performing Operations; Transporting C. Chemistry; Metallurgy D. Textiles; Paper E. Fixed Constructions F. Mechanical Engineering; Lighting; Heating; Weapons; Blasting G. Physics 1 http://www.wipo.int/classifications/ipc/en/.

76 4 . 4 .

 44 The improved Method: Semantic Similarity + Case-based Reasoning H. Electricity

Figure 4 . 6 :

 46 Figure 4.6: The bottom-up method of the retrieval of the group-class-subclass tree.

4 .

 4 Figure 4.7: The Evaluation of the improved method.

Figure 5 . 1 :

 51 Figure 5.1: An example of correspondences of items of different knowledge sources.

Figure 5 . 2 :

 52 Figure 5.2: The framework of the ontology-based approach.

1 shows the similar inventive standards for InventiveP rinciple 1 ,

 1 InventiveP rinciple 2 and InventiveP rinciple 36: • InventiveP rinciple 1: Segmentation (a) Divide an object into independent parts. (b) Divide an object into parts so that some its part can be easily taken away. (c) Increase the degree of object segmentation. • InventiveP rinciple 2: Taking Away (a) Take away an interfering part of the object. If some property of the object interferes, find what part of the object is a carrier of the property and separate it from the object. • InventiveP rinciple 36: Phase Transitions (a) Use physical phenomena accompanying phase transitions: change of volume, emission or absorption of heat, etc. InventiveP rinciple 1 is matched with Inventive Standard 59 (B-class), Inventive Principle 36 with Inventive Standard 29, 64, 65, 66, 67 (A-class), and Inventive Principle 2 with Inventive Standard 36, 44, 41, 62 (A-class) and Inventive Standard 59 (B-class).

90 5 . 4 .

 54 Ontology Reasoning for Searching Heuristic Abstract Solutions

Figure 5 . 3 :

 53 Figure 5.3: The inventive standards ontology.

  Generic_Solution_Model.Chooses_PE is defined to represent the relation between Problem and the chosen Phys-ical_Effect (see next chapter). The properties has_keyid_of_type and chooses_PE_Type 92 5.4. Ontology Reasoning for Searching Heuristic Abstract Solutions Table5.2: The OWL encoding classes and properties in the inventive standards ontology.

Table 5 .

 5 2 shows several OWL encoding classes and properties in the inventive standards ontology. The relations between super-classes and sub-classes are defined by SubClassOf, such as Substance and Field are two subclasses of Element. The properties are used to represent the relations between one object and another object or data, for example, description maps Element with its description in string type, and correspondsTo_GPM 5. Ontology-based Search for Heuristic Abstract Solutions 93 matches Problem and its corresponding Generic_Problem_Model.

94 5 . 4 .

 54 Ontology Reasoning for Searching Heuristic Abstract Solutions -has_S1_Num_GPM =0: * The additive Substance 1 is added to complete the incomplete problem model.

Figure 5 . 4 :

 54 Figure 5.4: An example of inference result.

6 .

 6 Ontology-based Approach for Using the Physical-Chemical-Geometrical Effects 105 (a) most unknown (b) most often used

Figure 6 . 1 :

 61 Figure 6.1: Frequency of TRIZ's main components.

Figure 6 . 2 :

 62 Figure 6.2: The formalization of physical effects(PE).

6 .Fig. 3 .

 63 Fig. 3. Formalization for the types PE to modify substance/Field Fig. 4. Formalization for the types PE to add substance/Field

Fig 5 ,

 5 and its corresponding formalization is shown in Fig6.

Fig. 5 .

 5 Fig. 5. The physical effect Evaporation: Liquid ─> Vapor Fig. 6. Formalization of the physical effect Evaporation

Figure 6 . 3 :

 63 Figure 6.3: Evaporation: Liquid -> Vapor.

Figure 6 . 4 :

 64 Figure 6.4: The formalization of Evaporation.

Figure 6 .

 6 Figure 6.5 shows the framework of the physical effects ontology. The class Physical_Effect is built with the property keyword, which makes it possible to obtain the heuristic physical effects through the search of keyword[3]. For each Physical_Effect, two states before and after the use of physical effects, are defined through the properties hasInitialState and hasFinalState. All the Physical_Effects are divided into two main kinds: Sub_PE -the Physi-cal_Effects which change Substance and Field_PE -the Physical_Effects which change

Figure 6 . 5 :

 65 Figure 6.5: The physical effects ontology.

Fig. 9 .

 9 Fig. 9. Before inference

Fig. 9 .

 9 Fig. 9. Before inference

124 7 . 2 .

 72 The Environment and Tools

7. 2 .Figure 7 . 1 :

 271 Figure 7.1: The inventive standards ontology in Protégé.

Figure 7 . 2 :

 72 Figure 7.2: The Interaction Between IngeniousTRIZ and Ontologies.

Figure 7 . 3 :

 73 Figure 7.3: The process of ontology inference with Jess.

Figure 7 . 4 :

 74 Figure 7.4: The framework of IngeniousTRIZ.

Figure 7 . 5 :

 75 Figure 7.5: The sequence diagram for Step1->Step4.

11 : 14 : 2 Figure 7 . 6 :

 1114276 Figure 7.6: The sequence diagram for Step4->Step8.

2 1 11 :Figure 7 . 7 :

 21177 Figure 7.7: The sequence diagram for Step8->Step11.

Figure 7 . 10 :Figure 7 . 11 :

 710711 Figure 7.10: Step3: The semantic match between SPs and GEPs.

7. 4 .Figure 7 . 12 :

 4712 Figure 7.12: Step4: The message box.

Figure 7 . 14 : 2 -

 7142 Figure 7.14: Step5: Semantic inventive standards for the case of the "Diving Fin".

Figure 7 . 15 :

 715 Figure 7.15: Step6: Su-Field Analysis-1 for the case of the "Diving Fin".

  (a) and (b), is executed to generate heuristic abstract solutions, which are stored in a new-built ontology.

( a )Figure 7 . 16 :

 a716 Figure 7.16: Step7: Su-Field Analysis-2 for the case of the "Diving Fin".

Figure 7 . 17 :Figure 7 . 18 :

 717718 Figure 7.17: The ontology inference for the heuristic abstract solution in the case of the "Diving Fin".

7. The Prototype: IngeniousTRIZ 145 Figure 7 . 19 :

 145719 Figure 7.19: Step8: The message box.

  Design the concept solution according to the obtained information from Step1->Step8 (a) To design (b) To save

Figure 7 . 20 :

 720 Figure 7.20: Heuristic Concept Solution for the case of the "Diving Fin".

Figure 7 . 21 :

 721 Figure 7.21: Step9: Heuristic Physical Effects-1 for the case of the "Diving Fin".

( a )

 a The level of granularity for substance The user needs to choose one from the list according to the specific problem The level of granularity of Field (b) The level of granularity for field

Figure 7 .

 7 Figure 7.22: Step10: Heuristic Physical Effects-2 for the case of the "Diving Fin".

Figure 7 .

 7 Figure 7.23: Step11: Heuristic Physical Effects-3 for the case of the "Diving Fin".

Figure 7 . 25 :

 725 Figure 7.25: The concept of tubular shear thickening fins.

------

  It is exact result I want in this phase, and I am sure it can be used to solve this problem. (value=1.0) 8.3. Perspectives -It is exact result I want in this phase, but I am not sure it can be used to solve this problem. (value=0.8) The result is acceptable to some extent in this phase. (value=0.4) It is not result I want in this phase. (value=0.0) 2. Completeness: All potential solutions are obtained in this phase. (value=1.0) Most solutions are obtained in this phase. (value=0.8) -A few of solutions are obtained in this phase. (value=0.4) No useful solution is obtained in this phase. (value=0.0) 3. Efficiency: -High. (value=1.0) -Moderate. (value=0.8) -Acceptable. (value=0.4) -Inacceptable. (value=0.0) d. The software developer collect the data when the users finish the whole process of solving problems. • Step3. Set standards and collect evidence.

B

  Inventive Principle 35 : Change of physical and chemical parameters (a) Change the object's aggregate state. (b) Change concentration or consistency of the object. (c) Change the degree of flexibility of the object. (d) Change the temperature of the object or environment. • Inventive Principle 36 : Phase transitions (a) Use phenomena occurring during phase transitions (e.g. volume changes, loss or absorption of heat, etc.). • Inventive Principle 37 : Thermal expansion (a) Use thermal expansion (or contraction) of materials. (b) If thermal expansion is being used, use multiple materials with different coefficients of thermal expansion. • Inventive Principle 38 : Strong oxidants (a) Replace common air with oxygen-enriched air. (b) Replace enriched air with pure oxygen. (c) Expose air or oxygen to ionizing radiation. (d) Use ionized oxygen. (e) Replace ozonized (or ionized) oxygen with ozone. • Inventive Principle 39 : Inert atmosphere (a) Use inert gases instead of usual ones. (b) Add neutral parts or additives to the object. • Inventive Principle 40 : Composite materials (a) Change from uniform to composite (multiple) materials.

