Samira Sadeghi
email: sadeghisamirafr@gmail.com

Thomas Dargon
email: thomas.dargon@gadz.org

Louis Rivest
email: louis.rivest@etsmtl.ca

Jean-Philippe Pernot
email: jean-philippe.pernot@ensam.eu

CAPTURING AND ANALYZING HOW DESIGNERS USE CAD SOFTWARE

Keywords: CAD, modeling, design process, modeling process, solid modeling, metrics, performance

Current Computer-Aided Design (CAD) packages support the storage of the final design models and solutions in different formats, and PLM software manages the highlevel information about the design process, such as the versioning of the design solutions. However, the processes happening inside the CAD software are not being fully captured. Information such as the sequence of actions (create a sketch, set a distance constraint, remove a pocket, modify the diameter of a through hole, etc.), versioning of the created objects, etc. is missing. This information can be used to understand how a designer uses CAD software to generate geometric representations. In design companies, capturing this information during a product design project would help to evaluate the designer's way of working with CAD software. In design education, collecting information on how design students generate geometric representations would allow teachers to identify the areas of misunderstanding, improve the education process by representing the optimal way of working, and help teachers to correctly evaluate their students' performance in using CAD software. This paper proposes a framework to support an analysis of how designers use CAD software to generate geometric representations. This framework consists of structured models and an approach which guides the actor in capturing the design process. We use CATIA as a CAD software solution, but the proposed approach is generic and can be extended to any CAD software. The validity of the proposed approach is illustrated through a case study.

INTRODUCTION

In designing a product, not only are design ability and design skills important; being skilled in using the supporting tools is also necessary for the success of a well-designed product. One type of supporting tool is CAD software. CAD tools are used in the more detailed design stages to create detailed design tasks, to create geometries in digital format in both 2D and 3D, to capitalize on the design information, to facilitate the numerical calculations, enhance the precision of the design, etc.

Defining a unique method for the creation of geometries in CAD software is not easy because of the wide variety of functions available within this tool. Different combinations of software commands can result in the same geometry (same final model). Moreover, the modeling process itself evolves and is difficult to predict. Often, the designer does not have a fixed method for designing a specific part or product. The method changes in terms of the results of each action and the difficulties encountered. These modeling processes are often long (depending on the difficulty of the part and the user's skill level). Thus, it is difficult to define a best practice or best process to follow for creating a specific geometry.

Myers et al. [START_REF] Myers | Acquiring design rationale automatically[END_REF] argue that monitoring the actions taken by a designer with a CAD tool would provide a rich, semantically-grounded process history for detailed design. Currently, the process information is not associated with the CAD file, indicating that there is no link between the geometric model and the user's modeling process.

The design of a CAD model results from successive steps of operations and manipulations partially stored in a construction tree. Therefore, by reading the construction tree from the root one can define the succession of operations which generate that CAD model. However, the construction tree does not capture the multiple and potentially iterative steps that were followed to reach the final tree, because that history is not stored within the construction tree.

In the example shown in figure 1, modelling processes 1 and 2 give rise to the same construction tree, even if the successive steps followed to reach the final tree are not the same. In step 4 of process 2, the designer has inserted a pocket in between the extrusion and the hole. Similarly, in process 1, the initial length of the first extrusion has been modified several times. This information has not been captured and thus does not appear on the final trees.

In addition, the time associated with each entity stored in the construction tree is only the date of creation and last modification date of that entity, in the format "hh: mm", which does not allow for great precision in a study of action temporality during the modeling process.

The difference between the users' modeling processes is due to the difference in their CAD knowledge. Bhavnani et al. [START_REF] Bhavnani | Leading indicators of CAD experience[END_REF] discussed the results of studying the behavior patterns of different types and levels of CAD users for a specific drawing task.

Their results show that the experience of the CAD user has a clear correlation to the pattern of commands used, the time taken, and the quality of the drawing produced. Chester [START_REF] Chester | 3D-CAD: modern technologyoutdated pedagogy?[END_REF] proposed three types of CAD knowledge:

• Declarative command knowledge corresponds to knowledge about the commands or algorithms that are available within CAD software. A user needs to know about these algorithms before he/she can use them and he/she needs to know that they exist so that he/she can find them in new software. This knowledge is generic in nature and is applicable across the majority of CAD software. • Specific procedural command knowledge corresponds to knowledge that enables an operator to execute the necessary commands. Specific procedural command knowledge thus varies from one CAD software package to another, and may also vary from one version of a CAD software package to another. • Strategic knowledge includes a range of Meta cognitive processes. Being aware that this type of knowledge occurs ensures that the choice of algorithms will enable the construction of a model that can actually be manufactured.

Extracting the above-mentioned knowledge types from design processes and defining the best practices requires capturing and analyzing the information about those design processes.

Motivations for capturing the design process inside CAD software

We have identified three domains where in it could be worthwhile to capture how designers use CAD software to generate geometric representations:

Universities and educational scenarios: Learning or teaching how to use CAD software poses challenges for both trainers and trainees. Capturing the student's modeling process can help a teacher to adapt their teaching according to the difficulties faced by each student. This can also help teachers to evaluate their students' work. For example, while two students may each submit a correct geometric model, one may have succeeded in their first attempt, but the other could have spent more time and followed more modeling steps. Based on the teacher's rating criteria, knowing this background information will help to distinguish the work of these two students.

Industrial scenarios: For companies that use CAD software on a daily basis, capturing design processes could be helpful in several ways. Information about these processes could help in identifying obstacles to the development of solutions, reduce modeling time and increase team efficiency. Specifically, it would help to identify good practices based on the expert's modeling methods. Capturing and analyzing employees' design processes may encourage them to reflect on their working method and to improve it.

Capturing the design process and the automatic generation of models representing users' modeling processes could help companies to check whether their employees have implemented the modeling rules set by the company. These modeling rules are imposed to support robustness as well as to facilitate the exchange of models between different designers.

CAD software developers' scenarios: Users' design processes can provide feedback that CAD software developers can use to improve the software. For example, if they realize that 80% of users use two functions one after the other, then in a future release, they may offer a new command which combines these two functions, thus increasing efficiency. Similarly, if they see that many people spend a lot of time on the definition of a particular object, they could add functionalities to the software that will facilitate the development of such objects.

Literature review

Several studies have investigated design process modeling and the capture of design information.

