# Ontology Mapping for Learning Objects Repositories Interoperability

Amel Bouzeghoub, Abdelatif Elbyed

► To cite this version:

Amel Bouzeghoub, Abdelatif Elbyed. Ontology Mapping for Learning Objects Repositories Interoperability. 8th International Conference Intelligent Tutoring Systems (ITS 2006), Jun 2006, Taipei, Taiwan. pp.794-797, 10.1007/11774303_103 . hal-00341916

HAL Id: hal-00341916

https://hal.science/hal-00341916v1

Submitted on 1 Nov 2023

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Distributed under a Creative Commons CC BY 4.0 - Attribution - International License
Ontology Mapping for Learning Objects
 Repositories Interoperability

Amel Bouzeghoub and Abdeltif Elbyed

GET/INT, 9, rue Charles Fourier,
91011 Evry, France
{Amel.Bouzeghoub, Abdeltif.Elbyed}@int-evry.fr

**Abstract.** In order to deal with the need of sharing learning objects within and across learning object repositories most of the recent work argue for the use of ontologies as a means for providing a shared understanding of common domains. But with the proliferation of many different ontologies even for the same domain, it become necessary to provide mapping process to perform interoperability. The main key issue must be addressed is to define educational systems interoperability mechanisms to create a virtual learning space. Although many efforts in ontology mapping have already been carried out, few of them use resources properties to generate semantic relations between local concepts. Our approach uses inference rules to combine several matchers in order to discover mapping dynamically and to improve the results qualities.

**Keywords:** Learning Resources, Ontology Mapping, Interoperability and Multi-Agent Systems.

# 1 Introduction

Ontologies offer a great potential in higher education providing in particular the sharing and reusing of information across educational systems and enabling intelligent and personalized learner support. The increased functionalities that ontologies imply will bring new opportunities to e-learning. Learners will be able to interact with distant educational systems easily and in a personalized way. An overview of ontologies for education field and an initial report on the development of an ontology-driven web portal O4E are presented in [2].

We propose in this paper an algorithm which is applied on a Web Based Educational System (WBES) named SIMBAD [1]. To facilitate resources exchange with other WBES, it becomes necessary to find solutions allowing the cooperation between various repositories of learning resources. The user may seek resources out of his/her private reference ontology. The problem is that the comprehension of a new classification (a new ontology) is expensive and does not constitute a justified investment. It is thus necessary to propose mechanisms which allow the user to access to resources of other repositories in a transparent way using his/her favourite WBES.

The particularity of this approach is that (i) it focuses on dynamic ontology mapping using a multi-agent system, (ii) it uses the resources properties to enrich the local ontology by generating semantic relations between local concepts (iii) it is based
on inference rules to compare the ontologies concepts. These inference rules may be
general ones or more specific rules added by an expert. This flexibility allowed the
algorithm to be applied to other domains.

In this paper we introduce a dynamic mapping approach for bridging gaps between
learning object repositories based on ontologies. Dynamic ontology mapping means
that during a user interaction (query), the mapping system receives a sequence of
concepts and returns the most specific mapping for each concept.

## 2 Algorithm Principal

The objective of our approach is to map ontologies dynamically, and only when
needed. The system tries to find semantic relations between the concepts of the user
query and the concepts in the target ontologies.

The algorithm is based on a multi-agent system and it combines different similarity
measures to find mapping candidates between two ontologies. We distinguish three
main categories of similarity: linguistic similarity, structural similarity and rule-based
similarity. Using these different similarities may increase the precision of the results.

In this section we describe the global architecture and the agents' behaviours of the
mapping process.

### 2.1 Architecture

The ontology interoperability needs to define mapping between ontologies. In our
architecture (figure 1) the mapping process is split up into five levels: (i) resources
level contains the various resources repositories (ii) ontology level describes the
resources classification (iii) interface level where we can find, the users and a set of
the ontology agents (OA) which generate new ontologies enriched with semantic
relations, (iv) simulation level where we calculate the similarity between candidate
concepts and (v) domain expert level allows to a expert to interact with the simulation
level.

### 2.2 Mapping Process and Agents Behaviors

The algorithm begins by generating information from the ontology. The ontology
agent OA uses the instances (resources) comparisons for deducing semantic relations
between concepts of the same ontology to deduce a new ontology O+. The OA agent
which intercepts a user query generates all possible relations between the query
concepts and sends both concepts and these relations to all other OA agents.

The simulation level contains four agents: SCA (Similarity Computation Agent),
GHA (Generation Hypotheses Agent), FHA (Filtration Hypotheses Agent) and CHA
(Choice Hypotheses Agent). We describe in the following the general behaviour of
each simulation agent:

The SCA agent determines similarity values of candidate mappings via different
matchers. The first iteration consists in providing a basic similarity between concepts.
In this iteration we use linguistic tools [6, 7] to compare concepts' names. In the $i^{th}$
iteration we use the similarity produced in $(i-1)^{th}$ iteration and we apply the inference
rules. These inference rules are either rules inferred from structural similarity or rules
proposed by the domain expert.
<figure><img src="image_2.png" /><figcaption>Fig. 1. Architecture approach for mapping process</figcaption></figure>

The GHA agent receives all similarities sent by SCA and it generates hypotheses using inference rules. These hypotheses consist of new correspondences between concepts. The generation of a hypothesis at i^th^ iteration is based on either the mapping set or the similarities generated previously. Indeed, depending on the similarity value, we generate mapping hypotheses between the couple of concepts which have a similarity value enough important.

The FHA agent filters all hypotheses generated by GHA. The hypotheses which do not verify certain constraints (e.g. structural constraints) are removed. The subset of filtered hypotheses is sent to CHA agent.

The CHA agent selects the hypotheses which have the best similarity using both existing mapping and user's feedback.

The final mapping is sent to ontology agents. After several user interactions, each OA acquires more knowledge about other OA(s) and defines a set of most relevant OA(s) (i.e. the OA(s) that answer to its needs). This set is called "agent's accountancies".

## 3 Conclusion

Various works [3, 4, 5] have been developed for supporting the mapping of ontologies. Most of them are based on syntactic and semantic matching heuristics given by an expert to generate static mapping. None uses inference rules which can be generated for different application domains. In our mapping approach, we try to use as much as possible available information contained in the ontology to determine
dynamically and if necessary the relationship between concepts. This information
consists of identifier name of concepts, ontology structure, manual/automatic rules
and resources properties which generate new semantic relations between concepts of
the same ontology.

In future work, we plan to add other match and techniques in order to resolve more
complex mapping problems. The implementation of the prototype is in its first phase.
It will be followed by a series of the experiments to validate and justify the quality of
the approach in sharing learning resources.

References

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