1 P 2 P 3 P

 123 roblem(?x) ∧ InventiveStandard(?y) ∧ chooses_IS(?x, ?y)∧ has_keyid_of _type(?y, 1) ∧ Generic_P roblem_M odel(?z)∧ correspondsT o_GP M (?x, ?z) ∧ Generic_Solution_M odel(?a)∧ correspondsT o_GSM (?x, ?a) ∧ F ield(?b) ∧ Substance(?c)∧ hasF ield_GP M (?z, ?b) ∧ hasSubstance2_GP M (?z, ?c)∧ has_S1_N um_GP M (?z, 0) ∧ has_S2_N um_GP M (?z, 1)∧ has_F _N um_GP M (?z, 1) → hasF ield_GSM (?a, ?b)∧ hasSubstance1_GSM (?a, added_S1_x2)∧ hasSubstance2_GSM (?a, ?c) ∧ has_S1_N um_GSM (?a, 1)∧ has_S2_N um_GSM (?a, 1) ∧ has_F _N um_GSM (?a, 1) If the component Substance1 is missing, add one to complete the Su-Field Model.Rule roblem(?x) ∧ InventiveStandard(?y) ∧ chooses_IS(?x, ?y)∧ has_keyid_of _type(?y, 1) ∧ Generic_P roblem_M odel(?z)∧ correspondsT o_GP M (?x, ?z) ∧ Generic_Solution_M odel(?a)∧ correspondsT o_GSM (?x, ?a) ∧ F ield(?b) ∧ Substance(?c)∧ hasF ield_GP M (?z, ?b) ∧ hasSubstance1_GP M (?z, ?c)∧ has_S1_N um_GP M (?z, 1) ∧ has_S2_N um_GP M (?z, 0)∧ has_F _N um_GP M (?z, 1) → hasF ield_GSM (?a, ?b)∧ hasSubstance2_GSM (?a, added_S2_y2)∧ hasSubstance1_GSM (?a, ?c) ∧ has_S1_N um_GSM (?a, 1)∧ has_S2_N um_GSM (?a, 1) ∧ has_F _N um_GSM (?a, 1) If the component Substance2 is missing, add one to complete the Su-Field Model.Rule roblem(?x) ∧ InventiveStandard(?y) ∧ chooses_IS(?x, ?y)∧ has_keyid_of _type(?y, 1) ∧ Generic_P roblem_M odel(?z)∧ correspondsT o_GP M (?x, ?z) ∧ Generic_Solution_M odel(?a)∧ correspondsT o_GSM (?x, ?a) ∧ Substance(?c) ∧ Substance(?d)∧ hasSubstance1_GP M (?z, ?c) ∧ hasSubstance2_GP M (?z, ?d)∧ has_S1_N um_GP M (?z, 1) ∧ has_S2_N um_GP M (?z, 1)∧ has_F _N um_GP M (?z, 0) → hasF ield_GSM (?a, added_F _z2)∧ hasSubstance1_GSM (?a, ?c) ∧ hasSubstance2_GSM (?a, ?d)∧ has_S1_N um_GSM (?a, 1) ∧ has_S2_N um_GSM (?a, 1)∧ has_F _N um_GSM (?a, 1) If the component Field is missing, add one to complete the Su-

Rule 10 P

 10 roblem(?x) ∧ InventiveStandard(?y) ∧ chooses_IS(?x, ?y)∧ has_keyid_of _type(?y, 225) → chooses_P E_T ype(?x, 3) If the chosen Inventive Standard belongs to the type 225, HEURISTIC PHYSICAL EFFECTS Name PE rules and explanations Substance(?a) ∧ changesOnOrF or_Element(?z, ?a) ∧ Substance(?b)∧ includes_Sub(?a, ?b) ∧ M odif y_SP E(?c)∧ hasInitialStateSub(?c, ?b) → chooses_P E(?x, ?c) If a certain substance needs to be modified, all the PEs which can modify this substance or its children substances, such as, liquid and water, are obtained. Rule P roblem(?x) ∧ Generic_P roblem_M odel(?y)∧ Generic_Solution_M odel(?z) ∧ correspondsT o_GP M (?x, ?y)∧ correspondsT o_GSM (?x, ?z) ∧ chooses_P E_T ype(?x, 2)∧ F ield(?a) ∧ changesOnOrF or_Element(?z, ?a) ∧ Add_F P E(?b)∧ hasIncrementalF ield(?b, ?a) → chooses_P E(?x, ?b) If a certain field needs to be added, all the PEs which can add this field, are obtained. Rule P roblem(?x) ∧ Generic_P roblem_M odel(?y)∧ Generic_Solution_M odel(?z) ∧ correspondsT o_GP M (?x, ?y)∧ correspondsT o_GSM (?x, ?z) ∧ chooses_P E_T ype(?x, 2)∧ F ield(?a) ∧ changesOnOrF or_Element(?z, ?a) ∧ F ield(?c)∧ includes_F ield(?a, ?c) ∧ Add_F P E(?b)∧ hasIncrementalF ield(?b, ?c) → chooses_P E(?x, ?b) If a certain field needs to be added, all the PEs which can add this field or its children fields, such as, Electric field and Electrodynamic field, are obtained. Rule P roblem(?x) ∧ Generic_P roblem_M odel(?y)∧ Generic_Solution_M odel(?z) ∧ correspondsT o_GP M (?x, ?y)∧ correspondsT o_GSM (?x, ?z) ∧ chooses_P E_T ype(?x, 3)∧ F ield(?a) ∧ changesOnOrF or_Element(?z, ?a) ∧ M odif y_F P E(?b)∧ hasInitialStateF ield(?b, ?a) → chooses_P E(?x, ?b) If a certain field needs to be modified, all the PEs which can modify this field, are obtained.

Figure 1 .

 1 Figure 1. Le processus d'utilisation de TRIZ pour résoudre des problèmes inventifs.

Figure 2 .

 2 Figure 2. La structure principale.

Figure 3 .

 3 Figure 3. L'organigramme de la méthode proposée.

Figure 9 .

 9 Figure 9. Diagramme de séquence pour Etape 8-> Etape 11.

  1 uses Vampire theorem prover, Surnia 2 uses Otter theorem prover are FOL based reasoner.• Combination of FOL and General Logic based Inference Engine: This type of reasoners are designed based on FOL and general logic. Horn Logic 3 is most widely used due to its simplicity and availability. The reasoners Java Expert System

		INFERENCE
	WORKING	ENGINE
	MEMORY
		PATTERN MATCHER	EXECUTION ENGINE
	RULE
	BASE	AGENDA

Figure 2.16: The architecture of Jess.

  detailed solution of the 4.2. The Inventive Principles Ontology

	Contradiction Matrix			Feature
	-version: String			-description: String
	1
	39*39
	Item	hasPositivePrimaryFeature	PrimaryFeature		AppliedFeature
	-hasPositivePrimaryFeature: PrimaryFeature		-description: String	-description: String
	-hasNegativePrimaryFeature: PrimaryFeature	hasNegativePrimaryFeature
	1 0...n
	InventivePrinciple		PrimarySubIP		hasPositive	hasNegative
	-IPNum: Int		-description: String -hasSubject: Subject		Applied Feature	Applied Feature
			-hasPredicate: Predicate
	hasPrimaryDescriptionOf	-hasObject: Object
		SubIP
	1
	1...n
	SubInventivePrinciple
	-IPNum: Int		AppliedSubIP		Problem
	-SubIPNum: Int -hasPrimaryDescriptionOfSubIP: PrimarySubIP -hasPredicate: Predicate -hasSubject: Subject -hasAppliedDescriptionOfSubIP: AppliedSubIP	hasAppliedDescriptionOf SubIP	-hasObject: Object -hasPredicate: Predicate -description: String -hasSubject: Subject	hasSolution	-description: String -hasPositiveAppliedFeature: AppliedFeature -hasNegativeAppliedFeature: AppliedFeature -hasSolution: AppliedSubIP
	-hasObject: Object

  .4.

	New Problem	Characterise	New Case
				Retrieve
	Case Library			Retrieved Case	New Case
		Previous Case		Reuse
		General Knowledge	Solved Case
				Revise
	Learned Case	Retain	Tested Repaired Case	Suggested Solution
			Confirmed
			Solution

  4. Automatic Match between Specific Parameters and Generic Engineering Parameters 77

					Context				is a
									has case
									equals to
			Group 1	Group 2	Group 3		Group 4
									?
									NC
		Class 1	Class 2	Class 3	Class 4	Class 5	Class 6
									New Case: Instantaneous
									water heater
									-Context Number: Subclass 7
		Subclass 1	Subclass 2	Subclass 3	Subclass 4	Subclass 5	Subclass 6	Subclass 7	-SP->GEP: Size -> ?
						Case 5: Sheet music stand		Case 6: Measuring cup
						-Context Number: Subclass 5		-Context Number: Subclass 6
						-SP->GEP: Size -> Volume of		-SP->GEP: Size -> Volume of
	Case 1	Case 2	Case 3	Case 4	Case 5	stationary object	Case 6	moving object

Table 5 .

 5 1: The similar inventive standards for InventiveP rinciple 1, Inventive Principle 2 and InventiveP rinciple 36 class, if yes, return the corresponding ones; or else, select five inventive standards of A-class most similar with the given inventive principle based on the proposed method.

	Inventive Principles	Similar Inventive Standards A-class B-class
	InventiveP rinciple 1		IS59
	Inventive Principle 2	IS36, IS44, IS41, IS62	IS59
	InventiveP rinciple 36 IS29 IS64 IS65 IS66 IS67
	of B-

Table 5 .

 5 3: The SWRL rules and explanation for General Solution 1.

