Ana Inês Oliveira

Luis M Camarinha-Matos

Negotiation Support in Collaborative Services Design

Keywords: Collaborative Network, Negotiation Support Environment, Business Services, Co-Design, Service Co-Creation Network, Service Design

ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

During the last decade, in manufacturing and service industries, collaboration among small and medium enterprises (SMEs) has focused on competencies and resources sharing as an approach to both create new competitive environments, as well as achieve agility to rapidly answer market demands. Working in collaboration implies on one hand sharing the opportunities and gained profits, but on the other hand also sharing the risks and losses, which in turn increases the survival chances of SMEs. Moreover, one market trend is to have more and more customizable products, so companies are forced to change their paradigm from mass-produced products towards highly customized products and ultimately one-of-a-kind products [START_REF] Camarinha-Matos | Collaborative Business Scenarios in a Service-Enhanced Products Ecosystem[END_REF]. This trend is reflected in the term mass-customization that refers to products and services which meet the needs/choices of each individual customer with regards to the variety of different product features.

In GloNet project [START_REF] Camarinha-Matos | Collaborative networks in support of service-enhanced products[END_REF] the design, development and deployment of an agile virtual enterprise environment for networks of SMEs involved in highly customized and service-enhanced products through end-to-end collaboration with customers and local suppliers (co-creation of products and business services) is envisaged [START_REF] Camarinha-Matos | Collaborative Business Scenarios in a Service-Enhanced Products Ecosystem[END_REF]. Collaboration among manufacturers, customers and local suppliers can be improved if a methodological approach is followed to design new business services to enhance the existing products. This leads to the notion of co-design, where all relevant stakeholders are collectively involved in the design process of a new service [START_REF] Sanders | Co-creation and the new landscapes of design[END_REF]. In addition, to support the network of partners (a virtual organization, VO) in achieving agreements during the design of a new business service, it is desirable to have a negotiation environment that facilitates collaboration, and decreases the risks and amount of time spent in this process. The achieved agreements will be the basis for the implementation of the designed service [START_REF] Oliveira | Agreement negotiation support in virtual organisation creation-an illustrative case[END_REF][START_REF] Oliveira | Negotiation Support and Risk Reduction in Collaborative Networks[END_REF].

Considering this background and a virtual organization breeding environment (VBE) [START_REF] Camarinha-Matos | A framework for virtual organization creation in a breeding environment[END_REF] context, that supports and fosters the creation of dynamic VOs, one relevant research question that emerges is:

How can an electronic negotiation support environment increase the agility in the creation process of successful dynamic virtual organizations?

One important motivation for this work is that by contextualizing the design of a new business service in a collaborative environment, it can also use the same negotiation support environment mechanisms to reach agreements, as the ones that are used for the VO creation process in the VBE environment. Therefore, in order to give an answer to this assumption, this paper presents a service co-design negotiation support system that is being designed and developed in the scope of the GloNet project and part of a PhD research work. The paper is then organized as follows: Section 2 identifies the relationship to collective awareness systems; Section 3 gives a brief overview of related literature namely on business services and services composition, the GloNet co-creation network characteristics, and service design; Section 4 illustrates the service co-design negotiation support system; and Section 5 presents the conclusions and future work.

Relationship to Collective Awareness Systems

Collective awareness systems refer to systems that intend to interrelate several sources of data and provide users with relevant data so that they can take better informed and sustainable decisions [START_REF] Sestini | Collective Awareness Platforms: Engines for Sustainability and Ethics[END_REF]. These systems harness concepts from other fields such as: the Internet of Things for data collection; Social Networks for interactions; and Wikis for the coproduction of new knowledge.

As previously mentioned, in the manufacturing and service industry, the trend is to move towards a global networked economy, being therefore important to build a collective network context awareness so that it is possible to share business, knowledge and collaboration. With this support it is possible to build alliances to develop creativity [START_REF] Di Maio | An open conceptual framework for operationalising collective awareness and social sensing[END_REF]. Considering the co-design of new business services where different and diverse stakeholders (including the customer) intervene in the process to achieve new business services design, the collective awareness systems appear with relevance once they can take more accurate and innovative decisions based on the environmental collected data.

Context and Related Areas

To contextualize the service co-design negotiation support, this section gives a brief overview of related literature.

Business services and composite business services. A business service refers to an organized set of added value activities from a business perspective [START_REF] Brentani | Innovative versus incremental new business services: different keys for achieving success[END_REF], considering issues such as the delivery conditions, service level agreements, period, availability, etc. [START_REF] Camarinha-Matos | Collaborative Business Scenarios in a Service-Enhanced Products Ecosystem[END_REF]. It corresponds to the service provided to the customer, and can be implemented by manual services and/or software services whose flow that can be modeled in different business process. Business services delivered to the customer can be composed of several atomic business services. The service providers of such business services together can form a virtual organization to deliver the composite business through a new entity that is the service integrator (that acts as the service provider of the composite business service) [START_REF] Camarinha-Matos | Collaborative Business Services Provision[END_REF]. In GloNet, the design of new business services can include this composition of services.

GloNet co-creation network. One of the relevant business scenarios identified in

GloNet is aimed at providing an environment that supports and promotes the collaborative design of business services. It includes the aspects of mass customization as well as the emergence of new products and solutions to identified needs, through collaboration between manufacturers, customer and members of the customer's community [START_REF] Camarinha-Matos | Collaborative Business Scenarios in a Service-Enhanced Products Ecosystem[END_REF]. Therefore, this network (that is a VO) aims at co-design and co-innovate new value-added services for products. During the life-time of a certain product, several service co-creation networks might be created depending on the number of times new promising business ideas come up for new services [START_REF] Camarinha | Supporting product-servicing networks[END_REF]. Also, an analysis of the IT implementation requirements of those business applications can be performed [START_REF] Yang | Research on the cooperative decision of business application needs in IT governance[END_REF]. This network shall be based on a collaboration environment that helps designing and providing business services based on innovation, knowledge and customer orientation, through collaboration between the different stakeholders (manufacturers, customer and members of the customer's community -open innovation approach).

Service design. It is an interdisciplinary area that connects all relevant stakeholders in the design of services through some methodological approach [START_REF] Mager | Special issue editorial: Designing for services[END_REF]. Service design aims at designing user-oriented services making them useful, effective and different from existing ones. It potentiates co-creation between the different users of a service, and the providers [START_REF] Sandberg | Co-creating collaborative food service opportunities through work context maps[END_REF]. In the different service design activities, especially in cocreation, not all stakeholders need to be involved at the same time; they can be involved just in the specific moments or situation in which they take part [START_REF] Sanders | Co-creation and the new landscapes of design[END_REF], which applies to the co-creation and co-design case of GloNet. There are a number of methods and tools for service design that have been emerging [START_REF] Wild | Review of Service Design Methods[END_REF]; some of which can be found in http://www.servicedesigntools.org/. Nevertheless, some of these tools are just manual methods to organize a collaborative process, others use software tools, but no integrated environment is available, neither any integration between service design and service delivery environments is available. GloNet project is not focused on designing business services but rather on creating a collaborative environment where new multi-stakeholder services can emerge and be provided.