Nomaguchi & Fujita [START_REF] Nomaguchi | Knowledge representation framework for interactive capture and management of reflection process in product concepts development[END_REF] proposed a knowledge representation framework, DRIFT (Design Representation Integration Framework of Three layers). The core of DRIFT is a three-layered design process model of actions, operations, and argumentation which captures and manages the reflection processes of generating and verifying design concepts. Ishino & Jin [START_REF] Ishino | Acquiring engineering knowledge from design processes[END_REF] proposed a threelayer design process model to represent generic design processes: 1) Event-Layer: captures primitivelevel design events that are generated by designers operating a CAD system; 2) Operation-Layer: represents higher-level design operations that reflect meaningful design actions, which are clusters of plural events; and 3) Product Model-Layer: represents the design alternatives that are generated from multiple design operations. Later, in [START_REF] Ishino | An information value based approach to design procedure capture[END_REF], they proposed a design activity knowledge acquisition (DAKA) framework that extracts designers' design activity knowledge from the CAD operation event data. Their framework is composed of a product model and a function-based design operation-mining algorithm for extracting meaningful design operations from CAD event databases. Sung et al. [START_REF] Sung | Automated design knowledge capture and representation in single-user CAD environments[END_REF] developed a method to record how and why a design has arrived ata given stage. They captured design processes and design knowledge by logging individual designer behavior and system interactions while a CAD system is being used. Sivanathan et al. [START_REF] Sivanathan | The application of ubiquitous multimodal synchronous data capture in CAD[END_REF] propose a generic framework for the ubiquitous multimodal data capture of CAD system activities. The metadata is captured and stored using ubiquitous multimodal capture tools embedded in the design environment. This data capture is automatic and embedded in the working environment. Consequently, the designer/engineer has no interruption in their usual design activity, and there is no extra workload involved in capturing and generating the information about the design process.

Several studies have investigated the development of methods and tools to capture the design rationale during a design process [START_REF] Lee | What's in design rationale?[END_REF], [START_REF] Potts | Recording the reasons for design decisions[END_REF], [START_REF] Liang | A design rationale representation model using patent documents[END_REF], and [START_REF] Zhang | A semantic representation model for design rationale of products[END_REF]. Design rationale retains design knowledge such as design assumptions, constraints and design reasoning that are often not captured. It captures design alternatives in order to help understand why some designs have been rejected [START_REF] Tang | A rationalebased architecture model for design traceability and reasoning[END_REF]. Myers et al. [START_REF] Myers | Automated capture of rationale for the detailed design process[END_REF] proposed the Rationale Construction Framework (RCF) that acquires rationale information for the detailed design process. The underlying approach involves monitoring designer interactions with a commercial CAD tool to produce a process history. Bracewell et al. [START_REF] Bracewell | Capturing design rationale[END_REF] proposed Design Rationale editor (DRed), a software tool that allows engineering designers to record their rationale as the design proceeds.

None of the works found in the literature include a specific investigation on understanding the way in which designers use CAD software to generate geometric representations.

Focus of our paper

The global objective of this research study is to offer a means to analyze how designers use CAD software to generate geometric representations. Because of the diversity of peoples' motivations for capturing the design process inside CAD software and the multiple ways designers design using CAD, we propose a framework to guide an actor (a person who seeks to capture the design process with a specific objective) so that they can achieve their objectives.

The proposed framework consists of structured models and an approach that guides an actor in capturing the design process.

The proposed models are introduced in section 2, and the proposed approach and corresponding steps are presented in section 3. In section 4, we introduce our specific scenario and show how we can achieve the objectives of our scenario by following the proposed approach step-by-step. We conclude in section 5, where we present the discussion and our conclusions.

PRPOSED MODELS FOR CAPTURING THE DESIGN PROCESS INSIDE CAD SOFTWARE

To support the actor in identifying the elements to be captured from the design process, we built a representative model and a data model of the design process. We considered several criteria for these models.

The models should:

• Represent the design process, showing how the work is actually performed; • Support comprehensive analysis of the process through the model; • Provide a flexible and easily understandable model for representing the process and the required level of detail based on scenario requirements; • Accurately represent the behavioral aspects of the process and track designer operations by providing a rich set of design operations; • Record the evolving attribute values, both structural and semantic; • Record the relationships between the objects, the object-process and the processes; and • Generate a stream of tool events and capture the time notion of the process.

Figure 2 The multi-layered design process model

Multi-Layered Design Process Model:

The first model we developed is a Multi-layered design process model that provides a representative model (Figure 2). This model is based on two main information layers: Product and Process. The Product Layer consists of three main layers: An Object Layer, Product forms or features Layer, and a Product Model Layer. The Process Layer has two main layers: The Process inside the CAD and the Process outside the CAD. The process inside the CAD contains three layers: A Software interaction operation layer, a Low level design operation layer, and a High level design operation layer.

In the product layers, the 'i' in 'PM i ' is the index of the state of the product model. The 'i' in 'O(j) i ' is the index of the object state and the 'j' is the identity of the object. 'PF(j) i ' is the specific type of the object, which is more meaningful in terms of design and is usually the aggregation of several objects. The 'i' in 'PF(j) i ' is the index of the state and the 'j' is the identity of the product form/feature. A 'PMi' is the aggregation of several 'O(j) i ' s.

Design Process Data Model:

Based on our multi-layered design process model, we propose the 'design process data model' (Figure 3). The model has been developed to represent the product design process and to structure the data so that it is prepared for analysis. The model's components are described below:

Product

PROPOSED APPROACH

We propose an approach (Figure 4) which leads the actor to reach his/her objective associated with his/her scenario. This approach consists of five main steps, developed in the following subsections:

|Step1|: Identification of the elements to be captured from the design process

First of all, the actor should identify which elements of the process should be captured, based on the scenario objectives. For example, is the time of the operations required or only the sequence of the operations?

The models proposed in the previous section will help the actor in this identification.

|Step2|: Selection of Methods for Data Capture

In this step the actor should select the appropriate method for data collection based on the result of the previous step. We have identified three main methods for data collection:

1-Screen Video Capture: screen recording software records the screen activities in real-time. This creates videos of whatever the user is doing on his/her desktop. It is intuitive and easy to use. The video data preserves the temporal and sequential structure of the user activity inside the CAD software. By slowing down and speeding up a video recording an actor can see all of the events. Despite the power of video to capture events, it is timeintensive to collect, review and analyze. As a consequence, the analysis tends to focus on short segments of video data at a micro analytical level [START_REF] Jewitt | An introduction to using video for research[END_REF].