	Name	SWRL rules and explanation
	Rule 1	P roblem(?x) ∧ InventiveStandard(?y) ∧ chooses_IS(?x, ?y)∧
		has_keyid_of _type(?y, 1) ∧ Generic_P roblem_M odel(?z)∧
		correspondsT o_GP M (?x, ?z) ∧ Generic_Solution_M odel(?a)∧
		correspondsT o_GSM (?x, ?a) ∧ F ield(?b) ∧ Substance(?c)∧
		hasF ield_GP M (?z, ?b) ∧ hasSubstance2_GP M (?z, ?c)∧
		has_S1_N um_GP M (?z, 0) ∧ has_S2_N um_GP M (?z, 1)∧
		has_F _N um_GP M (?z, 1) → hasF ield_GSM (?a, ?b)∧
		hasSubstance1_GSM (?a, added_S1_x2)∧
		hasSubstance2_GSM (?a, ?c) ∧ has_S1_N um_GSM (?a, 1)∧
		has_S2_N um_GSM (?a, 1) ∧ has_F _N um_GSM (?a, 1)
		If the component Substance1 is missing, add one to complete the
		Su-Field Model.
	Rule 2
		hasSubstance2_GSM (?a, added_S2_y2)∧
		hasSubstance1_GSM (?a, ?c) ∧ has_S1_N um_GSM (?a, 1)∧
		has_S2_N um_GSM (?a, 1) ∧ has_F _N um_GSM (?a, 1)
		If the component Substance2 is missing, add one to complete the
		Su-Field Model.
	Rule 3	P roblem(?x) ∧ InventiveStandard(?y) ∧ chooses_IS(?x, ?y)∧
		has_keyid_of _type(?y, 1) ∧ Generic_P roblem_M odel(?z)∧
		correspondsT o_GP M (?x, ?z) ∧ Generic_Solution_M odel(?a)∧
		correspondsT o_GSM (?x, ?a) ∧ Substance(?c) ∧ Substance(?d)∧
		hasSubstance1_GP M (?z, ?c) ∧ hasSubstance2_GP M (?z, ?d)∧
		has_S1_N um_GP M (?z, 1) ∧ has_S2_N um_GP M (?z, 1)∧
		has_F _N um_GP M (?z, 0) → hasF ield_GSM (?a, added_F _z2)∧
		hasSubstance1_GSM (?a, ?c) ∧ hasSubstance2_GSM (?a, ?d)∧
		has_S1_N um_GSM (?a, 1) ∧ has_S2_N um_GSM (?a, 1)∧
		has_F _N um_GSM (?a, 1)
		If the component Field is missing, add one to complete the Su-
		Field Model.

P roblem(?x) ∧ InventiveStandard(?y) ∧ chooses_IS(?x, ?y)∧ has_keyid_of _type(?y, 1) ∧ Generic_P roblem_M odel(?z)∧ correspondsT o_GP M (?x, ?z) ∧ Generic_Solution_M odel(?a)∧ correspondsT o_GSM (?x, ?a) ∧ F ield(?b) ∧ Substance(?c)∧ hasF ield_GP M (?z, ?b) ∧ hasSubstance1_GP M (?z, ?c)∧ has_S1_N um_GP M (?z, 1) ∧ has_S2_N um_GP M (?z, 0)∧ has_F _N um_GP M (?z, 1) → hasF ield_GSM (?a, ?b)∧

Table 5 .

 5 4: Examples of Jess facts.

	Jess facts
	Jess Classes
	(deftemplate owl:Thing(slot name))
	(deftemplate Element extends owl:Thing)
	(deftemplate Substance extends Element)
	(deftemplate Field extends Element)
	Jess Individuals
	(assert(Substance(name Water)))
	(assert(Substance(name Liquid)))
	Jess Properties
	(assert(description Water "H

2 O")) (assert(description Liquid "A substance with a definite volume but no fixed shape")) (assert(includes Liquid Water))

Table 5 .

 5 5: The Jess rules for General Solution 1.

	Name	Jess rules
	Rule 1	(def rule Rule 1 (P roblem(name ?x))(InventiveStandard(name ?y))
		(chooses_IS ?x ?y)(has_keyid_of _type ?y 1)
		(Generic_P roblem_M odel(name ?z))(correspondsT o_GP M ?x ?z)
		(Generic_Solution_M odel(name ?a))(correspondsT o_GSM ?x ?a)
		(F ield(name ?b))(Substance(name ?c))(hasF ield_GP M ?z ?b)
		(hasSubstance2_GP M ?z ?c)(has_S1_N um_GP M ?z 0)
		(has_S2_N um_GP M ?z 1)(has_F _N um_GP M ?z 1)
		⇒ (assert(hasF ield_GSM ?a ?b))
		(assertOW LP roperty hasF ield_GSM ?a ?b)
		(assert(hasSubstance1_GSM ?a added_S1_x2))
		(assertOW LP roperty hasSubstance1_GSM ?a added_S1_x2)
		(assert(hasSubstance2_GSM ?a ?c))
		(assertOW LP roperty hasSubstance2_GSM ?a ?c)
		(assert(has_S1_N um_GSM ?a 1))

⇒ (assert(hasF ield_GSM ?a added_F _z2))

  6. Ontology-based Approach for Using the Physical-Chemical-Geometrical Effects 113 Table 6.4: Examples of Jess facts.

	Jess facts
	Jess Classes
	(deftemplate owl:Thing (slot name))
	(deftemplate Physical_Effect extends owl:Thing)
	(deftemplate Field_PE extends Physical_Effect)
	(deftemplate Add_FPE extends Field_PE)
	(deftemplate Modify_FPE extends Field_PE)
	Jess Individuals
	(assert (Modify_FPE (name Mosbauer_Absorption)))
	(assert (Field (name gammaRayField)))
	Jess Properties
	(assert (hasKeyword_PE Mosbauer_Absorption "absorb gamma ray"))
	(assert(hasInitialStateField Mosbauer_Absorption gammaRayField))

Table 6 .

 6 5: Examples of Jess rules.

	Name	Jess rules
	ISRule 2 (def rule ISRule2 (P roblem(name ?x))(InventiveStandard(name ?y))
		(chooses_IS ?x ?y)(has_keyid_of _type ?y 1)
		⇒ (assert(chooses_P E_T ype ?x 2))
		(assertOW LP roperty chooses_P E_T ype ?x 2))
	PERule 1	(def rule P ERule1 (P roblem(name ?x))
		(Generic_P roblem_M odel(name ?y))
		(Generic_Solution_M odel(name ?z))
		(correspondsT o_GP M ?x ?y)(correspondsT o_GSM ?x ?z)
		(chooses_P E_T ype ?x 0)(Substance(name ?a))
		(changesOnOrF or_Element ?z ?a)(Add_SP E(name ?b))
		(hasIncrementalSubstance ?b ?a)
		⇒ (assert(chooses_P E ?x ?b))
		(assertOW LP roperty chooses_P E_T ype ?x ?b))
	PERule 2	(def rule P ERule2 (P roblem(name ?x))
		(Generic_P roblem_M odel(name ?y))
		(Generic_Solution_M odel(name ?z))
		(correspondsT o_GP M ?x ?y)(correspondsT o_GSM ?x ?z)

ISRule 1 (def rule ISRule1 (P roblem(name ?x))(InventiveStandard(name ?y))

(chooses_IS ?x ?y)(has_keyid_of _type ?y 1)

⇒ (assert(chooses_P E_T ype ?x 0)) (assertOW LP roperty chooses_P E_T ype ?x 0))

Table 7 .

 7 1: The environment and tools for the development of IngeniousTRIZ.

	Name	Version	Library
	Java Platform(Java SE) Version 1.7.0_02
	NetBeans IDE	Version 7.2
	MySQL	Version 5.1.22	JDBC driver: mysql-connector-java-5.1.22-bin.jar
	WordNet	Version 2.0	jwnl14-rc2.zip
			OWL-API:
			owlapi_bin.jar
	Protégé	Version 3.4.3	owlapi_src.jar
			Protege-OWL API:
			edu.standard.smi.protegex.owl\*.jar
	Jess	Version 7.1p2	jess.jar

Table 7 .

 7 2: The tables in the database "IngeniousTRIZ".

	Name	Description
	allinformation	Store all the information for each use, such as the specific
		parameters for the problem to be solved.
	contradictionmatrix Store the information for the contradiction matrix.
	ip	Store the detailed information for 40 inventive principles.
	is	Store the detailed information for 76 inventive standards.
	isbehavior	Store the types of modification for 76 inventive standard.
	sipis	Store the matching results between inventive principles and
		inventive standards.
	sipsm	Store the matching results between inventive principles and
		separation methods.
	sm	Store the detailed information for 11 separation methods.
	transformation	Store the detailed modifications of 76 inventive standards.

Table 8 .

 8 1: Four types of the performance of IngeniousTRIZ

	Value	Type
	0.8 -1.0 Excellent
	0.5 -0.8	Good
	0.3 -0.5 Acceptable
	0.0 -0.3	Poor