Research Contribution and Innovation

Negotiation in collaboration plays an important role as it can provide a support environment for the formation of VOs [START_REF] Camarinha-Matos | A framework for virtual organization creation in a breeding environment[END_REF][START_REF] Stelmach | Distributed contract negotiation system for virtual organizations[END_REF][START_REF] Picard | Engagement in collaborative networks[END_REF], and essentially improve the entire process of establishing a VO agreement that can lead to the governing rules and principles of the consortium during the operation phase. When designing new business services, there is already a consortium (acting like a VO) with the aim of reaching agreements on the design requirements of the new business service. Therefore, a negotiation support environment can contribute to boost the participation in the business service design, namely if the decision making process is fundamentally made by human actors. This is done by providing them with support functionalities, specifically: basic collaboration, data storage, editing, templates management, notary and traceability. As such, there are similarities to a negotiation support environment for the VO formation phase as already supported in the current PhD research work [START_REF] Oliveira | Agreement negotiation support in virtual organisation creation-an illustrative case[END_REF][START_REF] Oliveira | Negotiation Support and Risk Reduction in Collaborative Networks[END_REF][START_REF] Oliveira | Electronic Negotiation Support Environment in Collaborative Networks[END_REF]. As a result, the current work on negotiation support for the design of new business services also supports the adopted research hypothesis:

The process of creating dynamic virtual organizations can become more agile if an appropriate electronic negotiation wizard environment is established with the necessary soft modeling characteristics to structure and conduct the entire negotiation process, making it traceable, reducing the collaboration risks, and managing the participants' expectations. Moreover, the negotiation environment should be customizable according to different collaboration levels, either in terms of commitment or in terms of duration.

Nevertheless, the negotiation support environment for the design of new business services, represents a different collaboration level and improves the entire process of reaching agreements on the design of new business services so that they can be properly developed and/or implemented. To design new business services, a service design methodology has been adopted [START_REF] Oliveira | Electronic Negotiation Support Environment in Collaborative Networks[END_REF]. As mentioned, a number of methods and tools for service design can be found in different disciplines and for different purposes [START_REF] Wild | Review of Service Design Methods[END_REF]. Nevertheless, most of the tools are simply manual methods to organize collaboration processes. In GloNet, the aim is to use a service design methodology and adapt it to a philosophy of co-design where new business services can be designed in a collaborative environment with multi-stakeholders and customer involvement. The main steps of the approach are summarized in Table 1.

Table 1. Service Design Methodology in co-creation teams.

Service Design steps Description

1 Identify needed service Brainstorming exercise involving an analysis of the needs and characteristics of the customer.

2 Design touchpoints diagram To identify user interaction points with the service.

Design blueprint diagram

To describe the nature and the characteristics of the service interaction in enough detail to verify, implement and maintain it. It includes: temporal order, timings, and line of visibility (denoting what the customer sees and back-office).

Storyboard / storytelling

A tool derived from the cinematographic tradition; it is the representation of use cases through a series of drawings or textual description, put together in a narrative sequence that illustrates a sequence of events such as a customer journey.

5 Service prototyping Involving the selection, assembly and integration of the various service components (atomic services).

For this purpose, it is therefore important to identify: who are the participants; the touchpoints with the customer; and how the participants can share information and documentation between them. Table 2 below addresses some of the relevant characteristics of service design and how they are related to GloNet co-design.

Service Design relevant characteristics Relevance for co-creation teams

Participants

Service design assumes the involvement of various participants from different backgrounds and specially the interaction with the customer.

Co-creation of a new service is expected to involve a temporary collaborative network (VO), including different stakeholders, from geographically dispersed manufacturers, to the providers and supporting institutions close to the customer. The customer is also an active part.

TouchPoints

In service design it is particularly relevant to identify the customer journey in the process of receiving the service, and thus the points of interaction with the service provider.

Aiming user-centered services and being the customer an active part of co-design, it is very important to consider his interactions with the service, namely the moments and places that he gets into direct contact with the service.

Sharing

Service design methods, even if not supported by software tools, a shared space where all participants can visualize the progression of the design process is assumed.

Collaborative environment where the involved participants can interact in the design and creation processes and reach agreements.

Service Co-Design Negotiation Support System. The Services Co-Design Negotiation Support (CoDeN) system is intended to provide a collaborative environment for design of new business services where the various involved participants can reach agreements on what is decided. The involved participants (including the customer) in this process are defined a priori. Similar to a negotiation support system for VO creation [START_REF] Oliveira | Electronic Negotiation Support Environment in Collaborative Networks[END_REF], this system is also intended to generate an agreement that represents the reached consensus. Nevertheless, unlike the VO formation negotiation, here there are no free negotiation topics. Instead, the consensus that has to be reached based on the service design methodology that serves as a guide for the negotiation. Examples of available templates can be found in [START_REF] Namahn | Service Design Toolkit[END_REF]. Similar to other VOs, the entire process is conducted by the VO Planner that acts as the cocreation team mediator.

In Fig. 1, the main flow and interactions of the system are illustrated. The thick arrows represent the strong negotiation that is required in the co-design process and the numbers correspond to the steps of the approach summarized in Table 2. Also, this system directly interacts with: the negotiation support for agreement establishment that allows clients to exchange information with warranty of authenticity and validity as well as providing a safe repository for saving and requesting documentation. Below there is an adapted i* Rationale Strategic model where the involved actors as well as their dependency objectives with the system are illustrated. Within the boundaries of the Services Co-Design Negotiation Support system the respective tasks and sub-tasks are presented. The services that directly interact with the involved actors and other related systems are also depicted. Discussion of Results. The work presented in this paper is the result of a service codesign negotiation support system specification aimed at supporting the co-creation team mediator in the negotiation process of a new business service design. The support environment to achieve agreements on the business service design is based on software system that assists the human decision making and guides the generation of the final agreement (based on specific templates), and stores the participants' received input. In this way, the process becomes structured and traceable. Moreover, the intended environment is based on the same mechanism already specified for negotiation during a VO formation phase, meaning that it has to be adaptable to different types of collaboration. Through the interaction with other systems, namely: (i) support for agreement establishment, it is also possible to guarantee a degree of authenticity; and (ii) VO risks assessment is possible to reduce the risks in collaboration and increase adaptation to unexpected events because partners expectations are also considered. Through the on-going PhD research and participation in the European research project GloNet, the topic of new business service design gains more significance. Therefore, the test bed and validation of the current work is being considered in this context.

Conclusions and Further Work

To compete in the global market, where customer's requirements are becoming more specific and customizable, it is important that companies maintain efficiency and the sustainability level, so they tend to collaborate in order to accomplish those requirements of almost one-of-a kind products or services. One factor that is important is the agility that these companies can have to interoperate and co-work with each other in a co-creation environment. In order that the co-creation or codesign of a new business service becomes more feasible, it is important to have an advanced service to support the existing team, so that the negotiation process to establish agreements can be achieved. This paper summarizes an on-going research work that tries to fulfill the above requirements: the specification of the service codesign negotiation support system. As some of the concepts and preliminary results have already been positively validated, it is now foreseen to achieve a more advanced environment that comprehends most of the described areas with the needed adaptations to support the aimed negotiation support for dynamic VOs creation and new business services design. The emphasis of the current work is on the relations and interactions of the several sub-systems that together provide an advanced consortia formation support, namely: (i) the basic services for VO creation that are related to the order characterization and partners search; (ii) the base negotiation support with negotiation templates management and support for agreement establishment; and (iii) functionalities to reduce potential risks in collaboration. The ongoing validation process is intended to consist in peer validation and supported by the EU project GloNet.

Fig. 1 .

 1 Fig. 1. Services co-design negotiation support system interactions.

Fig. 2 .

 2 Fig. 2. Adapted i* Rationale Strategic Model for the Services Co-Design Negotiation Support system.

Table 2 .

 2 Service Design Methodology in co-creation teams.

Acknowledgments. This work has been supported by the Collaborative Networks and Distributed Industrial Systems Research Group of Uninova and partly by the GloNet project (FP7 programme) funded by the European Commission.