2-Macro Recording (CATIA):

The macro file or the log file corresponds to the procedural model. Macro files record the sequence modelling commands. In other words, a macro file is the history of the modeling commands issued by a designer. Most commercial CAD systems support macro file formats, but macro file formats are different for each CAD system. For example, the Macro file of CATIA is written as Visual Basic codes. CATIA records the names of the entities used during the modeling process and uses the 3D coordinates. To record entity names, CATIA uses a topological naming method [START_REF] Choi | Exchange of CAD Part Models Based on the Macro-Parametric Approach[END_REF]. However, the current version of Macro CATIA does not capture the time and software interaction operation.

3-Think aloud protocol: Much of the design process is a mental process; the sketches and drawings that form the visible record of designs do not disclose the underlying processes by which they were created. By visualizing the previous methods an actor can capture the actions of a designer performing a design task. In order to capture the mental process, the designer is encouraged to think aloud. However, this process may distract other designers during the design process.

Table 1 represents a summary comparing the three data collection methods. Each method has its advantages and disadvantages. During this step, the challenges are the tradeoffs between them, the quantity and quality of the information represented and the time needed to capture process and represent this information.

|Step3|: Data Collection

In this step, using the selected method for data capture, the data is collected from the target CAD users following the identified scenario.

|Step4|: Data Preprocessing

The raw data collected in the previous step should be cleaned to be prepared for data analysis. Depending on the data format, different processes and tools are required for this data cleaning. For example, the logfile of the CAD software saves the information related to the design process, but the text format nature of the log-file makes it difficult to use, as it requires a cleaning algorithm to be run on the macro file to prepare it for data analysis. Video files will also need to be reviewed and analyzed to extract the data in an appropriate format ready for analysis.

|Step5|: Data Analysis and Results Interpretation

Depending on the scenario, the data is analyzed in different ways. The actor should identify how this data can become meaningful to fulfill the requirements of a scenario. For example, if the objective is to compare the design process of two users, then, depending on the data type, the actor may create a graph of the user process and then compare it with graph of other users with comparison algorithms. Alternatively, he/she may just want to compare the two users based on statistical data.

CASE STUDY

In this case study, the objective is to compare the design processes of users that have created the same geometry. This will help to identify the users' level of expertise in using the CAD software as well as extract the hidden patterns of the processes.

Case Study |Step1|: Identification of the elements to be captured from the design process

We choose to focus on the operation inside the CAD and so we are not interested in the design objective or in the reasons why a user performs a specific operation. Figure 6 demonstrates the highlighted parts of the general model (Figure 3) that we wish to capture. We selected the 'Screen Video Capture' method to capture information about user interactions with the CAD software. The user's screen has been captured while the user was designing the part. The reason for selecting this method is that we are specifically interested in comparing several users based on the time factor; i.e. the time a user spends on each operation, the correlation of the process time with the use of specific operations, etc. As mentioned above, the current version of Macro CATIA does not allow the time value to be captured; we can only see the notion of time in terms of the operations' sequences.

Case Study |Step3|: Data Collection

In the proposed scenario, we asked four CATIA users to design the same part based on the design brief given to them. The selected part was a piece of an aircraft structure (figure 7-right). This piece is derived from a subject that was given to engineering students. The user starts the modeling from a base part containing the plane and the surfaces (figure 7left). The basic elements (such as the plane for the construction of references and the reference surface) have been made in advance to reduce the users' workload.

Each user's computer screen was captured during the modeling. Each user was in front of a single screen computer, in which CATIA V5 had been installed. The users were free to use methods and modeling tools of their choosing. The motivation for limiting the users to a single screen is that it allows the time that the user spends reviewing the design brief to be recorded. Hence, we can isolate this time from the total modeling time in order to calculate the effective modeling time.

Case Study |Step4|: Data Preprocessing

The video was viewed with care and the identified data elements and corresponding attributes (see

Case Study |Step5|: Data Analysis and Results Interpretation

The collected data is analyzed in this step to make a comparison between the four users.

We defined several criteria for the comparison. The limitation that we encountered in comparing users was the "matching" of objects. We cannot say whether object cut.12 of user A corresponds to object cut.12 of user B, since they probably realized their cuts in a different order. We can however compare the number of created objects or the time spent by object type.

The summary of the results is presented in Table 2. These four users have achieved the same part; it can be concluded based on the comparison criteria that user 4 has the most effective modeling process. Firstly, his total modeling time is the shortest. He also made the least modeling operations (only 33) and created fewer objects to achieve the final model (only 20). He also demonstrated that he knows the subject very well, since he only spent 3" on the brief.

The average time for an effective high level design operation allows us to measure the average time spent by a user in performing an operation. It is interesting to observe that for user 4, this time is not the shortest. User 2 is the one with the shortest average time, for an effective high level design operation. This would differentiate between two types of users: fast users who made many operations quickly, but whose operations are not always good, in the sense that they need to go back to delete or change objects; and slower users that spend more time for each operation and at the end will have made fewer operation to achieve the same geometric model.

Overall, the total modeling time increases with the number of actions. The number of objects increases too, which is quite logical because in general, each action creates an object.

Similarly, we note that user 2, the one with the longest modeling time and the worst efficiency ratio, has spent more time than others in the Sketch creation workshop, perhaps this is related to the user's skill level in using CATIA. Poor knowledge of the CATIA software would result in spending more time to realize effective actions. We can assume that user 2 is familiar with the CATIA software but that he/she may be less familiar with this type of part and so does not yet have a good working methodology for this type of part.

We defined another criterion: the ratio between the effective modeling time and the effective modeling time + idle time (= total modeling time -time outside the CAD). This ratio in some sense shows the efficiency of the user in using the software. Based on the time a user spends in front of the CATIA interface, this ratio provides information on the percentage of time during which he/she has taken actions which had a direct impact on the 3D model. It is still user 4 who has the best performance on this criterion, with over 60% efficiency in his/her use of the CATIA software. To achieve a ratio 100%, a user would have to make all of their actions one after another, without ever moving the model in space and knowing all the keyboard shortcuts for each CATIA command. It is therefore impossible to reach 100% for a human user. However, playing the macro part time to watch and review and can be difficult to meaningfully summarize. One minute of video takes an average of 8 minutes to fully extract the data (this time includes the time it takes to fill out the timetable associated with a video). Moreover, this data extraction is manual; it is not immune to a mistake when entering data in Excel or in the omission of an action. Hence, using video for large-scale campaigns (for hundreds of users) is not reasonable. Scaling up will require investigating automatic methods for capturing the design process. The main limitation of the current version of the macro file is the lack of time values for the operations. This study allowed us to have an idea of the types of conclusions that could be drawn from a comparison among four CAD users.