Semantic Similarity Between Inventive Princi- ples and Separation Methods

	188 F. THE SEMANTIC SIMILARITY AMONG THE TRIZ KNOWLEDGE SOURCES
	Inventive Principles InventiveP rinciple 1 InventiveP rinciple 2 InventiveP rinciple 3 InventiveP rinciple 4 Inventive Principles InventiveP rinciple 28 InventiveP rinciple 29 InventiveP rinciple 30 F.2 Inventive Principles InventiveP rinciple 31	Similar Inventive Standards A-class B-class IS59 IS44, IS41, IS62 IS59, IS36 IS19, IS55 IS19 Similar Separation Methods SM8, SM7 SM6, SM7 SM6, SM7 Similar Separation Methods SM11, SM8
	InventiveP rinciple 5 InventiveP rinciple 6 InventiveP rinciple 1 InventiveP rinciple 32 InventiveP rinciple 7 IS44, IS11, IS40, IS14, IS28 IS51, IS67, IS66 SM7, SM6 SM8, SM9 InventiveP rinciple 2 SM7, SM1 InventiveP rinciple 33 SM2, SM6	IS35 IS63, IS59
	InventiveP rinciple 8 InventiveP rinciple 3 InventiveP rinciple 34	IS11, IS44, IS21	SM7, SM6 SM7, SM6	IS67, IS63
	InventiveP rinciple 9 InventiveP rinciple 4 InventiveP rinciple 35	IS43, IS44, IS12	SM1, SM6 SM6, SM9	IS20, IS63
	InventiveP rinciple 10 InventiveP rinciple 11 InventiveP rinciple 5 InventiveP rinciple 36 InventiveP rinciple 12 InventiveP rinciple 6 InventiveP rinciple 37	IS1, IS15 IS16, IS11, IS28 IS16, IS21, IS1	IS63, IS66, IS67 SM7, SM2 SM9, SM7 IS36, IS57 IS20, IS71 SM7, SM8 SM9, SM7
	InventiveP rinciple 13 InventiveP rinciple 7 InventiveP rinciple 38	IS7, IS44	SM6, SM1 SM8, SM6	IS53, IS6, IS55
	InventiveP rinciple 14 InventiveP rinciple 8 InventiveP rinciple 39	IS43, IS44, IS14, IS1	SM2, SM9 SM7, SM9	IS59
	InventiveP rinciple 15 InventiveP rinciple 16 InventiveP rinciple 9 InventiveP rinciple 17 InventiveP rinciple 10	IS28, IS41, IS4	SM7, SM9 SM9, SM6	IS30 IS6, IS50 IS59, IS67
	InventiveP rinciple 18 InventiveP rinciple 11		SM2, SM6	IS20, IS32
	InventiveP rinciple 19 InventiveP rinciple 12		IS18, IS32, IS37 SM7, SM8
	InventiveP rinciple 20 InventiveP rinciple 21 InventiveP rinciple 13 InventiveP rinciple 22 InventiveP rinciple 14	IS1, IS43, IS44 IS62, IS43, IS44	SM7, SM6 SM7, SM6	IS22 IS20, IS65 IS61, IS63
	InventiveP rinciple 23 InventiveP rinciple 15		SM8, SM7	IS30, IS68
	InventiveP rinciple 24 InventiveP rinciple 16	IS43, IS1, IS44	SM8, SM9	IS59, IS70
	InventiveP rinciple 25 InventiveP rinciple 17	IS44	IS63, IS65, IS20, IS67 SM6, SM1
	InventiveP rinciple 26 InventiveP rinciple 27 InventiveP rinciple 18	IS1, IS44, IS43	SM7, SM2	IS36, IS57 IS36, IS63
	InventiveP rinciple 28 InventiveP rinciple 19		SM2, SM6	IS48, IS53
	InventiveP rinciple 29 InventiveP rinciple 20		SM7, SM6	IS52, IS61
	InventiveP rinciple 30 InventiveP rinciple 21	IS28, IS11, IS51	SM9, SM6	IS53, IS67
	InventiveP rinciple 31 InventiveP rinciple 32 InventiveP rinciple 22 InventiveP rinciple 33 InventiveP rinciple 23	IS62, IS43, IS44 IS11, IS28	IS36, IS57, IS67, IS63, IS61 SM6, SM7 IS63, IS67 IS36, IS57, IS59 SM8, SM7
	InventiveP rinciple 34 InventiveP rinciple 24		SM7, SM6	IS60
	InventiveP rinciple 35 InventiveP rinciple 25		SM8, SM7	IS34, IS63
	• Use of double Su-Fields InventiveP rinciple 39 • Segmentation (including porosity increase) IS44, IS62 InventiveP rinciple 36 IS29 IS64 IS65 IS66 IS67 InventiveP rinciple 37 IS28, IS43 InventiveP rinciple 26 SM9, SM7 IS59, IS63, IS67 IS63, IS67, IS65 InventiveP rinciple 38 IS54, IS69, IS70, IS71 InventiveP rinciple 27 SM2, SM8
	InventiveP rinciple 40	IS3	IS36, IS57, IS63, IS61

  InventiveStandard(?y) ∧ chooses_IS(?x, ?y)∧ has_keyid_of _type(?y, 3) → chooses_P E_T ype(?x, 1)If the chosen Inventive Standard belongs to the type 3, the PE of type 1 will be chosen. InventiveStandard(?y) ∧ chooses_IS(?x, ?y)∧ has_keyid_of _type(?y, 4) → chooses_P E_T ype(?x, 3)If the chosen Inventive Standard belongs to the type 4, the PE of type 3 will be chosen. If the chosen Inventive Standard belongs to the type 7, the PE of type 0 will be chosen. InventiveStandard(?y) ∧ chooses_IS(?x, ?y)∧ has_keyid_of _type(?y, 116) → chooses_P E_T ype(?x, 0)If the chosen Inventive Standard belongs to the type 116, the PE of type 0 will be chosen.

						HEURISTIC PHYSICAL EFFECTS
	Name Rule 3	impact field IS rules and explanations pressure field P roblem(?x) ∧ InventiveStandard(?y) ∧ chooses_IS(?x, ?y)∧ has_keyid_of _type(?y, 2) → chooses_P E_T ype(?x, 1) vibrational field If the chosen Inventive Standard belongs to the type 2,
						the PE of type 1 will be chosen.
	Rule 4 P roblem(?x) ∧ Rule 5 P roblem(?x) ∧ Rule 9 P roblem(?x) ∧	infrared ray field	adhesive field	gamma ray field	linking covalent field linking physical field microwave field centrifugal field	linking hydrophobic field linking metal field ultraviolet ray field displacement field	linking ionic field optical field x-ray field

Rule 6 P roblem(?x) ∧ InventiveStandard(?y) ∧ chooses_IS(?x, ?y)∧ has_keyid_of _type(?y, 5) → chooses_P E_T ype(?x, 2) If the chosen Inventive Standard belongs to the type 5, the PE of type 2 will be chosen. Rule 7 P roblem(?x) ∧ InventiveStandard(?y) ∧ chooses_IS(?x, ?y)∧ has_keyid_of _type(?y, 6) → chooses_P E_T ype(?x, 2) If the chosen Inventive Standard belongs to the type 6, the PE of type 2 will be chosen. Rule 8 P roblem(?x) ∧ InventiveStandard(?y) ∧ chooses_IS(?x, ?y)∧ has_keyid_of _type(?y, 7) → chooses_P E_T ype(?x, 0)

  1. Les Principes Inventifs: La TRIZ a formulé trente-neuf Paramètres Génériques d'Ingénierie, comme par exemple "le poids d'un objet mobile" ou "la vitesse". Un nouveau problème peut être résolu sans compromis en utilisant un Principe Inventif après que le problème ait été formulé comme une contradiction technique 2 . Une telle contradiction met en opposition deux Paramètres Génériques: "un paramètre générique que l'on souhaite voir s'améliorer sans qu'un autre paramètre générique ne se détériore". Le Principe Inventif fournit donc un guide indiquant de quelle manière le problème pourrait être résolu sans avoir l'effet négatif identifié. 2. Les Standards Inventifs: Les Standards Inventifs sont construits sous la forme de recommandations qui généralement peuvent être assimilées à des règles "Si <Condition1> et <Condition2> puis <Recommandation>". Ces deux conditions permettent de reconnaître la typologie du problème lié au standard. Pour tout problème abstraite, il existe un certain nombre de recommandations permettant la construction de sa solution abstraite correspondante. Les Standards Inventifs sont formulés de façon très abstraite, et donc leur utilisation pratique est assez difficile. 3. Les Méthodes de Séparation: Pour résoudre des problèmes contenant une contradiction physique 3 , 2 Une contradiction technique se pose quand il est nécessaire d'améliorer certaines caractéristiques du prototype existant, mais toutes les solutions connues dans le domaine ne produit pas le résultat souhaité ou leur utilisation entraînerait un effet négatif. 3 Une contradiction physique indique qu'une partie d'un onze Méthodes de Séparation sont utilisées pour l'éliminer. Les Méthodes de Séparation sont très génériques, et sont donc très difficiles à transposer dans la réalité sans connaissances spécifiques. Comme indiqué précédemment, toutes ces sources de connaissances de la TRIZ sont construites indépendamment du domaine d'application spécifique, et leurs niveaux d'abstraction sont très différents, ce qui entraîne une difficulté supplémentaire dans leurs interprétations. Afin de faciliter le processus de résolution de problèmes inventifs, un "Système Intelligent de Gestion de Connaissances" est développé dans cette thèse. D'une part, en intégrant les ontologies des bases de connaissance de la TRIZ, le gestionnaire propose aux utilisateurs de sources de connaissance pertinentes pour le modèle qu'ils construisent, et d'autre part, le

	gestionnaire	a	la	capacité	de	remplir
	"automatiquement" les modèles associés aux autres bases de connaissance.

Etape 1-> Etape 4: L'usage des Principes Inventifs.

  4. En prenant en compte que toutes les sources de connaissance de la TRIZ sont décrites comme des textes courts, les liens manquants entre les sources de connaissance sont définis à l'aide d'un calcul de similarité sémantique entre textes courts que nous proposons[1][2]. Au même temps, nous proposons l'utilisation du raisonnement sur les ontologies avec Protégé et Jess (Java Expert System Shell) pour générer des solutions abstraites heuristiques dynamiques[6][7]. Une nouvelle méthode de représentation des effets physiques est proposée. Un effet physique est représenté par un couple d'états du système avant et après son utilisation. Nous avons formalisé les informations quant à l'usage des effets physiques en OWL (Web Ontology Language). Les informations relatives aux contraintes liées à l'usage de chaque type d'effets physiques ont été formalisées en SWRL (Semantic Web Rule Language). Enfin, comme illustré dans la Fig.5, Jess est utilisé pour la recherche heuristique d'effets physiques[4][5]. Figure 5. Le processus de raisonnement. PrototypeAfin de vérifier les approches proposées sur des cas réels de conception inventive, le prototype IngeniousTRIZ a été développé à l'aide de la plateforme Java 1.7.02, de WordNet 2.0, de Protégé 3.4.3 et de Jess 7.1p2 (sur Windows OS).Comme illustré dans la Fig.6, le processus d'utilisation de ce prototype est divisé en trois phases: A. Le diagramme de séquence pour Etape 1-> Etape 4 est illustré dans la Fig. 7. l'information obtenue dans Etape 1, la contradiction est affichée sous forme d'un tableau.  Etape 3: SP->GEP Afin de chercher les Principes Inventifs à l'aide de la matrice de contradiction, deux Paramètres Spécifiques (SP) doivent correspondre aux trente-neuf Paramètres Génériques d'Ingénierie (GEP). L'utilisateur peut les choisir manuellement ou avec l'aide de la recherche sémantique.  Etape 4: Principes Inventifs A l'aide de la matrice de contradiction, une série de Principes Inventifs est obtenue.  Concept Solution Basé sur le Principe Inventif obtenu, l'utilisateur peut arrêter ce processus, concevoir une concept solution, et stocker l'information obtenue. B.