Catarina Lucena

João Sarraipa

Ricardo Jardim-Goncalves

A Knowledge Management Framework to Support Online Communities Creation

Keywords: Knowledge Management, Collective Awareness, Ontology. ALTER-NATIVA's Knowledge Base Learning Management System Query System ALTER-NATIVA Community Ontology + Thesaurus Thesaurus wiki Synchronization model JDBC Protegé

Nowadays, there are constant increases of people that actively produce and consume online content. They use various tools to create and share information with others, namely: blogs; forum; photo & video sharing and social networking sites. As a consequence the development of services or mechanisms for the acquisition of knowledge from Internet users is currently observed as crucial. However, most of such knowledge is often unstructured and is only available in a tacit form. Thus, this paper presents a framework to facilitate knowledge management to support communities' establishment. Its main objective is to build a community knowledge base from cyclical conversion of individual to organizational knowledge representations creation, and consequently, to allow specific reasoning and decision-making.

Introduction

Social Web is a set of relationships that link together people over the Web [START_REF] Appelquist | A Standards-based, Open and Privacy-aware Social Web[END_REF]. Social Web encompasses how social software is designed and developed in order to support and foster social interactions [START_REF] Porter | Designing for the social web[END_REF]. This social software tools typically handle the capturing, storing and presentation of communication and focus on establishing and maintaining a connection among users [START_REF] Allen | Tracing the Evolution of Social Software[END_REF]. Social Web focuses on human communication, and as consequence the vast majority of the information is in human readable format only. Thus software programs cannot understand and process this information, and much of the potential of the Web remains unusable. This leads to the necessity of having an approach or solution to concretize the semantic web concept.

The original Scientific American article on Semantic Web concept appeared on 2001 [START_REF] Berners-Lee | The Semantic Web[END_REF]. It described the evolution of a Web that consisted in a large amount of documents for humans to read and that included data and information for computers to manipulate [START_REF] Shadbolt | The Semantic Web Revisited[END_REF]. Semantic Web can be seen as a set of technologies that help knowledge sharing across the web between different applications. Ontologies based technologies play a prominent role in the Semantic Web [START_REF] Taye | Understanding Semantic Web and Ontologies: Theory and Applications[END_REF]. They make possible the widespread publication of machine understandable data, opening opportunities to automatic knowledge reasoning able to potentiate the generation of context awareness systems.

In this paper an initial assessment related to the necessity of gathering knowledge from social web applications is made. This gathering and storing of knowledge in a formalized structure will make possible to software applications to enrich the knowledge retrieved from individuals, adjusting it to specific objectives enabling its systems to have the notion of a context. This will contribute, for instance, to help users to find others to interact with about a specific purpose, which will facilitate to online communities creation and also to enable them to have contextual awareness able to make decisions supported by the knowledge handled. Thus, based on this, it is then proposed an ontology-based framework to contribute to such online communities creation. Afterwards, it is presented an architecture based in an application scenario of the proposed framework, followed by some conclusions and future work statements.

Relationship to Collective Awareness Systems

In today's internet, many applications arise in the context of "Web 2.0", a concept popularized by O'Reilly Media [START_REF] Reilly | What is Web 2.0: Design Patterns and Business Models for the Next Generation of Software[END_REF]. Web 2.0 focuses in collaboration of users and sharing information between them. On the one hand, Web 2.0 focuses on enhance creativity, information sharing and collaboration among users. On the other hand, Semantic Web refers to the intelligent interaction among systems and applications by deploying ontologies, semantic annotation of Web content, and reasoning. Its ultimate goal is to make data understandable to computers [START_REF] Devedzic | Web 2.0 & Semantic Web[END_REF]. Together they contribute for software applications with knowledge from social software. This leads to the Web 3.0 appearance, where Semantic Web technologies are integrated into Web 2.0 technologies, or powering, large-scale Web applications [START_REF] Hendler | Web 3.0 Emerging[END_REF].

Folksonomies are inventive Web 2.0 tools, which aggregates both web 2.0 and semantic web characteristics being great for categorizing documents and resources in a collaborative way [START_REF] Kaminski | EDITORIAL Folksonomies boost Web 2.0 Functionality[END_REF]. They arise when a large number of people are interested in particular information and are encouraged to describe it [START_REF] Shadbolt | The Semantic Web Revisited[END_REF]. The term was originally defined by Thomas Vander Wal [START_REF] Vander Wal | Folksonomies definition and wikipedia[END_REF] as the result of personal free tagging of information and objects (anything with a URL) for one's own retrieval. The person consuming the information does the act of tagging.

By other hand, ontologies specify a conceptualization of a domain in terms of concepts, attributes, and relations [START_REF] Fensel | Ontologies: A Silver Bullet for Knowledge Management and Electronic Commerce[END_REF]. Consequently, semantic web and its applications rely heavily on formal ontologies to structure data for comprehensive and transportable machine understanding. In addition, the Semantic Web's success is then dependent on the quality of its underlying ontologies [START_REF] Maedche | Ontology Learning for the Semantic Web[END_REF], [START_REF] Hazman | Article: A Survey of Ontology Learning Approaches[END_REF].

As mentioned before, social software normally shows limitations related to use or in the formalization of user's knowledge. For that reason a new framework to gather and formalize knowledge from social software, allowing reasoning and support to decision making is proposed (see Fig. 1). This framework uses ontologies and folksonomy concept principles to enable knowledge management features, able to facilitate the achievement of collective awareness. Thus, this proposed framework represents the author's research, which contributes itself to collective awareness systems establishment. Collective awareness refers to a common and shared vision of the whole team's or community context that allows members to coordinate implicitly their activities and behaviors through communication [START_REF] Daassi | Groupware and team aware: Bridging the gap between technologies and human behaviour[END_REF]. Making decisions with awareness will reduce the effort to coordinate tasks and resources by providing a context in which to interpret utterances and to anticipate actions [START_REF] Gutwin | Workspace Awareness in Real-Time Distributed Groupware: Framework, Widgets, and Evaluation[END_REF]. This is also an objective that the framework intends to contribute to, where collective awareness is supported by the knowledge acquisition features from social networks through specific reasoning over that knowledge. This facet will facilitate communities' coordination by helping them in decision-making. Decision-making can be regarded as the cognitive process resulting in the selection of a specific resource or action among several alternative scenarios. Every decision making process produces a final choice [START_REF] Reason | Human Error[END_REF]. As can be seen in Fig. 1, the proposed framework is composed by four modules: 1) Social Software; 2) Synchronization; 3) Ontology; and 4) Reasoning and Decision Making. The input of the framework is the Social Software users and the output is the community in general, which in this case raises some awareness using the knowledge represented in the system. The first module is social software, which is characterized by being collaboratively handled by social web users. This module can be a blog, wiki, forum, etc. However, as mentioned before, the content of social software is human readable only. This means that it is not formalized to facilitate its computerized use. For that reason, the synchronization module has incorporated some changes to that social software in order to transform its content to machine interpretable information. This synchronization is made between the knowledge represented in the used social software and the one in the ontology.

The role of the ontology in the proposed framework is to hold the gathered knowledge about a domain in a structured way, with a specific purpose to facilitate reasoning over that knowledge. Such reasoning is made by the module Reasoning and Decision Making, which is able to supply personalized or specific knowledge, representing to communities an access to structured contextual information. Contextual information is used by humans when communicate with each other. By improving computers access to such context, we increase the richness of communication human-computer and make it possible to produce more useful computational services [START_REF] Abowd | Towards a Better Understanding of Context and Context-Awareness[END_REF]. Thus, these communities can, between other options, use the retrieved knowledge, and consequently to facilitate the interaction with other users with same interests, experience, etc.

ALTER-NATIVA's Knowledge Management architecture

The proposed approach can be applied to various domains. In this case it is described the application of the framework to develop a Knowledge Management architecture for the ALTER-NATIVA community (Fig. 2).