Capturing the design process and using artificial intelligence methods can provide automated guidance for those users unfamiliar with the software. A completely captured design process may also lead to a modeling process fully realized by the software; the user would only have to insert the characteristics of the geometric elements he/she wishes to build. This will require a detailed study of the geometric characteristics of the model, the identification of certain common geometries and their appropriate classification.

Figure 1

 1 Figure 1 Two different modelling process generating the same construction trees by following different steps. Observe the resulting trees (step 5); it is impossible to distinguish the two processes.

  'O(j) i ' is created or modified by a sequence of high level or low level operations (e.g. D H O Pi and D L O Pi ). High level operation D H O Pi is performed through a sequence of low level operations (e.g. D L O Pi ). D L O Pi is performed through a sequence of software interaction operations (e.g. SO Pi : selecting the extrude function from the menu/shortcut to trigger the extrude function) (See Figure 5).

Figure 3

 3 Figure 3 Design process data model

Figure 4 AFigure 5

 45 Figure 4 A structured approach to capture how designers use CAD software

Figure 6

 6 Figure 6 The model of figure 3 adapted for capturing data in our case study

Figure 7 Figure 6 )

 76 Figure 7 left: the initial model, right: the completed model

Figure 8

 8 Figure 8The raw data captured from the video and entered in an Excel file

Form/feature State (PF(j) i ):Just as

		model. Several additional types of information are
		stored on the process side.
		• Process outside CAD: the aggregation of the
		operations outside the CAD.
		• Operation outside CAD (O Pi ): all types of
		operations that happen during the design process
		outside the CAD and that have a direct or indirect
		impact on the design process inside the CAD (ex.,
		reading the design brief during the design
		process).
		• Process inside CAD: the aggregation of the
		different types of operations performed on the
		objects inside the CAD.
		• Operation inside CAD: The operation inside the
		CAD creates a new object or modifies an existing
		object or simply manipulates the object for
		review. When it is applied to the object a new
		state of the object will be created. An operation
		can aggregate several operations.
		• Software Interaction Operation (SO Pi ): This
		type of operation is related to user interaction at
		the software interface level. For example, a user
		can use a keyboard shortcut to do an operation, or
		access the menu to do the same operation. This
		type of operation is tool-dependent.
		• Low Level Design Operation (D L O Pi ): Low
		level design operations are performed according
		to the sequence of the software operations. This
		type of operation is tool-independent and is
		characteristic of the key design operations. It
		supports direct creation (Line, Rectangle, Ellipse,
		Arc), modification (Undo, Again, Cut, Copy,
		Paste, Select All, Duplicate, Group, Push Back,
		Bring Front), set up (Font, Size, Style, Grid,
		Ruler, Scale, Snap, Browse) and manipulation
		(Rotate, Flip, Zoom) of objects.
		• High Level Design Operation (DHOPi): The
		high level operations aggregate low level design
		operations into higher level units that focus on the
		design objects at the level of forms and features.
	Process Data Model: captures the data of the	• Design Intent: Design intent is the thinking
	process itself. The 'ID' is the number that identifies	behind the user's operations and shows why users
	the operation's or process' identity. 'Type'	have done a specific operation.
	characterizes the type of the operation. 'Start time' is the time the operation or process is triggered and 'End time' is the time that that operation or process is finished. • Design Process: the aggregation of the Process outside CAD and the Process inside CAD. It has a start time and an end time, hence we can calculate the process duration. The result of the whole process will be the final state of the product	When the actor wants to use the proposed model for a specific purpose, based on his/her requirements, s/he should create a sub model of the proposed model. The model has the potential to provide multiple levels of abstraction. The customization of this model based on the scenario requirements will help to capture the data elements of the scenario for further analysis.

• Product Forms/features (PF): Product Forms/Features are high-level design objects (e.g. holes, pockets). It is more important to capture these than to capture the other types of objects. • Process with the Object state, each state refers to a product form/feature, and a product form/feature has several states.

Table 1

 1 Comparison between the three Data Collection methods

			Screen	Macro	Think
	Process Data Model	Video	Recording	aloud
			Capture	(CATIA)	protocol
		Product Model	Yes	Yes	-
		Product Model State	Yes	Yes	-
	Product	Object Object State Product Form or Feature	Yes Yes Yes	Yes Yes Yes	---
		Operation Outside CAD	Yes	-	-
	Process	Software Interaction Operation Low level design Operation High level design Operation	Yes Yes Yes	-Yes Yes	---
	User intent	-	-	Yes
	Time	Yes	-	-
	Bother the user during design process	-	-	Yes
	Automatic way of recording the data	Yes	Yes	-

Table 2

 2 Comparing the design metrics of four users

	Criteria	User1	User2	User3	User4
	Total time of the design Process	30:60	41:58	17:40	07:46
	Total time of the design
	Process outside the CAD	01:27	02:44	02:01	00:03
	software
	Total time of the design
	Process inside the CAD
	software= Total time of the design	29:33	39:14	15:39	07:43
	Process-Total time of the design
	Process outside the CAD software
	Total time of the effective design	11:02	12:45	08:40	04:44
	Ratio= Total time of effective
	design/ Total time of the design	37,43%	32,50%	55,38%	61,43%
	Process inside the CAD
	The average time for an
	effective high level design	00:08,2	00:07,6	00:12,1	00:08,6
	operation
	Total number of high level design operation	81	101	43	33
	Total Number of Created Objects	35	55	21	20


