maps with TMQL in E-learning context
Hakim Amrouche1, Marie-H´el`ene Abel2, and Amar Balla1
1 National institute of computer sciences; LP 68 M, Oued-Smar, Algiers, Algeria h [email protected]
2 UMR CNRS 6599 Heudiasyc, LP 20529 University of Compi`egne 60205 Compi`egne Cedex, [email protected]
3 National institute of computer sciences; LP 68 M, Oued-Smar, Algiers, Algeria a [email protected]
Summary. In E-Learning, the selection of the pedagogical contents to teach and the organization of this content are two principal operations during the design of a course. The growth and the diversity of the informational resources used generate problems of diffusion, access, of classification and management. With the MEMO- RAe project, we deal with these problems and propose to answer them by using an ontology- based organisational memory. In this article, we describe how and why we exploit the formalism of Topic maps to represent the contents of a course organisa- tional memory and the TMQL standard to interrogate it.
Key words: E-learning, organisational memory, Topic maps, TMQL, Ontol- ogy.
1 Introduction
The growth and the diversity of the informational resources used generate diffusion/access, classification and management problems. Many works are re- lated to the management and access problems to the online learning resources [3][4]. We can classify into two poles the produced systems: the contents pre- sentation systems and the contents management systems.
The contents presentation systems put the emphasis on navigation, visu- alization and organization of information in order to facilitate their compre- hension by users [5]. The learning contents management systems support the access and research of the learning data in relation with their presentation[5].
The MEMORAe(MEMoire Organisationnelle Appliqu´ee `a l’e-learning) project deals, at the same time, with the contents presentation and man- agement. Contrarily to the traditional presentation systems, it does not use adaptive techniques for navigation but an ontology structure: a course is built
around a set of concepts to apprehend. These concepts are defined by the mean of ontology and index the resources allowing their apprehension. The use of ontologies make possible, on the one hand, to define a common vocab- ulary; and on the other hand, to structure the learning contents of a course.
In this article, we describe how and why we exploit the Topic maps formalism to represent the organisational memory contents of a course and the contribu- tion of TMQL standard to iterogate it. The rest of the article is organized as follows: in section 2, a detailed description of the MEMORAe Project is given.
In section 3, the Topic Maps formalism is specified as well as the TMQL inter- rogation associated standard. Thereafter, section 4, we give implementation details of the MEMORAe query mechanism using TMQL.
2 The MEMORAe project
Learning is composed of actors (learners, teachers, etc.) and of various re- sources (definitions, exercises, etc), it is written in various forms (books, Web sites, etc.) like various mediums (paper, video, etc), so training is an organiza- tion. This is why within MEMORAe project [2], the resources and information are managed by means of an ontology-based learning organisational memory.
The MEMORAe project deals both with the content presentation and man- agement. The MEMORAe project does not use adaptive techniques for nav- igation but it uses the structure of an ontology: a training is built around a set of concepts to apprehend. Concepts, defined within ontology index the resources allowing their apprehension.
In the MEMORAe project, two types of ontologies are used: domain on- tology and application ontologies. Both types of ontologies (application and domain) are jointly used to index the teaching concepts and resources. For the moment two application ontologies are defined: NF01 and B31 [2]. NF01 Ontology is designed for the training of Algorithmic course and Pascal pro- gramming. B31 Ontology is designed for the training of statistics course.
The ontologies defined in MEMORAe are formalized using Topic Maps formalisme in order to better index the informational resources and to provide a powerful means for visualization and navigation in the memory contents.
3 Topic Maps
The Topic Maps formalism [6]is a formalism for the representation and the organisation of the knowledge. Topic Maps represent knowledge by a graph made up of nodes bound by semantic relations. A node can be any object which can have a meaning in a given field. The Topics Maps model is power- ful enough for navigation, visualization and information organization to facil- itate the comprehension of this information by users. The Topic Maps model
was standardized by ISO. The Topic Maps formalism presents three basic elements: Topic, Association and Occurrence:
• Topic: Topic is the data-processing representation of a Subject applied to a localization set (Context).
• Association: an association enables to connect two or several Topics.
• Occurrence: an occurrence (information resource) can be an article, an picture, a video, a comment, etc. It is attached to a topic.
For example, the concept finite set is defined as follows: a finite set is a set that has a cardinal. In Topic maps, it is represented by:
• Three Topics: Finite set, Set, Cardinal.
• Two Associations: is a, has.
• One can specify that the concept of finite set is treated in the resource of the Web page type http://www.planete-maths.com/html /.
Note that, at the beginning, this model was not used in semantic Web. With the definition of XTM[7] standard (XML for TM), Topic Maps were intro- duced into the universe of the semantic Web. An XTM file has a similar syntax to an XML file.
Topics Maps can be used in various fields. Therefore, it is important to have exploitation tools of Topic Maps (creation,integration,etc), in this context, the TMQL specification was born.
3.1 TMQL
The purpose of TMQL [8] (Topic Map Query Language) is to create a standard for the interrogation of Topic maps. TMQL is not a language, but it is a specification for the interrogation languages of Topic Maps. The purpose of TMQL is to simplify the development of the Topics Map based applications.