ALTER-NATIVA is one ALFA III project. ALFA is a program of cooperation between Higher Education Institutions of the European Union and Latin America. ALFA III retains the original objective of the previous phases of the ALFA Program, that is, to promote higher Education in Latin America as a means to contribute to the economic and social development of the region [START_REF]Development and Cooperation -EUROPEAID[END_REF]. The main goals of the project were to: 1) Give education for everyone; 2) Give an environment of formation to professors when leading with persons with some disabilities; 3) Balance the inequalities of opportunities when accessing information. In this architecture a complete Knowledge Management cycle was implemented. The beginning of the cycle or the input of the architecture is the knowledge of the ALTER-NATIVA community. This knowledge is processed and consumed by the community, which then uses it to re-feed this cycle with more knowledge. The gather of knowledge from the community is focused on the creation of a Thesaurus to represent the information related to the concepts/keywords (lexicon) of four distinct areas, namely Literature, Mathematics, Science and Teaching.

The Social Software used in the developed architecture was the Wikipedia. Wikipedia is an open encyclopedia that is collaboratively edited by its users. This software provides so called categories that are used to classify articles and other pages. The process of assigning categories to Wikipedia articles is a kind of collaborative tagging like in a folksonomy, but in this case, it is related to the categorization of concepts based on its definitions. One particularity is that the resulting category system is a thesaurus. A Thesaurus can be represented by a set of classes organized as a taxonomy representing domain reference concepts with associated meanings about domain in a semantic related structure. However, despite this strong usability, Wikipedia is not fully prepared to be integrated with other information systems. For that reason, in ALTER-NATIVA were implemented and used two extensions to allow a structured insertion of knowledge. The MediaWiki community developed the CategoryTree extension. It facilitates an organized introduction of concepts in a hierarchical form, representing the wanted concept tree. The other extension was developed to avoid the insertion of data with HTML codification (e.g. in the middle of tags), which resulted in an easy and coherent knowledge insertion form for the community users. This extension is composed by four textboxes (Fig. 3 a)): in the first textbox it is inserted the father of the concept (category of which this concept is member); in the second it is inserted the definition of the concept; in the third the creation date; and in the forth the author/authors identification.

The synchronization module of this architecture uses Wikipedia Data Base to detect any changes in the log of information that could have occurred in the Wiki Thesaurus since its last run. JDBC (Java Database Connectivity) is used to querying Wiki Thesaurus database. If any change occurred, it then updates the Knowledge Base's Thesaurus accordingly. The OWL API provides classes and methods to load and save OWL files, to query and manipulate OWL data models. This module runs periodically thanks to a "cron job" implemented in the Wikipedia's server. Thus, changes made by the users are constantly updated establishing in this way, a dynamic knowledge handling feature since its gathering from web users till its availability to the community.

The establishment of a Knowledge Base arises from the necessity to formalize the knowledge related to the ALTER-NATIVA's domain and allowing its communities to actively participate in its maintenance. The ALTER-NATIVA's Knowledge Base is split in two distinct parts: the "Ontology" and the "Thesaurus", which are connected by the relation "has keywords". This relation helps to characterize the resources of this community e.g. Virtual Learning Objects (VLO) making use of the knowledge gathered by the architecture. The Ontology is dedicated to represent the Knowledge of this community's resource. It main objective is to facilitate resources representation (categorization), searching and recommendation. Resources categorization uses the lexicon represented in the thesaurus. Each resource can be related to a set of concepts of the thesaurus.

The resources searching functionality use specific characteristics of the resource e.g. level of expertise of a professor, and the concepts that characterize them, which then can be used to find a professor with a pre-determined profile. The resources' recommendation functionality is an advanced feature that automatically records specific patterns usage information, as an example, a professor of Mathematics normally has interest in differential calculus. Thus, next time a professor of this kind of profile enters the system, the system automatically recommends to him a set of resources. It could be, for instance, a specific type of VLO related to differential calculus.

The ALTER-NATIVA Knowledge Base is able to support on the suggestion of users to interact with, based on common interests and levels of expertise potentiating communities' creation. This is done by the advanced services of the Community Element Finder of the architecture (Fig. 2). Other feature is the recommendation of VLO to which is done by the advanced services of the module Learning Management System. Whether the contextual information is formal, seed by the keywords and associated meaning or by the ontology, the expertise of the platform users is available for the implemented architecture be able to recommend with some context awareness. This could be reached by associating for instance the levels of expertise of users to the collected by associating the number of times that the user consults a specific topic. Then, by specific inference it is possible for the system to be aware of some context. One professor with a specific profile normally has interests or needs about a particular thing (e.g. VLO).

Demonstration scenario

The ALTER-NATIVA's project main goal is to define curricular guidelines with technological support for higher education in the areas of language, mathematics and science with specific focus in supporting people in context of diversity. Thus, one of the main scenarios of this project pursue the idea of supporting teachers from different disciplines, many of them from pedagogical careers, with accessible tools that would facilitate them in the creation of working groups for discussion and for the generation of specific VLOs supported by a recommendation system [START_REF] Sarraipa | Knowledge Representation in Support of Adaptable eLearning Services for All[END_REF]. As a consequence, the importance of developing services or mechanisms to gather knowledge of the domain experts was observed. Since in this scenario, professors are the main actors and better know how to characterize this project's resources: the Thesaurus for each of the ALTER-NATIVA project areas (literature, mathematics and science areas) gathered the knowledge directly from the professors. An example of a contribution to the thesaurus creation is shown in Fig. 3. An expert inserts the information corresponding to de concept "Concepto geométrico" in the form of Fig. 3 a). When the user submits it, the output is the web page of Fig. 3 b). After having the thesaurus with substantial contents, it is possible trough Community Element Finder services, search for specific profile of people. As an example, it is possible to search for professors that are experiment in "Geometric concepts" ("Concepto geométrico"). Another similar contribution in the possibility of search for a specific VLO characterized for keywords using the Learning Management System, as an example a VLO, which has the keyword "Geometric concepts". The system is also able of advanced recommendations based on the historical information of users. Taking as an example, if a user consults several times a VLO, is most likely that he/she wants to consult other ones with similar characterization in terms of keywords. The same kind of recommendation is also made when searching for a user to interact with. If a user wants to develop a VLO characterized by a set of keywords, a possible suggestion of user to interact are other users that use to develop Virtual Learning Objects with the same keywords characterization.

Conclusions

The proposed framework establishes a set of components with specific characteristics to guide on the creation of software platforms able to support online communities' creation. Its main characteristic is related to its capability of gathering and formalization of knowledge from its users to afterwards use it for specific reasoning services. Due to the use of such knowledge, these services aggregate a collective view of a domain knowledge. Depending on its design, such services are able to provide its related systems with specific reasoning and Decision-making ability.

In the example of ALTER-NATIVA project the services were designed to recommend specific resources in relation to a determined profile of user. Thus they give some support to what is defined as "context awareness". Depending on the profile (context) of the user, the system is able (aware) to suggest the most appropriate resources to such kind of people, which will help them in their (online) community creation.

As future work, the authors want to integrate ontology learning in the framework. The concept Ontology learning represents methods and techniques used to build, semi-automatically or automatically, ontology from scratch, enriching, or adapting an existing ontology using several sources [START_REF] Gómez-Pérez | A survey of ontology learning methods and techniques[END_REF]. Thus, he authors' intend to create a prototype to allow an autonomous gather of knowledge from social software, which capable afterwards used to automatically build its related ontology. One example of a step in execution/implementation is to consider Wikipedia categories as ontological classes and the corresponding pages as instantiations of the knowledge base. This would allow a higher dynamic conversion from the individuals to community knowledge.

Fig. 1 .

 1 Fig. 1. Framework for Knowledge Management to support online communities creation.

Fig. 2 .