Charlotte Lousteau-Cazalet
email: charlotte.loustau-cazalet@ifp.fr

Abdellatif Barakat
email: abdellatif.barakat@supagro.inra.fr

Jean-Pierre Belaud
email: jeanpierre.belaud@ensiacet.fr

Patrice Buche
email: buche@supagro.inra.fr

Brigitte Charnomordic

Stéphane Dervaux
email: stephane.dervaux@versailles.inra.fr

Sébastien Destercke
email: destercke@hds.utc.fr

Juliette Dibie-Barthelemy

Caroline C Sablayrolles
email: caroline.sablayrolles@ensiacet.fr

Claire Vialle
email: claire.vialle@ensiacet.fr

Lousteau-Cazalet Charlotte

Barakat Abdellatif

Belaud Jean-Pierre

Buche Patrice

Busset Guillaume
email: guillaume.busset@ensiacet.fr

Charnomordic Brigitte

Dervaux Stéphane

Destercke Sébastien

Dibie Juliette

Sablayrolles Caroline

Vialle Claire

A decision support system using multi-source scientific data, an ontological approach and soft computingapplication to eco-efficient biorefinery

Keywords: Decision support system, uncertainty management, belief theory, fuzzy numbers, knowledge engineering, ontology, bioprocess eco-design I

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

A decision support system using multi-source scientific data, an ontological approach and soft computing -Application to eco-efficient biorefinery INTRODUCTION Environmental sustainability assessment of processes is being increasingly viewed as an important tool to aid in the shift towards sustainability. It is a complex task that requires several steps and the examination of numerous factors: energy consumption, energy efficiency and environmental factor of chemical, physicochemical and mechanical treatments. If we consider the bioconversion of lignocellulosic biomass processes [START_REF] Barakat | Eco-friendly dry chemo-mechanical pretreatments of lignocellulosic biomass: Impact on energy and yield of the enzymatic hydrolysis[END_REF][START_REF] Zhu | Woody biomass pretreatment for cellulosic ethanol production: Technology and energy consumption evaluation[END_REF], comparative studies remain scarce, even if the topic of lignocellulosic biomass has been extensively studied in the past thirty years, yielding a great number of scientific papers focused on a specific study. Building DSS able to include scientific data extracted from the literature opens the way to a whole series of new (Meta)-analyses, making it possible to widen the scope of work [START_REF] Busset | Environmental Life Cycle Analysis using knowledge engineering based approach for assessing sustainability of biorefinery systems[END_REF], in order to build more realistic DSS and to help researchers involved in the process design to make rational decisions based on data and knowledge expressed by domain experts in the scientific literature. However this topic is challenging in many ways. The first obstacle holding back the use of those scientific data is their textual format and heterogeneous structure. In this context, the use of ontologies is relevant [START_REF] Trokanas | Semantic approach for preassessment of environmental indicators in Industrial Symbiosis[END_REF][START_REF] Raafat | An ontological approach towards enabling processing technologies participation in industrial symbiosis[END_REF] to structure the experimental information and express it in a standardized vocabulary. This permits to organize knowledge in order to perform automatic reasoning and to facilitate linked open data. The second challenge is to take into account data imprecision and incompleteness. Indeed, a scientific publication often presents data summaries with various formats, for instance intervals or [mean, standard deviation] pairs. These summaries are issued from sets of experiments, which are not available in the paper. The use of intervals or fuzzy numbers is well suited to deal with such imprecisions and uncertainties. The third difficulty consists in taking into account source (document) reliability when using these data in calculations. Belief theory provides elegant solutions to handle this point [START_REF] Destercke | Evaluating Data Reliability: An Evidential Answer with Application to a Web-Enabled Data Warehouse[END_REF].

The DSS architecture proposed in this paper aims at coupling generic methods and reusable software modules to meet these challenges, while being instantiated to meet the specific needs of a particular application. More precisely, this DSS relies on the development of a system: (i) able to annotate, store and maintain potentially incomplete or imprecise data extracted from the scientific literature in dedicated databases, (ii) allowing the computation of indicators taking into account data imprecision, (iii) evaluating document reliability.

Our approach is compared to the state of the art in Section 2, and the software architecture is detailed in Section 3. To illustrate our proposal, we present in Section 4 an example about glucose extraction in rice straw comparing four processes that may include a sequence of unit operations. Section 5 concludes the paper.

II. COMPARISON WITH THE STATE OF THE ART

To the best of our knowledge, there is no comparable DSS implementing a full pipeline such as the one presented in Fig. 1, which allows to represent imprecise data extracted from heterogeneous textual documents in order to compare indicators (for example bioprocess efficiency indicators). This DSS, which will be described in more detail in Section 3, is a pipeline composed of three steps: The only tool comparable with @Web to implement the first step of the DSS is, to the best of our knowledge, Rosanne [START_REF] Rijgersberg | How semantics can improve engineering processes: A case of units of measure and quantities[END_REF]], an Excel ``add-in'' application build on the OM ontology, an ontology of quantities and units of measure. Rosanne allows quantities and units of measures associated with columns of an Excel table to be annotated using concepts from OM. Moreover, as @Web, Rosanne manages the notion of phenomenon, very similar to the notion of symbolic concept in @Web, which represents non numerical data, as for instance studied objects. The main difference is that @web defines the notion of relation, which links data (studied object with controlled parameters and results) in order to represent a whole experiment. It is important in the DSS as this notion is used to extract annotated data in order to compute indicators. Moreover, @Web proposes an end-user graphical interface to query annotated tables using soft computing tools, in particular a bipolar fuzzy pattern matching algorithm [START_REF] Destercke | A flexible bipolar querying approach with imprecise data and guaranteed results[END_REF] which takes into account the fact that data stored in @Web may be imprecise and of diverse reliability [START_REF] Destercke | Evaluating Data Reliability: An Evidential Answer with Application to a Web-Enabled Data Warehouse[END_REF]. This is not available in the current version of Rosanne. From its side, Rosanne proposes an interesting functionality to merge several annotated tables sharing a column annotated with the same concept. As a conclusion, @Web and Rosanne tools are complementary and are based on a partly common ontological representation, the quantity-units component of @Web being very close to the one used by OM.

III. ARCHITECTURE OF THE DECISION SUPPORT SYSTEM

Fig. 1 details the three steps of the data treatment pipeline, which combines ontologies and soft computing tools. In the first step, experimental data published in scientific papers are annotated thanks to an ontology and assessed in terms of their source reliability. Annotated data (that may be imprecise) are stored in a RDF database and available in open access via permalinks, a SPARQL1 end-point and a dedicated querying system guided by the ontology. The second step consists in extracting annotated data from the RDF database to compute indicators and data reliability scores. Indicators and data reliability scores can be visualized in the third step as graphical maps.