In the TMQL recommendations, it is specified that the query must be carried out on an abstract data model, independently of the storage method (data bases or XTM). In other words, the execution of a request on a data base or on an XTM file should give the same result. The requests must also support the logical inference.
There are several implementations of the TMQL specification, such as Tolog [9], TMRQL [8], AsTMa [8]. Most of TMQL implementations remain prototypes and there is no stable implementation. Tolog is the most stable TMQL implementation, it is for that reason that we chose it to implement our interrogation prototype of Topic Maps.
4 Using Tolog to search concept in MEMORAe
For the implementation of the MEMORAe project, a relational model of ISO Topic Maps standard was set up. Thus ontologies used are stored in a re-
lational database. An E-MEMORAe environment was developed for the ex- ploitation of these ontologies. This environment allows navigation througth ontologies as well as visualization, it was evaluated both on navigation and visualization sides [1]. Note that E-MEMORAe search interface is based on the execution of SQL query on the data base, since ontologies are stored in a data base. On the other hand let us note that SQL language is only intended for data base interrogation, but Topic Maps can be stored under various for- mats (for example: XTM file) therefore occurs the impossibility of using SQL.
Besides, it is difficult with SQL, to exploit the semantic links that exist be- tween Topics. This reduces the possibility of making deductions. It is for these reasons that we were brought to consider TMQL standard for memory inter- rogation with the goal of standardizing E-MEMORAe environment within information search and interrogation.
So to simplify the operation of interrogation and have more flexibility and expressivity in the query writing, we thought of integrating an interrogation environment, in MEMORAe, while working on Tolog, but the current imple- mentation of Tolog does not allow querying Topic Maps stored in relational database. Only the interrogation of XTM files is possible. So it is necessary to extract XTM file from the data base before carrying out the query. Currently, the operation of XTM-file extraction is manual (the automatic extraction is still under development).
4.1 A query Results
MEMORAe philosophy consists to give the most useful information for a learner. Thus the response to a request is part of the ontology that contains the relevant concept and its close neighbourhood. The close neighbour or the family of an concept is defined by the father, the brothers and first generation children of this topic. This philosophy must be respected during the use of Tolog.
In ontologies, concepts can be connected by various types of links. There are two types of significant links: the specialisation/generalisation link and the instanciation link. The specialisation/generalisation link is used to intercon- nect two classes in order to specify the relations (subclass, superclass). The instanciation link is used to connect a class with these instances. This link is represented by the relation instance-of.
In MEMORAe ontologies, this type of links exists, so to find the neighbour- hood or the Topic family, we exploited these two types of links (or relations that express these links). Thus, we get the following definitions:
• Topic B is the parent of Topic A : if A is an instance of B or B is a superclass of A.
• Topic B is the direct child of another Topic A: if B is an instance of A or B is a subclass of A.
• Topic A is the brother of another Topic B if they have the same father.
To simplify the achievement of this operation at the time of the implementa- tion stage, we use Tolog mechanism of the inference rules. These relations are modelled with the inference rules, as a series:
• father-of( A, B) :- {instance-of( B, A) OR superclass( B, A) }this rule describes the relation B is the father of A.
• child-of (A,B) :-{instance-of(A,B) OR subclass(A,B)}: this rule describes the relation A is the child of topic B.
• brother-of(A,B) :-{father-of(A, C) AND father-of(B, C)}: this rule de- scribes the relation brother between A et B . The rule father-of is used to express the rule parent.
The rule that describes the relation family is defined, according to the three preceding rules, by:
family-of(A,B):-{father-of(A,B) OR brother-of(A,B) OR child-of (A,b) .}
This rule is added to the default search query. Thus, its result is added to the default search result in order to have a complete result.
4.2 Implementation
For the implementation of our prototype, we used the JAVA language with TM4J API (Topic Maps for Java) [10]. TM4J is an Open Source JAVA API that allows the integration of Tolog query. We carried out some tests on an fragment of the B31 statistics ontology. Note that the result obtained is rep- resented in the form of an XTM file. The interpretation of an XTM file is difficult for users, it is why we used the TM4L viewer tool [11]to display the result. TM4L is an Open source tool for the visualization of Topic Maps in E-learning [11]. Our objective is to display the result directly in the E- MEMORAe environment. Figure 1 shows the result of search of the concept
’set’.
Fig. 1. Visualisation of a query results using TM4L
5 Conclusion and further works
Our objective with the Memorae project is to propose a new method for in- formation organization and selection in E-learning field. Within this work, we consider that training is an organization and propose to manage, capitalize and diffuse its knowledge and resources by an organisational memory, mod- elled with ontologies. We chose Topic maps standard formalism to represent it. For the memory information and interrogation search, we direct ourselves towards the use of TMQL standard for it proposes a Topic Maps powerful interface of exploitation. In order to test the power of TMQL, we developed an interrogation prototype from Tolog tool. Our tests were carried out on the B31 statistics ontology and they showed the importance of TMQL.
Our prototype should be improved and enriched in order to enable more complex queries, for example: the search for a concept in a given context, the search for a resource associated with a concept.
The choice of TMQL to interrogate our learning memory , being validated, we now work on his integration in the E-MEMORAe environment.
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