 2 Fig. 2. ALTER-NATIVA's Knowledge Management architecture.

Fig. 3 .

 3 Fig. 3. Knowledge gathering in the wiki insertion form.


George Burlacu
email: burlacu_george85@yahoo.com

Ruben Costa

Joao Sarraipa

Ricardo Jardim-Golcalves

Dan Popescu
email: dan_popescu_2002@yahoo.com

A Conceptual Model of Farm Management Information System for Decision Support

Keywords: Ontology, Management Information System, Data Acquisition

In a today economy, it is crucial to have systems able to handle information with precision. In addition, it is also important to apply technological innovations in the various domains, with the objective to modernize and transform them to become more competitive. In this paper, a conceptual framework for a Farm Management Information System (FMIS) is presented. It is focused in the different ways of using the information coming from various sources as sensors to assist farmers in decision making of agriculture business.

Introduction

Precision agriculture is a modern method to make agriculture, which refers to optimizing the production through the fusion of traditional mechanized agriculture procedures with new technologies such as monitoring systems, command & control systems, geographical location systems and support information systems. These optimizations have the aim of choosing the right time for culture seeding, at the right place and monitoring the culture throughout the growth period, depending on the various parameters.

Precision agriculture uses various tools for collecting information such as soil quality sampling, remote field sensing and yield monitoring. Optimization focuses on increasing yields, reducing cultivation costs and minimizing environmental impacts [START_REF] Gebbers | Precision Agriculture and Food Security[END_REF]. The challenges that precision agriculture tries to overcome are: -Automatic collection of the related information about the physical structure and chemical composition of the soil; -Development of a well-structured database, which should be integrated in GIS (Geographical Information Systems); -Development of intelligent agricultural machinery for farm working with high degree of spatial accuracy; -Nutrients distribution in variable doses required for uniform plants growth, in order to compensate the unevenness of soil characteristics; -Pesticides applications have to take into consideration the nature of existing pests and weeds in crops [START_REF] Tellaeche | A new vision-based approach to differential spraying in precision agriculture[END_REF]; -Making crop productivity maps in real time based on flow sensors mounted in combine hopper that harvested grain [START_REF] Adamchuk | On-the-go soil sensors for precision agriculture[END_REF]; -Making electrical conductivity distribution maps of soil. These maps provide an overview of water distribution and concentration in soil [START_REF] Johnson | Field-scale electrical conductivity mapping for delineating soil condition[END_REF].

Traditional agriculture uses a variety of techniques to improve land quality in order to make it suitable for planting. This includes using animal manure and digging water-channels for field's irrigation. Modern agriculture uses technologies for plant breeding and pesticides and fertilizers optimization.

Relationship to Collective Awareness Systems

Collective Awareness Systems are designed to support the environment, by sharing knowledge, best practices and processes, in order to achieve changes in lifestyle, in terms of production and consumption [5].

In this context, the paper propose a framework for a Farm Management Information System, which is a system able to manage the information with precision, having the purpose to optimize both resources and processes that occurs in the farm, using sensors systems and other types of technologies for monitoring the land.

Furthermore, the paper present an ontology that has been developed with the aim of representing the knowledge used for understanding the concepts and relations regarding the Farm Management Information System. Also, the ontology offers support to farmers in decision-making regarding the land and crop management.

Related Work

A Farm Management Information System (FMIS) is a management information system designed to assist agricultural farmers to perform various tasks ranging from operational planning, implementation and documentation for assessment of performed field work.

Sørensen proposed a conceptual model to develop a FMIS (Fig. 1a)). The main actor in this picture is the farm manager who needs to manage the overall agricultural crop production. The managerial demands are caused not only by the internal farm activity of production, but also by external entities like government, customers, universities, who increase the pressure on the agricultural sector to change the methods of production, whether the quality, the price or the technological improvements is considered [START_REF] Sørensen | Conceptual Model of a Future Farm Management Information System[END_REF]. The proposed FMIS aims to support management tasks and real-time decision making, as well as compliance management by automating data acquisition, contextualization operations taking into account external parameters (e.g., regulations, Best Management Practices (BMP), market information, etc.). In addition, the structure of FMIS should allow the connection with external systems (e.g., market, financial, administration, etc.). The automatically acquired data provide complete monitoring of operational activities. The farm manager Information initiates the both internal and external filtering, taking into account the planned operational activities. Based on this, an execution plan can be generated and sent to the executants (e.g. equipment, staff or supply service that are designed to perform the operation).

In addition to the conceptual model proposed by Sørensen, Payman Salami and Hojat Ahmadi [START_REF] Salami | Review of Farm Management Information Systems (FMIS)[END_REF] defined a system architecture (Fig. 1b)) focused in how the user of the system should understand it. The communication is represented by arrows, which are intentionally left vague, meaning that they do not have a specific protocol or content of the communication. The end user does not need to know or even care how the communication is handled between the various systems, only that it occurs and that it is possible.

In Romania, agriculture plays an important role in our daily lives; many families produce the necessary revenues existence in this way. In recent years, this sector has increasingly presented more interest and attracted many development funds. Even so, many farmers choose to work the land in the traditional way. Their motivation is mainly related to the financial aspect necessary to implement a FMIS.

Farm Management Information System Framework

The new agricultural technologies such as Variable Rate Technologies (VRT), Remote Sensing, GIS and Global Positioning System (GPS), in addition to the developments in modelling and simulation of crop production, provide numerous opportunities for the development of Precision Agriculture. In Fig. 2 is represented a new conceptual model of FMIS. Unlike the conceptual models proposed by Sørensen, Salami and Ahmadi, this framework focuses on the way it performs data acquisition, mapping, making spatial distribution maps, productivity maps and profit maps. The acquired information is stored on a platform together with the information from the various external web services; the system purposes some possibilities from its knowledge base, through which the farmer can take a decision on land or crops. in a mechanized collection by using modern agricultural machinery. This practice is known as "sampling" [START_REF] Schnug | Sampling and nutrient recommendations -The future[END_REF]. Sampling is used to obtain an overview distribution of soil chemical properties and/or to check for any abnormalities in normal chemical properties of soil. Also physical sizes (soil electro conductivity, soil pH, concentrations of nitrates, potassium, sodium) are measured directly in the field by specific laboratory values methods. Also, measuring the position of these points is recorded using GPS [13].Data from the mapping process are used to estimate the production of agricultural crops, in the entire area or specific areas of interest. In this estimation process, data from the history of agricultural land are taken into account and analyzed. A profit map can be created using inputs records in crops and outputs in harvest. So, a farmer can determine what areas of the field aren't profitable.

Farmer can also make crop productivity maps, using sensors that measure and record in real time the harvested crops volume. Various parameters like quantity per hectare and flow are measured. In this way, crop productivity maps are a valuable solution in farm management. Spatial distribution maps of the relevant parameters are ranked based on several criteria (visualization, physical support, technical type mapping, type of variables, type of representation, etc.). One advantage of using spatial distribution maps is the avoidance of the surplus or deficit in natural or mineral fertilizer (nitrogen, phosphorus, potassium, magnesium, nitrates). Automated VRT allow farmers to vary inputs, such as fertilizers, pesticides and seeding rates [START_REF] Srivastava | Space Inputs for Precision Agriculture: Scope for Prototype Experiments in the Diverse Indian Agro-Ecosystems[END_REF]. Varying input rates aims at either increasing yields or reducing costs, depending on the farmer's goal for the production system. Auto guidance systems, on the other hand, assist equipment operators to drive through the fields so that efforts could be focused on other important tasks. Therefore, these technological tools help to reduce redundancy, labour costs and to save operation times.