Heterogeneous experimental data integration (step 1)

To facilitate integration of scientific data coming from heterogeneous sources, one of the relevant solutions is to use ontologies [START_REF] Noy | Defining N-ary relations on the semantic web[END_REF][START_REF] Guarino | What is an ontology?[END_REF][START_REF] Doan | Principles of Data Integration[END_REF]. @Web implements the first step of Fig. 1 as a complete workflow (see Fig. 2) to manage experimental data: extraction and semantic annotation of data from scientific documents, data source reliability assessment and bipolar flexible querying of the collected imprecise data stored in a database opened on the Web. @Web relies on an Ontological and Terminological Resource (OTR) which guides the scientific data semantic annotation and the querying. OTR is composed of two layers: a generic one and a specific one dedicated to a given application domain. Since the OTR is at the heart of the scientific data capitalization workflow, @Web can therefore be reused for different application domains: only the specific part of the OTR must be redefined to re-use @Web for a new domain (see [START_REF] Touhami | An Ontological and Terminological Resource for n-ary Relation Annotation in Web Data Tables[END_REF] for a reuse in food packaging domain).

Fig.2. Knowledge annotation and querying in @Web system.

@Web is composed of two sub-systems (see Fig. 2) combining knowledge engineering and soft computing tools. The first one is an annotation sub-system for the acquisition and annotation, with concepts of the OTR, of experimental imprecise data extracted from data found in scientific documents; those annotated data being stored into a database. This sub-system also allows the reliability of data sources to be assessed using the approach of [START_REF] Destercke | Evaluating Data Reliability: An Evidential Answer with Application to a Web-Enabled Data Warehouse[END_REF]. The second sub-system is a bipolar flexible querying system based on the approach presented in [START_REF] Destercke | A flexible bipolar querying approach with imprecise data and guaranteed results[END_REF], which allows data stored in the database to be queried. @Web is implemented using the semantic web standards (XML 2 , RDF, OWL 3 , SPARQL): the OTR is defined in OWL2-DL, annotated tables in XML/RDF and the querying in SPARQL. We present in Section 3.1.1 the way OTR has been modeled to be used in @Web. Section 3.1.2 details the model used to assess data source reliability.

OTR model

The OTR is designed to annotate data tables representing scientific experiments results in a given domain (see [START_REF] Touhami | An Ontological and Terminological Resource for n-ary Relation Annotation in Web Data Tables[END_REF] for more details). We made the choice to represent an experiment which involves a studied object, several experimental parameters and a result using n-ary relations in order to structure information in a simple way which can be easily understood by annotators. As recommended by W3C [START_REF] Noy | Defining N-ary relations on the semantic web[END_REF], we used the design pattern which represents a n-ary relation thanks to a concept associated with its arguments via properties. Fig. 3 illustrates this concept (Milling is a unit operation performed on a given biomass).

2 Extensible Markup Language is a markup language. 3 Web Ontology Language is a knowledge representation model built upon RDF.

Reliability assessment scores

We recall in this section the approach presented in [START_REF] Destercke | Evaluating Data Reliability: An Evidential Answer with Application to a Web-Enabled Data Warehouse[END_REF] in order to compute reliability assessment scores associated with data sources extracted from the Web. Given a document o collected from some bibliographical resources on the web, the role of the reliability is to affect an interval-valued score that reflects the a priori reliability of information o. The interval is obtained through an expert system using meta-information, and the length or imprecision of reflects to which extent the various pieces of meta-information are consistent.

The system is built as follows. First, an ordered finite reliability space is built, being the lowest reliability value, the highest. Usually, (as in this paper) or to ensure a good compromise between complexity and expressiveness. A non-decreasing score function on is then defined, in our case . Second, groups of meta-information that will be used to assess reliability are defined, a group taking values , . Various types of meta-information have been considered for the data sources:

1. meta-information on the data source itself: for instance the source type (e.g. scientific publication, technical report), the source reputation, citation data; 2. meta-information related to means used to produce data.

Such information is typically included in a section called Material and method in papers based on experiments in Life Science, which thoroughly describes the experimental protocol and material. Some methods may be known to be less accurate than others, but are still chosen for practical considerations; 3. meta-information related to statistical procedures: presence of repetitions, uncertainty quantification (i.e. variance, confidence interval), elaboration of an experimental design.

In practice, the groups are made so that their impact on reliability can be estimated independently, which can lead to make groups containing multiple criteria (e.g. number of citation and publication date). After the groups have been formed, for each value , an expert of the field from which data are collected gives his/her opinion about how reliable is the data whose meta-information is . This opinion is expressed linguistically, chosen from a set of limited modalities (or combinations of them), e.g. very unreliable, slightly unreliable, neutral, slightly reliable, very reliable and unknown. Each modality is then transformed into a fuzzy set. Fig. 5 illustrates such a fuzzy set, defined on Θ with R=5.

Fig.5. Fuzzy set corresponding to the term very reliable.

To each document o are then associated S fuzzy sets defined on corresponding to its metainformation. Those fuzzy sets are then merged together using evidential theory and a maximal coherent subset approach which allows conflicting evidences to be taken into account (i.e. assessment of high reliability for an aspect but of low reliability for another). The result of this merging is a mass distribution which reflects the global reliability of o (see [START_REF] Destercke | Evaluating Data Reliability: An Evidential Answer with Application to a Web-Enabled Data Warehouse[END_REF] for more details). Final score is then computed using the following formula: Eq.1. Final score definition.

is obtained with the same formula, replacing inf by sup. These scores are then used in the querying system to order annotated data associated with documents thanks to their reliability. [START_REF] Destercke | Evaluating Data Reliability: An Evidential Answer with Application to a Web-Enabled Data Warehouse[END_REF] presents various means to analyze the result of the reliability, such as the reasons that have led to an imprecise assessments and the detection of subgroups of agreeing/disagreeing meta-information.

Software workflow

In this section, due to the lack of space, we only present the step 1 of the workflow which implements the data treatment pipeline presented in Fig. 1. @Web relies on the generic part of the OTR model (see the core ontology in Fig. 4) and allows the management of the domain ontology (by example Biorefinery OTR). As @Web relies on the generic part of the OTR model, several OTR dedicated to different application domains can be managed simultaneously in @Web. For instance, in our current implementation, an OTR dedicated to gas transfer in packaging materials has also been defined and is available at http://www6.inra.fr/cati-icat-atweb. Current version of the OTR of units of measure is also available at http://www6.inra.fr/cati-icat-atweb (section @Web platform, thumbnail Ontology, option Unit Ontology).