	 Etape 1: Contradiction Description Afin de résoudre les contradictions dans un problème spécifique, l'utilisateur est guidé à entrer des
	paramètres, par exemple, deux paramètres spécifiques.  Etape 2: Contradiction Formalisation Basée Figure 6. L'organigramme du prototype: IngeniousTRIZ.

Figure 4. L'architecture de la méthode.  L'utilisation d'effets physiques: sur

Etape 4-> Etape 8: La recherche heuristique de solutions abstraites.

  Le diagramme de séquence pour Etape 4-> Etape 8 est illustré dans la Fig. 8. Selon le problème spécifique, l'utilisateur doit choisir un Standard Inventif, qui est le plus similaire avec le Principe Inventif obtenu. L'utilisateur peut les sélectionner manuellement ou avec l'aide de la recherche sémantique. 

 Etape 5: Standards Inventifs Sémantique

Etape 6: Su-Field Analyse-1

  Dans cette étape, l'utilisateur doit fournir les éléments (substances et champ) du modèle de problème. 

Etape 7: Su-Field Analyse-2

  Selon le Standard Inventif choisi, l'utilisateur doit sélectionner un type de modification correspondant. Basé sur l'information obtenue ci-dessus, le raisonnement sur les ontologies est utilisé pour générer des solutions abstraites heuristiques dynamiques.  Concept Solution Heuristique Basé sur les solutions abstraites heuristiques obtenu, l'utilisateur peut arrêter ce processus, concevoir une concept solution heuristique, et stocker l'information obtenue. Le diagramme de séquence pour Etape 8-> Etape 11 est illustré dans la Fig. 9. Basé sur le Standard Inventif choisi, plusieurs types d'effets physiques sont proposés, et l'utilisateur doit choisir l'un d'eux. 

 Etape 8: Su-Field Analyse-3

C. Etape 8-> Etape 11: L'utilisation heuristique d'effets physiques pour obtenir une ou des solution(s) conceptuelle(s).

 Etape 9: Effets Physiques Heuristique-1

Etape 10: Effets Physiques Heuristique-2

  L'utilisateur doit fournir le niveau de granularité de l'élément qui est modifié ou ajouté dans le solution modèle. 

Etape 11: Effets Physiques Heuristique-3

  Basé sur l'information obtenue ci-dessus, le raisonnement sur les ontologies est utilisé pour générer des effets physiques heuristiques dynamiques.  Concept Solution Heuristique Basé sur les solutions abstraites heuristiques et les effets physiques heuristiques, l'utilisateur peut arrêter ce processus, concevoir une concept solution heuristique, et stocker l'information obtenue.

  TRIZ (Acronyme russe pour Théorie de Résolution des Problèmes Inventifs) est une méthode systématique pour la résolution de problèmes inventifs dans différents domaines. Selon TRIZ, la résolution de problèmes inventifs consiste en la construction du modèle et l'utilisation des sources de connaissance de la TRIZ. Plusieurs modèles et sources de connaissances permettent la résolution de problèmes inventifs de types différents, comme les quarante Principes Inventifs pour l'élimination des contradictions techniques. Toutes ces sources se situent à des niveaux d'abstractions relativement élevés et sont, donc, indépendantes d'un domaine particulier, ce qui nécessite des connaissances approfondies des domaines d'ingénierie différents. Cette étude vise à faciliter et automatiser le processus de résolution de problèmes inventifs basé sur la similarité sémantique et techniques de l'ontologie. Tout d'abord, les sources de connaissance de la TRIZ sont formalisés sur des ontologies afin que l'inférence heuristique puisse être exécutée à la recherche des solutions spécifiques. Ensuite, les méthodes de calcul de similarité sémantique sont proposés pour rechercher et définir les liens manquants entre les sources de connaissance de la TRIZ.Afin de faciliter le processus de résolution de problèmes inventifs, un "gestionnaire intelligent" est développé dans cette thèse. D'une part, selon les ontologies des sources de connaissance de la TRIZ, le gestionnaire propose aux utilisateurs des sources de connaissance pertinentes du modèle qu'ils construisent, et d'autre part, le gestionnaire a la capacité de remplir "automatiquement" les modèles des autres sources de connaissance.Les TRIZ utilisateurs commencent à résoudre les problèmes inventifs avec la source de connaissance de leur choix afin d'obtenir une solution abstraite. Selon les éléments sélectionnés de la première source de connaissance, les éléments similaires d'autres sources de connaissance sont obtenus sur la base de la similarité sémantique calculée à l'avance. Avec l'aide de ces éléments similaires et les pointeurs heuristiques vers les effets physiques, d'autres solutions spécifiques sont obtenues par le raisonnement de l'ontologie.Enfin, un prototype de logiciel a été développé basé sur la similarité sémantique et les ontologies interviennent en support du processus de génération automatique de solutions conceptuelles.En termes de perspectives, plusieurs voies peuvent être envisagées. Le premier facteur problématique est WordNet, qui est utilisé comme le dictionnaire sémantique dans le calcul de similarité sémantique de textes courts. Les méthodes proposées ne seront pas toujours efficaces en raison des limites de WordNet. L'interaction avec les utilisateurs permettra de résoudre ce problème. Par conséquent, nous avons La deuxième perspective est qu'un certain nombre d'information est perdue pendant le processus de la recherche des solutions abstraites heuristiques. Les TRIZ utilisateurs commencent à résoudre les problèmes inventifs avec quarante Principes Inventifs afin d'obtenir une solution abstraite. Selon les Principes Inventifs sélectionnés, les Standards Inventifs similaires sont obtenus sur la base de la similarité sémantique calculée à l'avance. Avec l'aide de ces Standards Inventifs similaires, les solutions abstraites heuristiques sont obtenues basé sur l'inférence de l'ontologies. Cependant, une grande quantité d'information est perdue pendant la transformation de la résolution avec contradictions techniques à avec Su-Field analyse. Une manière de résoudre ce problème est l'analyse avec onze Méthodes de Séparation et contradictions physiques pour fournir l'information complémentaire. Pour les Principes Inventifs choisis, ce ne sont pas seulement les Standards Inventifs similaires qui sont utilisés pour générer les solutions abstraites heuristiques, mais aussi les Méthodes de

	Séparation l'information de niveau physique. similaires, utilisées	pour	fournir
	l'intention	de	fournir	une	plate-forme	de
	communication avec les utilisateurs grâce à l'élaboration de SWRL règles.

Please note although these generalized solutions are discussed in the context of a Su-Field model with harmful impact, they are applicable to Su-Field models with other types of problems, for example, excessive and insufficient interactions between substances.

http://function.creax.com/.

http://inventionmachine.com/products-and-services/innovation-software/.

For more detailed information, see the OWL Web Ontology Language Overview: http://www.w3.org/TR/owl-features.

http://www.ruleml.org.

The abstract syntax of SWRL rule axioms, in a version of extended BNF notation, is given in http://www.w3.org/Submission/2004/SUBM-SWRL-20040521/#2.

http://www.w3.org/TR/owl-ref/#DataRange.

Unsafe rules allow variables in the head that do not occur anywhere in the body. If this is allowed, the definition of bindings for variables cannot be defined correctly in this context. Also, termination of reasoning cannot be guaranteed as all the individuals in the KB could be used to bind the variable in the head. E.g., the rule Child(?x) ⇒ isInnocent(?y) is unsafe.

http://owl.man.ac.uk/hoolet.

http://www.w3.org/2003/08/surnia/.

http://www.w3.org/2005/rules/wg/wiki/Horn_Logic.

http://jena.apache.org/.

http://protege.cim3.net/cgi-bin/wiki.pl?SWRLJessTab.

http://protege.cim3.net/cgi-bin/wiki.pl?SWRLRuleEngineAPI.

Due to the characteristics of TRIZ knowledge, described by short texts in natural language rather than ontology language, it is necessary to firstly formalize the corresponding TRIZ knowledge sources based on ontologies, and then facilitate their use based on ontology reasoning.As an emerging technology, ontology reasoning has not been widely used in the domain of inventive design. In order to support and simulate the process of Su-Field analysis, Bultey et al firstly established several ontologies to formalize the concepts and relations in Su-Field analysis, and then built an ideal TRIZ reasoning environment based on ontology inference[START_REF] Bultey | A substance-field ontology to support the triz thinking approach[END_REF]. However, this research focuses on the Su-Field analysis and the use of inventive standards, without considering other TRIZ knowledge sources and their relations.Accordingly, this thesis firstly formalizes the TRIZ knowledge sources, and then develops an intelligent management system based on ontology reasoning. Indeed, during the processing of a new case, experts are brought to work with various models at different levels of abstraction. The knowledge management system should suggest the experts the use of the relevant knowledge sources, consistent with the level of abstraction of the model they are building. It will also be able to complete "automatically" the rest of the models, by exploiting the semantic links obtained among the different knowledge sources.

https://framenet.icsi.berkeley.edu.

Although the hierarchy has numerous levels, we have chosen here, for the sake of simplicity, to represent only three of them.

Sim: 0.68 Sim: 0.84 Sim: 0.33 Sim: 0.51 Sim: 0.73 Sim: 0.75 Sim: 0.78 Sim: 0.77 Sim: The value of similarity (The larger the calculated similarity, the more similar the two short texts). Sim: 0.15 Sim: 0.05

http://www.triz.co.uk/cp12.php.

http://protege.stanford.edu/plugins/owl/api/.

http://function.creax.com/.

http://inventionmachine.com/products-and-services/innovation-software/.