The sensors are used to determine soil properties (ground temperature/air temperature, humidity), hydric stress, and degree of crop disease (using the reflection of light on the leaves to determine their level of chlorophyll). Also, the sensors are used for measuring various parameters like electrical conductivity, high concentrations of potassium, nitrates, sodium, pH, or can be used to measure the soil and crops properties. The need for increase the production and simultaneously the efforts for saving resources make the sensor systems the best-allied tool. Their function requires the physical storage of data, using devices known as "data loggers", which are equipped with non-volatile memory. Data transmission is done either by cable (wired) or by radio signals (wireless) [START_REF] Ruiz-Garcia | A Review of Wireless Sensor Technologies and Applications in Agriculture and Food Industry: SoA and Current Trends[END_REF].

Using remote sensing sensors (aerial or satellite sensors) lands maps and cultures maps are obtained. The "aerial photographer" is an optical sensor that observes the variations in soil colour, crop development and land boundaries. Images obtained by satellite or by an aircraft can provide maps of vegetative indices, which reflect also the plant health, the soil conditions or the crops status. The obtained database is more accurate (contains a large number of measured values and parameters), thereby it increases the quality of the agricultural production estimations and decisions .

In Fig. 3 different use of type of sensors that can be found in farm are presented. The role of Context allocator is to adapt the nodes behaviour in order to divide the workload based on operating or network conditions. A sensor node is a node in a wireless sensor network. This node gathers sensory information and communicates with other connected nodes in the network and is capable to perform some processes [START_REF] Dargie | Fundamentals of wireless sensor networks: theory and practice[END_REF]. FMIS can use sensors in three different ways: competitive; collaborative; and individual. The first case represents the competitive level of sensors. In this case, if the system needs to wait for the operator's intervention, maybe it becomes too slow, and thus the decision-making capability can give large benefits. So using the context allocation, new tasks are allocated to suitable nodes. In the second case, the sensors collaborate forming a network. Wireless Sensor Networks (WSNs) have been deployed as a cost-effective communications technology, which allows the acquisition and transmission of different data from the crop to final users. These WSNs consists on non-intrusive communication devices of small size, to which one or more precision sensors for data collection are adapted. Once the information arrives at their operators, it is further processed and studied in order to make an appropriate decision. In the third case, using for example moisture sensors and data coming from the weather station (external source), the farmer can set a daily schedule pre-programmed for soil irrigation, according to a soil moisture level, calibrated by the farmer. A soil moisture sensor will be connected to each zone, in the relevant regions in order to assess the humidity for the entire area. Thus, the water consumption used for this purpose is minimized. If the soil moisture, at one time, is already higher than the reference level, irrigation is not done in that area and so it saves water.

Farm Management Information System Ontology

The ontology has been developed with the aim of representing the knowledge that can be used for understanding the concepts and relations regarding the FMIS, with the purpose of supporting farmers in decision-making regarding the land and crop management. The ontology was built through Protégé editor, using the OWL Full version, mainly because of its high expressiveness capability. Fig. 4 a) illustrates an excerpt of the defined ontology representing a FMIS aligned with its conceptual model (Fig. 2). The ontology intends to answer different questions about FMIS features. As an example, Fig. 4b) illustrates how the ontology represents sensors characteristics. The types of sensors used in FMIS described in [START_REF] Tellaeche | A new vision-based approach to differential spraying in precision agriculture[END_REF], are represented as instances of Sensors class [START_REF] Gebbers | Precision Agriculture and Food Security[END_REF], which provides data for GIS [START_REF] Adamchuk | On-the-go soil sensors for precision agriculture[END_REF], able to measure: concentration, electro-conductivity, moisture and temperature [START_REF] Johnson | Field-scale electrical conductivity mapping for delineating soil condition[END_REF].

Conclusions and Future Work

In Romania, after many years of decline, reviving the agricultural activities has become a priority. In this context, precision agriculture supported by management information systems of the farm activities represents a viable and effective solution for its modernisation. In this paper, it is proposed a FMIS for precision agriculture, which is focused on functions to handle: land mappings, internal and external data collection, monitoring parameters, and data processing for decision support. Through such type of features, the farmer is able to see information about new technology, available resources (human, materials), information from the market (competitors, prices, forecasts), information coming from ministries and European Union, so it can take a better profitable decision. FMIS related knowledge is represented in a designed ontology to facilitate formal data acquisition from various monitoring, control, analysis and tracking systems for the referred precision farming implementation.

In the future authors intend to develop a platform that will integrate this ontology and external services; business process modelling between farmer, suppliers and potential clients using Business Process Model and Notation (BPMN); to then effectively test the proposed FMIS in pilot farms.

Fig. 1 .

 1 Fig. 1. FMIS: a) A Conceptual model [6]; b) Architecture from the user viewpoint [7].

Fig. 3 .

 3 Fig. 3. Different type of sensors used in farm.

Fig. 4 .

 4 Fig. 4. FMIS Ontology: a) structure excerpt; b) sensors characterization.

Acknowledgments. This article results has been developed within the EXPERT POSDRU/107/1.5/S/76903 (http://plone.cempdi.pub.ro/BurseDoctoraleID76903) and FITMAN Nº 604674 (http://www.fitman-fi.eu) projects, which has been funded respectively from the European Social Fund and EC 7th Framework Programme.


Jakub Flotyński
email: flotynski@kti.ue.poznan.pl

Krzysztof Walczak
email: walczak@kti.ue.poznan.pl

Multi-Platform Semantic Representation of Interactive 3D Content

Keywords: 3D web, Semantic Web, 3D content, multi-platform, ontology

In this paper, a semantic approach to building multi-platform 3D content is proposed. The presented solution is intended to enable flexible and efficient creation of 3D presentations covering a wide range of target platforms -visualisation tools, content representation languages and programming libraries. Referring to the semantics of particular content elements can facilitate conceptual knowledge-based content creation at arbitrarily high levels of abstraction, and it can improve indexing, searching and analysis of 3D content in a variety of application domains on the web.

Introduction

Widespread use of interactive 3D technologies and multimedia platforms, including powerful and omnipresent mobile devices, has been recently enabled by the significant progress in hardware performance, the rapid growth in the available network bandwidth as well as the availability of versatile input-output devices. 3D technologies become increasingly used in various application domains, such as education, training, entertainment and social media, significantly enhancing possibilities of presentation and interaction with multimedia information sources, thus increasing collective awareness of their users.

However, to reach a high number of recipients on the web, support for a diversity of hardware and software systems must be provided. Currently, wide coverage of different hardware and software systems by 3D presentations is typically achieved by providing separate implementations of various 3D content browsers and presentation tools. However, in contrast to the development of individual 3D content browsers and presentation tools for different target systems, compatibility of 3D content representation with diverse popular presentation platforms could improve the reuse of 3D content components and the overall use of 3D content on the web. In such an approach, once 3D content is created, it can be presented using different platforms. Moreover, such approach does not require users to install additional software, but it can leverage well-established 3D content browsers and presentation tools that may already be installed on the users' systems (e.g., Adobe Flash Player or WebGL/X3DOM-compliant web browsers). However, currently, the development of 3D platforms is driven by large industry players in a competitive environment and the issue of multi-platform 3D content presentation is still neglected, resulting in fragmentation of content and technologies. This is an important obstacle preventing the mass use of 3D application interfaces.

The main contribution of this paper is a multi-platform semantic representation of interactive 3D content. The approach permits flexible and efficient creation of 3D content for a variety of target presentation platforms. In the proposed solution, once the structure of 3D content is designed, it can be automatically transformed into different final presentation forms, which are suited to different 3D content presentation platforms. The selection of the target platforms to be used is an arbitrary decision of a system designer. Referring to the semantics of particular 3D content components and the conformance to the well-established Semantic Web standards enables 3D content representation that is independent of particular browsers and presentation tools, permits reflection of complex dependencies and relations between content components, and can facilitate indexing, searching and analysis of 3D content in a variety of web applications.