Fig.6. Five steps of the annotation sub-system in @web

The annotation workflow of @Web is implemented in five steps presented in Fig. 6. Recorded tutorials of current @Web version are available on-line (http://www6.inra.fr/cati-icatatweb/Tutorials) for readers interested by the complete workflow. In this papier, we focus on two steps. Firstly, we present the second step which is dedicated to document reliability assessment using the model presented in Section 3.1.2. In the current version, meta-information associated with each document is manually entered in order to compute reliability score. In Fig. 7, , reliability score has led to an imprecise assessment due to conflict between expert opinions associated with meta-information: "citation age and citation number" and "source type" are considered very reliable, "Enzymatic hydrolysis reproducibility" and "Biochemical and physic-chemical analysis reproducibility" are considered hardly reliable because only the average value of experimental results associated with those unit operations are given in the document. All operations involving belief functions-needed to compute reliability score have been implemented in a R package. The package is called belief [START_REF] Maillet | R-belief: Contains basic functions to manipulate belief functions and associated mass assignments[END_REF], and it includes basic functions to manipulate belief functions and associated mass assignments (currently on finite spaces only). Secondly, we focus on the fourth step, called Table annotation, which corresponds to the manual semantic annotation of the selected data tables using the concepts of Biorefinery OTR. Taking into account the actual content of the original table, the annotator selects from the n-ary relation concepts defined in the OTR those relevant to annotate the table. Several n-ary relations may be used in a given annotated table in order to annotate experimental data associated with a complete pretreatment process.

Table 1 Excerpt of the annotated table Process Description

Table 1 presents an example of an annotated table in @Web extracted from the scientific document [START_REF] Hideno | Wet disk milling pretreatment without sulfuric acid for enzymatic hydrolysis of rice straw[END_REF], which describes a biorefinery pretreatment process composed of a sequence of four unit operations realized in experiments 1 and 2. The columns of the annotated table correspond to arguments of the relation Milling_Solid_Quantity_Output_Relation (see Fig. 3).

For instance, we can see on the row n o 1 that the first unit operation is a cutting milling, instance of the relation Milling_Solid_Quantity_Output_Relation. The row n o 3 shows that the third unit operation of this process is a second milling, dry ball milling, another instance of the relation Milling_Solid_Quantity_Output_Relation. During the manual data entering guided by the OTR, @Web proposes assistance to several tasks. For example, it is possible to enter an imprecise quantitative value as an interval of values or a pair mean/standard deviation. In Table 2, the quantity Output solid constituent quantity is defined as the precise value 5g for Cutting milling treatment in row n°1 and as the interval [3.1e-2,4.9e-2] g. for Enzymatic hydrolysis treatment in row n°4. Missing data are denoted by the interval [-inf; inf]. In the fifth and last step of annotation, called Storage, the annotated data tables are stored in a RDF triple store which could be queried through either an end-user querying interface or a SPARQL endpoint for open data access. The data annotated with @Web may be queried through an end-user interface, which implements a flexible bipolar querying method described in [START_REF] Destercke | A flexible bipolar querying approach with imprecise data and guaranteed results[END_REF][START_REF] Destercke | Evaluating Data Reliability: An Evidential Answer with Application to a Web-Enabled Data Warehouse[END_REF]. It must be noticed that this querying method performs simultaneously three kinds of reasoning: (1) inference using specialization relation defined in the OTR, (2) ranking according to fuzzy pattern matching between preferences expressed in the query and imprecise data, (3) ranking according to preferences expressed about data source reliability. Selection criteria can be expressed on relation arguments. They may be mandatory or desirable.

IV. CASE STUDY: BIOPROCESS EFFICIENCY

We now present the application of the pipeline presented in Fig. 1 to a case study of bioprocess efficiency [START_REF] Adapa | Grinding performance and physical properties of non-treated and steam exploded barley, canola, oat and wheat straw[END_REF][START_REF] Amiri | Organosolv pretreatment of rice straw for efficient acetone, butanol, and ethanol production[END_REF]. The DSS aims at solving the dilemma of assessing the environmental impact of alternative biorefinery systems, namely glucose extraction in rice straw. Several processes are being compared on the basis of scientific data extracted from bibliographical resources on the Web. Fig. 8 displays the studied system. Efactor is a classical indicator used to compare bioprocess efficiency. For a given set of n documents , we consider for each document the m experimental settings which are described in denoted Each experimental setting is associated with a given biomass, denoted , which belongs to the set of l studied biomasses . For a given biomass b and a given process p, a matter balance indicator, denoted Efactor(o i ,p,b) can be computed for experimental setting belonging to a given document ,. Efactor can be seen as the total input quantity of matter not valorized into glucose but required to produce 1 kg of glucose. Efactor is presented as follows in [START_REF] Chuetor | Innovative combined dry fractionation technologies for rice straw valorization to biofuels[END_REF]:

Eq.2. Efactor definition.
where  B is the initial constant biomass quantity (kg),  C is the chemical reagent product constant quantity used in the process (kg),  S is the constant quantity of solvent (water and/or solution) used in the process (kg),  GRQ (kg) is a quantity defined as the biomass quantity (input of the enzymatic hydrolysis unit operation) multiplied by the glucose rate (glucose available in the raw biomass, denoted GR) and the glucose yield (glucose extracted from the biomass, denoted GY) which depends on the considered experimental result.

The experimental results considered in this case study are GR and GY. Consequently, GR (resp. GY) can be considered as a sample drawn from a random variable. We have noticed that, in a given document , the GY random variable depends on experimental settings, which is not the case for the GR random variable whose sampling shows no variation. In the following, we propose for a given document , a given biomass and a given process , to compute Efactor(o i ,p,b) in selecting the best experimental setting presented in document and computing Efactor best (o i ,p,b). Having in mind the imprecision expressed for random variable GY, a pessimistic point of view will prefer to guarantee the best minimal GY, while an optimistic one will prefer to guarantee the best maximal GY. In this paper, we have chosen the pessimistic point of view to select the best experimental setting. Let us consider (resp.