IDM (Inventive Design Methodology)[1, 2] is an extension to TRIZ, that is intended to process problems with hundreds of parameters, contradictions and problems not necessarily linked to inventive product design (for example, in software engineering or in the organization field).

http:// www.time-to-innovate.com.

http://docs.oracle.com/javase/7/docs/api/. http://www.oracle.com/technetwork/java/javame/index.html. http://www.oracle.com/technetwork/java/javase/overview/index.html. http://www.oracle.com/technetwork/java/javaee/overview/index.html. http://www.oracle.com/technetwork/java/javafx/overview/index.html.

https://netbeans.org/.

http://dev.mysql.com/.

http://www.linuxjournal.com/article/3609.

http://dev.mysql.com/downloads/connector/j/3.1.html.

http://wordnet.princeton.edu/.

http://www.stanford.edu/class/archive/cs/cs276a/cs276a.1032/projects/docs/jwnl/javadoc/.

http://protege.stanford.edu/.

http://protege.stanford.edu/plugins/owl/download.html.

http://protege.stanford.edu/overview/protege-owl.html.

http://protegewiki.stanford.edu/wiki/OntoViz. http://protegewiki.stanford.edu/wiki/OWLViz. http://protege.cim3.net/cgi-bin/wiki.pl?SWRLTab.

http://protegewiki.stanford.edu/wiki/ProtegeOWL_API_Programmers_Guide.

http://owlapi.sourceforge.net/index.html.

SWRL Rule Engine API is packaged with Protege-OWL API before Protégé 3.5 (included).

http://herzberg.ca.sandia.gov/.

The process of defining the technical contradiction for a specific case is introduced in the website: http://www.time-to-innovate.com/steps_matrix.

In this application,

physical effects are used to test the related functions of the ontology inference.

Up to now, only the proposed methods in Chapter 3 and 4 have been integrated in the research version of STEPS.

A. Bultey, F. D. B. de Beuvron, and F. Rousselot, "A substance-field ontology to support the TRIZ thinking approach," International Journal of Computer Applications in Technology, vol.30, pp. 113-124,

Les contradictions peuvent être techniques ou physiques.

Acknowledgements

This dissertation was made possible by the help of many individuals during the last three years and a half in France. Foremost, I would like to thank China Scholarship Council (CSC) for the financial support during my Ph.D. study and research in the Design Engineering Laboratory (LGeCO) at the National Institute of Applied Science (INSA) of Strasbourg, and the Engineering Science,

Chapter 7

The Prototype: IngeniousTRIZ From Chapter 3 to Chapter 6, we have introduced the automatic ontology-based mechanism for solving inventive problems in detail.

Chapter 3 introduces methods of calculating semantic similarity, which are the foundations of the research presented in Chapters 4 and 5. In order to facilitate the use of the contradiction matrix and inventive principles, Chapter 4 presents the automatic match between specific parameters and generic engineering parameters based on semantic similarity and case-based reasoning. In Chapter 5, the TRIZ knowledge sources are firstly formalized based on ontology, then the missing links among them are defined through the calculation of semantic similarity, and finally based on the TRIZ knowledge sources ontologies and their links, the heuristic abstract solutions are obtained through the execution of ontology inference. Chapter 6 deals with the use of physical effects to interpret the Solution Model in real life. In order to verify the proposed approaches with the users' cases, a prototype called IngeniousTRIZ, which combines the function of the approaches stated above, is developed. This chapter introduces this prototype in terms of the following three parts: state of the art, the development, and the detailed process of using this prototype to solve a specific problem.

State of the Art

Over the last two decades, many techniques in computer science, especially in Artificial Intelligence, are brought in the field of inventive design to facilitate the process of using the surface of the diving fin is big, it is better for the kicking efficiency but worse for the ease of use, and vice versa.

The Contradiction Matrix

The "Y-axis" of the contradiction matrix stands for the parameters to be improved and the "X-axis" shows the "undesired results", that is, the parameters to be deteriorated (http : //www.inventive -design.net).

Appendix C The 40 Inventive Principles

The 40 Inventive Principles (IP) are used with the contradiction matrix to solve technical contradictions. This list with the so-called "sub-principles" that intend to help clarify the meaning of the principles was taken from the TRIZ Journal (http : //www.inventivedesign.net).

• Inventive Principle 1 : Segmentation (a) Divide an object into independent parts. (b) Make an object easy to disassemble. (c) Increase the degree of fragmentation or segmentation.

• Inventive Principle 2 : Taking out (a) Separate an interfering part or property from an object, or single out the only necessary part (or property) of an object.

• Inventive Principle 3 : Local quality (a) Change an object's structure from uniform to non-uniform, change an external environment (or external influence) from uniform to non-uniform.

(b) Make each part of an object function in conditions most suitable for its operation.

C. THE 40 INVENTIVE PRINCIPLES

(c) If an object (or process) is rigid or inflexible, make it movable or adaptive.

• Inventive Principle 16 : Partial or excessive actions (a) If 100 percent of an object is hard to achieve using a given solution method then, by using 'slightly less' or 'slightly more' of the same method, the problem may be considerably easier to solve.

• Inventive Principle 17 : Another dimension (a) To move an object in two-or three-dimensional space.

(b) Use a multi-story arrangement of objects instead of a single-story arrangement.

(c) Tilt or re-orient the object, lay it on its side.

(d) Use 'another side' of a given area.

• This class is used for solving problems by developing solutions at different levels in the system (super-system or sub-system). The major recommendations from this class are how to improve systems by combining elements or combining with other systems. Class 3 contains 2 sub-classes containing 6 IS:

Sub-class 3.1 Simplicity-complexity-simplicity (mono-bi-poly) and increasing flexibility and dynamisation (Transition to super-system and to bi and poly systems; use no links, rigid links, flexible links, "field" links) (5 IS) Sub-class 3.2 Transition to micro-level (examine the sub-system, use smart substances) (1 IS)

Class 4: Solutions for Detection and Measurement

This class is used for solving measuring or detection problems in engineering systems. These solutions have many distinguishing features, especially the use of indirect methods and the use of copies.

Detection and measurement are typically for control. Detection is binary (something either happens or doesn't happen) and measurement has some level of quantification and precision. For example, a length measurement might be 2.15 m ± 0.01 m. Often the most innovative solution is automatic control which removes formal detection/measurement by taking advantage of physical, chemical or geometrical effects.

The major recommendations of this class are:

• Try to change the system so that there is no need to measure/detect.

• Measure a copy.

• Introduce a substance that generates a field (introduce a mark internally or externally). Class 5 solutions help when simplifying or trimming the system to remove components or to reduce the strength of the relevant interaction. The first four classes of Standard Solutions above often lead to solutions which increase complexity because something if often added to the system to solve the problem. This fifth class shows how to get something extra through simplification but without introducing anything new.

The useful recommendations from this class are:

• Instead of a substance, introduce a field.

• Instead of a substance, introduce a void.

• Introduce a substance for a limited time.

• Introduce a little bit of a substance, but in a very concentrated way.

• Use phase changes.

• Get the substance or environment to change themselves to solve the problem.

• Use segmentation.

Appendix E The 11 Separation Methods

The 11 separation methods (SM) are used to solve physical contradictions. This list was taken from the TRIZ Journal (http : //www.inventive -design.net).

Separation in Space

• SM1. Separation of conflicting properties in space.

Separation in Time

• SM2. Separation of conflicting properties in time.

Separation by system transition

• SM3. Combination of homogeneous or heterogeneous systems into a super-system.

• SM4. Transition from a system to an anti-system, or combination of system with anti-system.

• SM5. The entire system has a property X while its parts have a property opposite to X (anti-X).

• SM6. System transition 2: transition to system that works on the micro-level.

Separation by phase transition

• SM7. Substitution of the phase state of a system's part or external environment.

E. THE 11 SEPARATION METHODS

• SM8. Dual phase state of a system part (using substances capable of converting from one phase to another according to operating conditions).

• SM9. Using of phenomena associated with phase transitions.

• SM10. Substitution of a mono-phase substance with a dual-phase state.

Separation by physical-chemical transition

• SM11. Substance appearance-disappearance as a result of decomposition-combination, ionization-recombination.

Appendix F

The Semantic Similarity Among the

TRIZ Knowledge Sources

The semantic similarity among the TRIZ knowledge sources is divided into two kinds: the similarity between inventive principles and inventive standards, and the similarity between inventive principles and separation methods.

F.1 Semantic Similarity Between Inventive Principles and Inventive Standards

There are two types of similar inventive standards for 40 inventive principles, that is, A-class inventive standards: the inventive standards generalized into seven general solutions and B-class inventive standards: the inventive standards identified as the existing inventive principles.

Appendix G

The SWRL Rules and Their

Explanations for Searching Heuristic

Abstract Solutions

The 40 SWRL rules for searching heuristic abstract solutions are divided into two kinds: the first 9 rules describing the modification of A-class inventive standards and the other 31 rules for the modification of B-class inventive standards.

Name SWRL rules and explanations

Rule P roblem(?x) ∧ InventiveStandard(?y) ∧ chooses_IS(?x, ?y)∧ has_keyid_of _type(?y, 2) ∧ Generic_P roblem_M odel(?z)∧ correspondsT o_GP M (?x, ?z) ∧ Generic_Solution_M odel(?a)∧ correspondsT o_GSM (?x, ?a)

has_F _N um_GSM (?a, 1) Modify Substance2 to eliminate or reduce harmful impact.