Interactive 3D Content in Collective Awareness Systems

Support for content presentation across different platforms is essential for building collective awareness systems. Several works have been devoted to multi-platform 3D content presentation. In [START_REF] Mendes | Interactive 3D visualization of natural and cultural assets[END_REF], a specific 3D browser plug-in for different web browsers has been described. In [START_REF] Celakovski | Multiplatform real-time rendering of MPEG-4 3D scenes with Microsoft XNA[END_REF], an approach to hardware multi-platform 3D content presentation based on MPEG-4 has been proposed. In [START_REF] Almer | A tourism information system for rural areas based on a multi-platform concept[END_REF], an approach to multiplatform visualisation of 2D and 3D tourism information has been presented. In [START_REF] Tack | Platform independent optimisation of multi-resolution 3D content to enable universal media access[END_REF], an approach to adaptation of 3D content complexity with respect to the available resources has been proposed. In [START_REF] Han | Multi-platform online game design and architecture[END_REF], a multi-platform on-line game has been presented. In [START_REF] Jankowski | A dual-mode user interface for accessing 3d content on the world wide web[END_REF], integrated information spaces combining hypertext and 3D content have been proposed to enable dual-mode user interfaces -embedding 3D scenes in hypertext and immersing hypertextual annotations into 3D scenes -that can be presented on multiple platforms on the web. The aforementioned works cover the development of 3D content presentation tools and environments as well as contextual platform-dependent content adaptation. However, they do not address comprehensive and generic methods of content transformation to improve building of multi-platform 3D content presentations.

Collective awareness systems require sharing the meaning of data and content between users and systems. Numerous works have been devoted to semantic 3D representation of information. In [START_REF] Pittarello | Semantic description of 3D environments: a proposal based on web standards[END_REF], integration of X3D and OWL using sceneindependent ontologies and the concept of semantic zones have been proposed. In [START_REF] Kalogerakis | Coupling Ontologies with Graphics Content for Knowledge Driven Visualization[END_REF], an ontology for X3D as well as semantic properties for coupling VR scenes with domain knowledge have been described. In [START_REF] Walczak | Flex-VR: Configurable 3D Web Applications[END_REF], 3D content representation based on reusable elements with specific roles has been introduced. In [START_REF] De Troyer | Conceptual modeling for virtual reality[END_REF], an approach to generating virtual words upon domain ontologies has been considered. In [START_REF] Latoschik | Semantic Reflection for Intelligent Virtual Environments[END_REF], semantic entities in VR applications have been discussed. The aforementioned approaches address different aspects of semantic modelling of 3D content, but they lack general solutions for comprehensive conceptual creation of 3D content with respect to its components, properties and relations, at an arbitrarily high level of semantic abstraction.

The Method of Semantic 3D Content Creation

Although several approaches have been proposed for semantic modelling of 3D content, they lack general and comprehensive solutions for flexible creation of 3D representation of information. The following requirements, which have been specified for an approach to 3D content creation, go beyond the current state of the art in semantic modelling of 3D content. First, the approach should enable conceptual declarative modelling of content with discovery of hidden knowledge, which is not specified explicitly, but has impact on the modelled content. Second, it should reduce the effort in content design by enabling modelling of complex content components and properties at arbitrarily chosen levels of abstraction, including both the aspects that are directly related to 3D content and the aspects that are specific for a particular domain (e.g., to facilitate content creation by domain experts who are not ITspecialists). Third, it should be independent of particular hardware and software platforms to enable multi-platform 3D content presentation.

This section provides an overview of a method of semantic creation of interactive 3D content (proposed in [START_REF] Flotyński | Semantic Modelling of Interactive 3D Content[END_REF]), which leverages the model of multi-platform 3D content proposed in the next section. In the presented method, the creation of 3D content is a sequence of partly dependent activities (Fig. 1). Modelling of 3D content in the first three activities -design of a concrete representation of 3D content, mapping the concrete representation to domain-specific concepts, and design of a conceptual representation of 3D content -is performed by a developer and a domain expert, depending on the expertise required in the particular modelling activity. The two following activities of the method -expanding the content representation and building the final content representation -are performed automatically by specific software. The activities precede 3D content presentation, which may be done using various content presentation platforms. The design of a concrete semantic representation (activity 1) -provides particular elements of 3D content to enable representation of domain-specific concepts that are further used in activity 3. The result of activity 1 is a concrete semantic representation of 3D content, which is a knowledge base compliant with the platform-independent semantic model of 3D content (PIM -proposed in [START_REF] Flotyński | Semantic Multi-layered Design of Interactive 3D Presentations[END_REF]). The knowledge base incorporates semantic components and properties that are directly related to 3D content, e.g., meshes, groups of objects, materials, viewpoints, events, etc.

The mapping of a concrete 3D content representation (created in activity 1) to domain-specific semantic concepts enables 3D presentation of domain-specific knowledge bases (created in activity 3) [START_REF] Flotyński | Conceptual Semantic Representation of 3D Content[END_REF]. Mapping is performed once for a particular domain-specific ontology and a concrete representation, and it permits the reuse of concrete components and properties for forming 3D representations of various domain-specific individuals (which conform to the domain-specific ontology).

The design of a conceptual semantic representation (activity 3) enables creation of 3D content at a high level of abstraction with a domain-specific ontology. This activity can be performed many times for a particular domain-specific ontology, a concrete representation and a mapping. The following activities of the content creation process can be performed automatically. Expanding the content representation multiplies the concrete components (created in activity 1), which are associated with domain-specific concepts, and assigns them directly to domainspecific individuals (created in activity 3). Building a final 3D representation is a transformation of the expanded representation to its final 3D counterpart, which is encoded using a particular 3D content representation language. This stage of the method is an extension of the approach proposed in [START_REF] Flotyński | Building multi-platform 3D virtual museum exhibitions with Flex-VR[END_REF], and it uses the multiplatform model of 3D content, which is the main contribution of this paper.

Multi-Platform Model of 3D Content

Although numerous solutions have been proposed for creating semantic representations of 3D content, they do not enable flexible creation of multi-platform 3D content, which can be used for visualisation of various types of information using a multitude of available content presentation tools. In this section, a new multi-platform model of 3D content is proposed (Fig. 2a). The model extends the semantic content model (cf. [START_REF] Flotyński | Semantic Multi-layered Design of Interactive 3D Presentations[END_REF][START_REF] Flotyński | Conceptual Semantic Representation of 3D Content[END_REF]) and enables the last activity of the content creation method (cf. [START_REF] Flotyński | Semantic Modelling of Interactive 3D Content[END_REF] and Section 3) -building the final 3D content representation. The model consists of four parts: platform-independent content representations (PIRs), platform-specific content representations (PSRs), template bases (TBs) and transformation knowledge bases (TKBs). The model enables transformation of PIRs to PSRs, which may be visualised with diverse 3D content representation languages (e.g., X3D, Java), programming libraries (e.g., Java3D, Away3D) and game engines (e.g., Unity, Unreal), which are determined by the content presentation platforms to be used. Transformation is performed using TKBs and TBs, which are specific to particular presentation platforms. An individual TB and its corresponding TKB are created once every time a new presentation platform is introduced to the system (Fig. 2b). Since a TB and a TKB are added, they may be used for the development of various PSRs that are permitted by the new presentation platform (Fig. 2c). The particular parts of the proposed content model are described in the following subsections.