) the mean value (resp. the standard deviation) associated with the GY random variable of experimental setting j described in document . We assume that the sample is drawn from a normal distribution (the sample size is unknown; this is usually a reasonable assumption in such experiments). Then the best experimental setting with a confidence degree of 95%, denoted , is the one having the maximal lower bound of a 95% confidence interval:

The procedure is illustrated using rice straw biorefinery treatment process data from [START_REF] Amiri | Organosolv pretreatment of rice straw for efficient acetone, butanol, and ethanol production[END_REF]. The best experimental setting corresponds to the one having the maximal lower bound of the 95% confidence interval associated with the GY random variable. Let us consider that B = 1 kg, S = 8 kg, C= 0.0005kg and the 95% confidence interval associated with the GR random variable = [0.51995, 0.57335] in [START_REF] Amiri | Organosolv pretreatment of rice straw for efficient acetone, butanol, and ethanol production[END_REF], we compute, following Eq. We have seen in the previous section that we use @Web queries to extract in csv files data in order to compute Efactor associated with a given topic. We have implemented the computation of the Efactor indicator in a R program. Graphical representations are generated to display an X-Y plot for a given topic and a given biomass where X corresponds to Efactor and Y to glucose yield. For instance, in Fig. 10. , we show a ranking of biorefinery treatments based on Efactor computation for best experiment of considered documents. Each point corresponds to a given biorefinery treatment of rice straw presented in a given document. For each point, the category of treatment is represented by a geometric symbol (see the legend of Fig. 10. ). Reliability scores associated with each document, whose computation has been presented in section 3.1.2, are based on metadata given in Table 2. They have been represented in two colors for each point. The surrounding (resp. inner) color corresponds to the upper bound (resp. lower bound). For instance, the point "CM then dry BM" 4 (corresponding to biorefinery process PM-UFM in Fig. 10) has a glucose yield around 90% and a low Efactor. It is associated with reliability scores which correspond to an imprecise assessment due to disagreeing meta-information represented by an external circle painted in red and an internal one in green (see Reliability index in Fig. 10. ). Results obtained on rice straw with the DSS have been presented to experts in biorefinery. Those results have been positively assessed by experts who used tables and graphics associated with Efactor indicators produced by the DSS to perform the following analysis. In Fig. 10, it must be noticed that a low Efactor (2.03 ± 0.14) was estimated for Cutting Milling (CM) coupling to Ball Milling (BM) with about 90% of glucose yield (89.4 % ± 2) even if data source reliability is not fully established (see reliability indicator and associated metadata in Fig. 7). In general water or chemical pretreatments of rice straw produced more glucose compared to mechanical or dry pretreatment (mechanical, torrefaction…), but produced 4 which means Cutting Milling then dry Ball Milling more effluents with a high Efactor. Results presented in Fig. 10 clearly demonstrate that dry pretreatments (milling, torrefaction…) are simpler technologies which are in general less effective in the production of glucose, but without the need of any chemical or water inputs with an low environmental impact (low Efactor), thus minimizing waste generation while maximizing value of the lignocellulosic feedstock.

V. CONCLUSION AND PROSPECT

In this paper we have proposed a decision support system based on the integration of multisource scientific data and on the calculation of overall indicators. The DSS combines an ontology-based semantic approach and soft computing tools (fuzzy logic and belief theory) to handle data imprecision and reliability. The ontology is used to guide the annotation of potentially incomplete or imprecise experimental data retrieved from the bibliography in order to store them in a structured database. Moreover a model has been used to assess the reliability of data source, and a ranking of results is done taking into account data imprecision and reliability. The potential of the approach has been illustrated with a study of environmental impact factors of biomass conversion processes. Used with the reliability indicators, the DSS gives interesting information for an early stage of decision making at research or laboratory scale. The current development of data warehouses makes it possible for such approaches to gain in efficiency and to give more and more realistic results.

VI.

( 1 )

 1 annotation guided by the ontology of experimental data published in scientific papers, (2) annotated data extraction and indicators computation, (3) indicators visualization in graphical maps. In general, relevant experimental data published in textual documents are scattered in different parts of the document and expressed in different formats. For example, in Process Engineering papers, operation control parameters are often described in sentences within the Material and Method section, while experimental results are presented in tables located in the Results and discussion section. Automatic extraction of scattered information from text and tables of scientific articles is an open research topic[START_REF] Hawizy | ChemicalTagger: a tool for semantic text-mining in chemistry[END_REF][START_REF] Jessop | Mining chemical information from open patents[END_REF][START_REF] Knoblock | Semi-automatically Mapping Structured Sources into the Semantic Web[END_REF][START_REF] Buche | Fuzzy Web Data Tables Integration Guided by an Ontological and Terminological Resource[END_REF][START_REF] Tian | QODI: Query as Context in Automatic Data Integration[END_REF]. It is out of the scope of this paper dedicated to the implementation of a first version of an operational pipeline presented in Fig.1, in which annotation is a manual operation guided by the ontology. However, comparison could be done concerning the first step of the pipeline: experimental imprecise data representation using the semantic tool (including data annotation and querying guided by an ontology) proposed in this paper, called @Web (for Annotated Tables from the Web).

Fig. 1 .

 1 Fig. 1. Data treatment pipeline combining ontologies and soft computing tools.

Fig. 3 .

 3 Fig.3. A Relation concept to model the milling unit operation.

Fig. 4 .

 4 Fig.4.OTR model specialized for biorefinery.3.1.2 Reliability assessment scoresWe recall in this section the approach presented in[START_REF] Destercke | Evaluating Data Reliability: An Evidential Answer with Application to a Web-Enabled Data Warehouse[END_REF] in order to compute reliability assessment scores associated with data sources extracted from the Web. Given a document o collected from some bibliographical resources on the web, the role of

Fig. 7 .

 7 Fig.7. @Web reliability assessment associated with [Hideno et al. 2009].

Fig. 8 -

 8 Fig. 8 -Biorefinery pre-treatment process.

2 ,

 2 Efactor best (o i ,p,b) = [42.04, 51.34].

Fig. 9 :

 9 Fig. 9: 95% confidence intervals associated with the GY random variable for experimental settings presented in [2].

Fig. 10 .Table 2 .

 102 Fig.10. Efactor associated with rice straw for best experiment of documents Source Production Statistics Type of source Sugar analysis method Energy measure repetitions Citation count Enzymatic hydrolysis repetitions Publication date Biochemical and physico-chemical treatment repetitionsTable 2. Metadata considered in the reliability assessment.

SPARQL (SPARQL Protocol and RDF Query Language) is the protocol and the query language which permits to search, add, modify or suppress RDF graphs

ACKNOWLEDGEMENTS

This work has been realized in the framework of the IC2ACV Carnot 3BCAR project.