Rule

P roblem(

hasSubstance2_GP M (?z, ?d) ∧ has_S1_N um_GP M (?z, 1)∧ has_S2_N um_GP M (?z, 1) ∧ has_F _N um_GP M (?z, 1) → hasF ield_GSM (?a, ?b) ∧ hasSubstance2_GSM (?a, ?d)∧ hasSubstance1_GSM (?a, S1_with_modif ication_x1)∧ has_S1_N um_GSM (?a, 1) ∧ has_S2_N um_GSM (?a, 1)∧ has_F _N um_GSM (?a, 1) Modify Substance1 to be less sensitive to harmful impact. Rule P roblem(?x) ∧ InventiveStandard(?y) ∧ chooses_IS(?x, ?y)∧ has_keyid_of _type(?y, 4) ∧ Generic_P roblem_M odel(?z)∧ correspondsT o_GP M (?x, ?z) ∧ Generic_Solution_M odel(?a)∧ correspondsT o_GSM (?x, ?a)

hasF ield_GSM (?a, F _with_modif ication_z1)∧ has_S1_N um_GSM (?a, 1) ∧ has_S2_N um_GSM (?a, 1)∧ has_F _N um_GSM (?a, 1) Change Field to reduce or eliminate harmful impact. HEURISTIC ABSTRACT SOLUTIONS Name SWRL rules and explanations Rule P roblem(?x) ∧ InventiveStandard(?y) ∧ chooses_IS(?x, ?y)∧ has_keyid_of _type(?y, 5) ∧ Generic_P roblem_M odel(?z)∧ correspondsT o_GP M (?x, ?z) ∧ Generic_Solution_M odel(?a)∧ correspondsT o_GSM (?x, ?a)

hasF ield_GSM (?a, added_F _z2)∧ has_S1_N um_GSM (?a, 1) ∧ has_S2_N um_GSM (?a, 1)∧ has_F _N um_GSM (?a, 2) A counteractive Field is added to eliminate, neutralize or isolate harmful impact.

Rule

hasSubstance2_GP M (?z, ?d) ∧ has_S1_N um_GP M (?z, 1)∧ has_S2_N um_GP M (?z, 1) ∧ has_F _N um_GP M (?z, 1) → hasSubstance1_GSM (?a, ?c) ∧ hasSubstance2_GSM (?a, ?d)∧ hasF ield_GSM (?a, added_F _z2)∧ has_S1_N um_GSM (?a, 1) ∧ has_S2_N um_GSM (?a, 1)∧ has_F _N um_GSM (?a, 2) A positive Field is introduced to increase useful effect. Rule P roblem(?x) ∧ InventiveStandard(?y) ∧ chooses_IS(?x, ?y)∧ has_keyid_of _type(?y, 7) ∧ Generic_P roblem_M odel(?z)∧ correspondsT o_GP M (?x, ?z) ∧ Generic_Solution_M odel(?a)∧ correspondsT o_GSM (?x, ?a) has_F _N um_GSM (?a, 1) Use the copy of Substance to be measured. Rule P roblem(?x) ∧ InventiveStandard(?y) ∧ chooses_IS(?x, ?y)∧ has_keyid_of _type(?y, 413) ∧ Generic_P roblem_M odel(?z)∧ correspondsT o_GP M (?x, ?z) ∧ Generic_Solution_M odel(?a)∧ correspondsT o_GSM (?x, ?a) ∧ F ield(?b) ∧ Substance(?c)∧ Substance(?d) ∧ hasF ield_GP M (?z, ?b) ∧ hasSubstance1_GP M (?z, ?c)∧ hasSubstance2_GP M (?z, ?d) ∧ has_S1_N um_GP M (?z, 1)∧ has_S2_N um_GP M (?z, 1) ∧ has_F _N um_GP M (?z, 1) → hasSubstance1_GSM (?a, ?c) ∧ hasSubstance2_GSM (?a, ?d)∧ hasF ield_GSM (?a, F _with_def inite_spatial -temporal_structure_z4)∧ has_S1_N um_GSM (?a, 1) ∧ has_S2_N um_GSM (?a, 1)∧ has_F _N um_GSM (?a, 1) For measurement problem, the sequentially detection of changes is used.

Name SWRL rules and explanations

Rule P roblem(?x) ∧ InventiveStandard(?y) ∧ chooses_IS(?x, ?y)∧ has_keyid_of _type(?y, 444) ∧ Generic_P roblem_M odel(?z)∧ correspondsT o_GP M (?x, ?z) ∧ Generic_Solution_M odel(?a)∧ correspondsT o_GSM (?x, ?a) ∧ F ield(?b) ∧ Substance(?c)∧ Substance(?d) ∧ hasF ield_GP M (?z, ?b) ∧ hasSubstance1_GP M (?z, ?c)∧ hasSubstance2_GP M (?z, ?d) ∧ has_S1_N um_GP M (?z, 1)∧ has_S2_N um_GP M (?z, 1) ∧ has_F _N um_GP M (?z, 1) → hasSubstance1_GSM (?a, ?c) ∧ hasSubstance2_GSM (?a, ?d)∧ hasF ield_GSM (?a, added_F _z2)∧ has_S1_N um_GSM (?a, 1) ∧ has_S2_N um_GSM (?a, 1)∧ has_F _N um_GSM (?a, 1) For measurement problem of feSFM, the external environment preoperties are used.

Rule

P roblem(?x) ∧ InventiveStandard(?y) ∧ chooses_IS(?x, ?y)∧ has_keyid_of _type(?y, 451) ∧ Generic_P roblem_M odel(?z)∧ correspondsT o_GP M (?x, ?z) ∧ Generic_Solution_M odel(?a)∧ correspondsT o_GSM (?x, ?a) ∧ F ield(?b) ∧ Substance(?c)∧ Substance(?d) ∧ hasF ield_GP M (?z, ?b) ∧ hasSubstance1_GP M (?z, ?c)∧ hasSubstance2_GP M (?z, ?d) ∧ has_S1_N um_GP M (?z, 1)∧ has_S2_N um_GP M (?z, 1) ∧ has_F _N um_GP M (?z, 1) → hasF ield_GSM (?a, ?b) ∧ hasSubstance2_GSM (?a, ?d)∧ hasSubstance1_GSM (?a, added_S1_x2)∧ has_S1_N um_GSM (?a, 2) ∧ has_S2_N um_GSM (?a, 1)∧ has_F _N um_GSM (?a, 1) For measurement problem, combine another system to form a bi-or poly-system.

Rule P roblem(?x) ∧ InventiveStandard(?y) ∧ chooses_IS(?x, ?y)∧ has_keyid_of _type(?y, 5111) ∧ Generic_P roblem_M odel(?z)∧ correspondsT o_GP M (?x, ?z) ∧ Generic_Solution_M odel(?a)∧ correspondsT o_GSM (?x, ?a) ∧ F ield(?b) ∧ Substance(?c)∧ Substance(?d) ∧ hasF ield_GP M (?z, ?b) ∧ hasSubstance1_GP M (?z, ?c)∧ hasSubstance2_GP M (?z, ?d) ∧ has_S1_N um_GP M (?z, 1)∧ has_S2_N um_GP M (?z, 1) ∧ has_F _N um_GP M (?z, 1) → hasF ield_GSM (?a, ?b)∧ hasSubstance1_GSM (?a, S1_in_void_f orm_x6)∧ hasSubstance2_GSM (?a, S2_in_void_f orm_y6)∧ has_S1_N um_GSM (?a, 1) ∧ has_S2_N um_GSM (?a, 1)∧ has_F _N um_GSM (?a, 1) Substance in the form of "void". HEURISTIC ABSTRACT SOLUTIONS Name SWRL rules and explanations Rule P roblem(?x) ∧ InventiveStandard(?y) ∧ chooses_IS(?x, ?y)∧ has_keyid_of _type(?y, 5112) ∧ Generic_P roblem_M odel(?z)∧ correspondsT o_GP M (?x, ?z) ∧ Generic_Solution_M odel(?a)∧ correspondsT o_GSM (?x, ?a) ∧ F ield(?b) ∧ Substance(?c)∧ Substance(?d) ∧ hasF ield_GP M (?z, ?b) ∧ hasSubstance1_GP M (?z, ?c)∧ hasSubstance2_GP M (?z, ?d) ∧ has_S1_N um_GP M (?z, 1)∧ has_S2_N um_GP M (?z, 1) ∧ has_F _N um_GP M (?z, 1) → hasSubstance1_GSM (?a, ?c) ∧ hasSubstance2_GSM (?a, ?d)∧ hasF ield_GSM (?a, added_F _z2)∧ has_S1_N um_GSM (?a, 1) ∧ has_S2_N um_GSM (?a, 1)∧ has_F _N um_GSM (?a, 1) A Field is used instead of a Substance. Rule P roblem(?x) ∧ InventiveStandard(?y) ∧ chooses_IS(?x, ?y)∧ has_keyid_of _type(?y, 5114) ∧ Generic_P roblem_M odel(?z)∧ correspondsT o_GP M (?x, ?z) ∧ Generic_Solution_M odel(?a)∧ correspondsT o_GSM (?x, ?a) ∧ F ield(?b) ∧ Substance(?c)∧ Substance(?d) ∧ hasF ield_GP M (?z, ?b) ∧ hasSubstance1_GP M (?z, ?c)∧ hasSubstance2_GP M (?z, ?d) ∧ has_S1_N um_GP M (?z, 1)∧ has_S2_N um_GP M (?z, 1) ∧ has_F _N um_GP M (?z, 1) → hasF ield_GSM (?a, ?b)∧ hasSubstance1_GSM (?a, S1_in_small_quantities_x7)∧ hasSubstance2_GSM (?a, S2_in_small_quantities_y7)∧ has_S1_N um_GSM (?a, 1) ∧ has_S2_N um_GSM (?a, 1)∧ has_F _N um_GSM (?a, 1) Substances in small quantities are used. Rule P roblem(?x) ∧ InventiveStandard(?y) ∧ chooses_IS(?x, ?y)∧ has_keyid_of _type(?y, 5115) ∧ Generic_P roblem_M odel(?z)∧ correspondsT o_GP M (?x, ?z) ∧ Generic_Solution_M odel(?a)∧ correspondsT o_GSM (?x, ?a) ∧ F ield(?b) ∧ Substance(?c)∧ Substance(?d) ∧ hasF ield_GP M (?z, ?b) ∧ hasSubstance1_GP M (?z, ?c)∧ hasSubstance2_GP M (?z, ?d) ∧ has_S1_N um_GP M (?z, 1)∧ has_S2_N um_GP M (?z, 1) ∧ has_F _N um_GP M (?z, 1) → hasF ield_GSM (?a, ?b)∧ hasSubstance1_GSM (?a, S1_in_small_quantities_and_concentrated_in_certain_parts_x8)∧ hasSubstance2_GSM (?a, S2_in_small_quantities_and_concentrated_in_certain_parts_y8)∧ has_S1_N um_GSM (?a, 1) ∧ has_S2_N um_GSM (?a, 1)∧ has_F _N um_GSM (?a, 1) Substances in small quantities and concentrated in certain parts are used.