Platform-independent Representations

A platform-independent representation (PIR) of 3D content is a knowledge base comprised of platform agnostic semantic elements: individuals -reflecting 3D content components, and properties -reflecting dependencies and relations between the components. Every PIR is compliant with the PIM (cf. [START_REF] Flotyński | Semantic Multi-layered Design of Interactive 3D Presentations[END_REF] and Section 3). A PIR declaratively describes 3D content, as it is a set of semantic statements (Ss) and rules (Rs), which may be described using, e.g., RDF and SWRL, respectively. Every S is a triple consisting of a subject, a property and an object. Ss are used to describe aspects of 3D content that are related to geometry (e.g., shape), structure (e.g., subcomponents), space (e.g., position), appearance (e.g., texture) and simple behaviour (e.g., animations). Every R is an implication including a body and a head, which are conjunctions of Ss. An R is interpreted in the following way: if the body is satisfied, then the head is also satisfied. Rules are used to describe complex behaviour of 3D content, in particular complex animations and interactions, which may depend on multiple factors, such as time, state of objects, user actions, etc. PIRs can be presented on different platforms when transformed to PSRs with appropriate TBs and TKBs.

Platform-specific Representations and Template Bases

A platform-specific representation (PSR) of 3D content is a counterpart to a PIR, and it includes 3D objects and 3D scenes, which are encoded using a 3D content representation language. Every PSR is built upon a TB, as it is a combination of templates (Ts), which are parameterised fragments of code (e.g., sequences of instructions) of a particular 3D content representation language. Ts may be linked to individual Ss as well as to Ss that are included in Rs of PIRs. For some of the target platforms used, this context of linking may determine the form of the T used. Every T may be given in two template variants that differ depending on the context of its use.

Active template variants (ATVs) are used for the independent Ss (to express logical facts) and the Ss included in the heads of Rs (to express logical results), while passive template variants (PTVs) are used for the Ss included in the bodies of Rs (to express logical conditions). For instance, the S [object pim:shape "cone"] may be transformed to an ATV [object.shape="cone"] or to a PTV [object.shape. isEqual("cone")], which are encoded in an object-oriented language.

To enable flexible transformation of PIRs to PSRs, the granularity of Ts should reflect the semantic granularity of their corresponding Ss -Ts should neither extend nor narrow the meaning of the linked Ss. Composing basic Ss into semantically more expressive Ss is performed in the previous activities of the semantic modelling method presented. Template parameters (TPs) enable the reuse of individual Ts in different contexts in combination with other Ts, e.g., by joining or nesting Ts. All PSRs that are based on a common TB and which are presented on a common target platform are generated from PIRs using a common TKB.

Transformation Knowledge Bases

A transformation knowledge base (TKB) links an individual TB to the PIM, thus enabling reflection of semantic Ss by parameterised Ts. A TKB specifies a transformation of PIRs to the corresponding PSRs that are to be presented on a common target platform. Semantic elements contained in a TKB do not influence the modelled 3D content. A TKB consists of statement patterns (SPs), each of which matches a group of possible Ss that should be processed collectively. Linking a group of Ss to an SP is performed by semantic generalisation. A generalisation may pertain to the subject, the property or the object of Ss. For instance, a possible generalisation (an SP) of the S [object pim:color "red"] in terms of property, is the SP [object pim:appearanceProperty "red"], while a possible generalisation of the S [object rdf:type pim:Mesh3D] in terms of object, is the SP [object rdf:type pim:GeometricalComponent].

A target PSR, which is generated based on Ss of a PIR, may require an exchange of TPs in a group of different Ts, which are associated with these Ss, and it may require a specific order of Ts. For this purpose, SPs may be gathered into statement collection patternsstatement set patterns (SSPs), which do not respect the order of the associated Ts, and statement list patterns (SLPs), which do respect the order of the associated Ts in the resulting PSR. For instance, while a declarative PIR including the pair of Ss

Example of a Multi-Platform 3D Content Representation

In this section, an example of a multi-platform 3D content representation is discussed. The example 3D scene (Fig. 3) could be used, e.g., in a collective educational system or a virtual museum system. The scene includes a light source and a 3D model of a plough, which has been retrieved from a repository of a virtual museum of agriculture. In the example, the Adobe Flash Player is selected as the target presentation platform. However, in general, arbitrary selected multiple platforms could be used. A PIR of the scene is presented in Listing 1. It uses the PIM (the pim prefix) to describe the 3D content components regardless of any particular target platform. A TB and a TKB, which enable encoding of PIRs with the ActionScript imperative programming language and the Away3D library, are presented in Listings 2 and 3. The listings include only elements that are crucial for the discussion.

The S1 and S2 statements (Listing 1) create the scene and set its background with the appropriate colour. The Ss are processed according to the SLP1 (Listing 3), which includes two SPs. The S1 is dynamically linked to T1 (by its property and object) and the S2 is dynamically linked to T2. The T1 is a template of an ActionScript document and it initializes a 3D view, while the T2 sets the background of a scene. The order of the SPs in the SLP1 determines the order of the T1 and T2 in the resulting document (a PSR). The SLP1 requires a new object to be created before any property of this object is set (according to the imperative programming paradigm). If the T linked to the SP1 includes the $actions TP, the T linked to the SP2 is injected into this TP, else the T of the SP2 follows the T of the SP1 in the final PSR. The S3 and S4, which create a light source, are processed in the same order (determined by the SLP1) as the previous Ss, independently of their initial order in the PIR.

The plough, which is a virtual museum artefact, is a structural component that consists of the three geometrical components-a box, a wheel and a frame (S5-S7). The SLP2 is applied to the S5-S7-in the resulting PSR, instructions that create new objects in the scene, precede instructions that link these objects by properties (T3). In the example, the resulting PSR (an ActionScript document) includes the Ts in the required order, with the proper TPs specified. The PIR has been created with the Protégé editor. However, a tool for visual semantic modelling can be developed. representation enables more efficient and flexible methods of indexing, searching and analysis of the content regarding complex dependencies and relations between its components, provides methods of describing rules of combining different components and permits description of complex content behaviour.

The following directions of future research are possible. First, the proposed approach needs to be implemented for selected content representation languages (e.g., ActionScript and X3D), and combined with the approach to transformation of 3D content description formats proposed in [START_REF] Flotyński | Building multi-platform 3D virtual museum exhibitions with Flex-VR[END_REF]. Second, the approach should be evaluated and compared to other solutions in terms of both: the possibilities of high-level conceptual content creation and the effort in the implementation of multi-platform 3D content presentations. Third, the complexity of TBs and TKBs for different target languages -imperative and declarative -could be compared. Furthermore, a visual modelling tool supporting the semantic content creation can be developed. Finally, a persistent link between semantic and final content representations can be proposed to provide real-time synchronisation of the content state.

Fig. 1 .

 1 Fig. 1. Semantic creation of 3D content

Fig. 2 .

 2 Fig. 2. Multi-platform model of 3D content (a), introducing a new platform to the system (b), and modelling multi-platform 3D content (c)

  .] does not depend on the order of the Ss, a corresponding imperative PSR may require the following order of the associated Ts (imperative code instructions) [DirectionalLight light = new DirectionalLight(); light.intensity = 10;].

Fig. 3 .

 3 Fig. 3. A final 3D content representation

Conclusions and Future Works

In this paper, a new approach to building multi-platform representations of 3D content has been proposed. The presented solution has several important advantages in comparison to the available approaches to 3D content presentation. First, it is more convenient for environments, which cover various hardware and software systems, as the development of TBs and TKBs requires less effort than the development of individual content models or content browsers. Second, the possible use of wellestablished 3D content presentation tools, programming languages and libraries liberates users from the installation of additional software, which can improve the dissemination of 3D content. Third, the use of Semantic Web standards permits conceptual knowledge-based content modelling that refers to hidden information inferred using, e.g., RDF, OWL or SWRL reasoners. Moreover, semantic