Name SWRL rules and explanations

Rule P roblem(?x) ∧ InventiveStandard(?y) ∧ chooses_IS(?x, ?y)∧ has_keyid_of _type(?y, 5117) ∧ Generic_P roblem_M odel(?z)∧ correspondsT o_GP M (?x, ?z) ∧ Generic_Solution_M odel(?a)∧ correspondsT o_GSM (?x, ?a) ∧ F ield(?b) ∧ Substance(?c)∧ Substance(?d) ∧ hasF ield_GP M (?z, ?b) ∧ hasSubstance1_GP M (?z, ?c)∧ hasSubstance2_GP M (?z, ?d) ∧ has_S1_N um_GP M (?z, 1)∧ has_S2_N um_GP M (?z, 1) ∧ has_F _N um_GP M (?z, 1) → hasF ield_GSM (?a, ?b)∧ hasSubstance1_GSM (?a, copy_of _S1_X5)∧ hasSubstance2_GSM (?a, copy_of _S2_y5)∧ has_S1_N um_GSM (?a, 1) ∧ has_S2_N um_GSM (?a, 1)∧ has_F _N um_GSM (?a, 1) A copy of Substance is used instead of itself. hasSubstance2_GP M (?z, ?d) ∧ has_S1_N um_GP M (?z, 1)∧ has_S2_N um_GP M (?z, 1) ∧ has_F _N um_GP M (?z, 1) → hasF ield_GSM (?a, ?b)∧ hasSubstance1_GSM (?a, S1_in_dif f erent_phases_x12)∧ hasSubstance2_GSM (?a, S2_in_dif f erent_phases_y12)∧ has_S1_N um_GSM (?a, 1) ∧ has_S2_N um_GSM (?a, 1)∧ has_F _N um_GSM (?a, 1) Change the phase state of Substance. Rule P roblem(?x) ∧ InventiveStandard(?y) ∧ chooses_IS(?x, ?y)∧ has_keyid_of _type(?y, 532) ∧ Generic_P roblem_M odel(?z)∧ correspondsT o_GP M (?x, ?z) ∧ Generic_Solution_M odel(?a)∧ correspondsT o_GSM (?x, ?a)

has_S1_N um_GSM (?a, 1) ∧ has_S2_N um_GSM (?a, 1)∧ has_F _N um_GSM (?a, 1) Use the dual phase state of Substance.

Name SWRL rules and explanations

Rule P roblem(?x) ∧ InventiveStandard(?y) ∧ chooses_IS(?x, ?y)∧ has_keyid_of _type(?y, 533) ∧ Generic_P roblem_M odel(?z)∧ correspondsT o_GP M (?x, ?z) ∧ Generic_Solution_M odel(?a)∧ correspondsT o_GSM (?x, ?a) ∧ F ield(?b) ∧ Substance(?c)∧ Substance(?d) ∧ hasF ield_GP M (?z, ?b) ∧ hasSubstance1_GP M (?z, ?c)∧ hasSubstance2_GP M (?z, ?d) ∧ has_S1_N um_GP M (?z, 1)∧ has_S2_N um_GP M (?z, 1) ∧ has_F _N um_GP M (?z, 1) → hasF ield_GSM (?a, ?b)∧ hasSubstance1_GSM (?a, S1_with_phase_transition_by_using_physical_phenomena x 14)∧ hasSubstance2_GSM (?a, S2_with_phase_transition_by_using_physical_phenomena_y14)∧ has_S1_N um_GSM (?a, 1) ∧ has_S2_N um_GSM (?a, 1)∧ has_F _N um_GSM (?a, 1) Use phenomena accompanying a phase transition. Rule P roblem(?x) ∧ InventiveStandard(?y) ∧ chooses_IS(?x, ?y)∧ has_keyid_of _type(?y, 534) ∧ Generic_P roblem_M odel(?z)∧ correspondsT o_GP M (?x, ?z) ∧ Generic_Solution_M odel(?a)∧ correspondsT o_GSM (?x, ?a) ∧ F ield(?b) ∧ Substance(?c)∧ Substance(?d) ∧ hasF ield_GP M (?z, ?b) ∧ hasSubstance1_GP M (?z, ?c)∧ hasSubstance2_GP M (?z, ?d) ∧ has_S1_N um_GP M (?z, 1)∧ has_S2_N um_GP M (?z, 1) ∧ has_F _N um_GP M (?z, 1) → hasF ield_GSM (?a, ?b)∧ hasSubstance1_GSM (?a, S1_with_dual -phase_state_x15)∧ hasSubstance2_GSM (?a, S2_with_dual -phase_state_y15)∧ has_S1_N um_GSM (?a, 1) ∧ has_S2_N um_GSM (?a, 1)∧ has_F _N um_GSM (?a, 1) Use Substance with dual-phase state. Rule P roblem(?x) ∧ InventiveStandard(?y) ∧ chooses_IS(?x, ?y)∧ has_keyid_of _type(?y, 535) ∧ Generic_P roblem_M odel(?z)∧ correspondsT o_GP M (?x, ?z) ∧ Generic_Solution_M odel(?a)∧ correspondsT o_GSM (?x, ?a) ∧ F ield(?b) ∧ Substance(?c)∧ Substance(?d) ∧ hasF ield_GP M (?z, ?b) ∧ hasSubstance1_GP M (?z, ?c)∧ hasSubstance2_GP M (?z, ?d) ∧ has_S1_N um_GP M (?z, 1)∧ has_S2_N um_GP M (?z, 1) ∧ has_F _N um_GP M (?z, 1) → hasF ield_GSM (?a, ?b)∧ hasSubstance1_GSM (?a, S1_with_dual -phase_state_and_interaction_x16)∧ hasSubstance2_GSM (?a, S2_with_dual -phase_state_and_interaction_y16)∧ has_S1_N um_GSM (?a, 1) ∧ has_S2_N um_GSM (?a, 1)∧ has_F _N um_GSM (?a, 1) Use Substance with interacting dual-phase state. hasSubstance2_GP M (?z, ?d) ∧ has_S1_N um_GP M (?z, 1)∧ has_S2_N um_GP M (?z, 1) ∧ has_F _N um_GP M (?z, 1) → hasF ield_GSM (?a, ?b)∧ hasSubstance1_GSM (?a, S1_nearest_higher_or_lower_element_x20)∧ hasSubstance2_GSM (?a, S2_nearest_higher_or_lower_element_y20)∧ has_S1_N um_GSM (?a, 1) ∧ has_S2_N um_GSM (?a, 1)∧ has_F _N um_GSM (?a, 1) Simple method to obtain Substance particle, the nearest higher or lower element is chosen.

Appendix H

The Classification of Substance and

Field

The substance and field are classified based on the ontology built by Bultey 1 .

H.1 The Classification of Substance

Appendix I

The SWRL Rules and Their

Explanations for Searching Heuristic

Physical Effects

The 50 SWRL rules for searching heuristic physical effects are divided into two kinds: 42 IS (Inventive Standard) rules and 8 PE (Physical Effect) rules.

I.1 IS Rules

I.2 PE Rules

Name PE rules and explanations Rule 1 P roblem(?x) ∧ Generic_P roblem_M odel(?y)∧ Generic_Solution_M odel(?z) ∧ correspondsT o_GP M (?x, ?y)∧ correspondsT o_GSM (?x, ?z) ∧ chooses_P E_T ype(?x, 0)∧ Substance(?a) ∧ changesOnOrF or_Element(?z, ?a) ∧ Add_SP E(?b)∧ hasIncrementalSubstance(?b, ?a) → chooses_P E(?x, ?b) If a certain substance needs to be added, all the PEs which can add this substance, are obtained.

Rule 2 P roblem(?x) ∧ Generic_P roblem_M odel(?y)∧ Generic_Solution_M odel(?z) ∧ correspondsT o_GP M (?x, ?y)∧ correspondsT o_GSM (?x, ?z) ∧ chooses_P E_T ype(?x, 0)∧ Substance(?a) ∧ changesOnOrF or_Element(?z, ?a) ∧ Substance(?c)∧ includes_Sub(?a, ?c) ∧ Add_SP E(?b)∧ hasIncrementalSubstance(?b, ?c) → chooses_P E(?x, ?b) If a certain substance needs to be added, all the PEs which can add this substance or its children substances, such as, liquid and water, are obtained.

Rule 3 P roblem(?x) ∧ Generic_P roblem_M odel(?y)∧ Generic_Solution_M odel(?z) ∧ correspondsT o_GP M (?x, ?y)∧ correspondsT o_GSM (?x, ?z) ∧ chooses_P E_T ype(?x, 1)∧ Substance(?a) ∧ changesOnOrF or_Element(?z, ?a) ∧ M odif y_SP E(?c)∧ hasInitialStateSub(?c, ?a) → chooses_P E(?x, ?c) If a certain substance needs to be modified, all the PEs which can modify this substance, are obtained.

Rule 4 P roblem(?x) ∧ Generic_P roblem_M odel(?y)∧ Generic_Solution_M odel(?z) ∧ correspondsT o_GP M (?x, ?y)∧ correspondsT o_GSM (?x, ?z) ∧ chooses_P E_T ype(?x, 1)∧

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