=Paper=
{{Paper
|id=None
|storemode=property
|title=Proposal of a New Approach for Ontology Modularization
|pdfUrl=https://ceur-ws.org/Vol-875/short_paper_1.pdf
|volume=Vol-875
|dblpUrl=https://dblp.org/rec/conf/womo/SouissiCG12
}}
==Proposal of a New Approach for Ontology Modularization==
https://ceur-ws.org/Vol-875/short_paper_1.pdf
Proposal of New Approach for Ontology Modularization
Amir Souissi1, Walid Chainbi2 and Khaled Ghedira3
1
Ecole Nationale des Sciences de l’Informatique /SOIE - Manouba - Tunisia
Amir.souissi@planet.tn
2
Sousse National School of Engineers /SOIE, Sousse 4054 - Tunisia
Walid.chainbi@gmail.com
3
Institut supérieur de gestion de Tunis /SOIE - Tunisia
Khaled.ghedira@isg.rnu.tn
Ontologies have established themselves as a powerful tool to enable knowledge
sharing, and a growing number of applications have benefited from the use of ontolo-
gies as a means to achieve semantic interoperability among heterogeneous, distributed
systems [1]. With the evolution of cooperative and distributed systems, and the emer-
gence of the semantic Web, ontologies have become an indispensable resource. The
number of ontologies available on the Web has also increased due to the appearance
of several tools that assist users in creating their ontologies. This has posed problems
of understanding and reuse of those resources already difficult to design. A solution
was then proposed by the knowledge engineers namely modularization. Ontology
modularization is crucial to support knowledge reuse on the ever increasing semantic
Web [2]. However, modularization methods that serve the reuse goal are often in-
tended for humans to assist them in building new ontologies, rather than for applica-
tions that need only a relevant part of an existing ontology. Moreover, modules ob-
tained are always subject to verification and maintenance by humans to validate the
semantic consistency of their contents. Unlike previous studies, we investigate in this
paper how a modularization based on semantic comparison, may provide a module
directly reusable by the application that requests it. Our contribution is twofold. On
the one hand, it allows an application to extract and use a module that covers a sub-
domain from an ontology that covers a wider knowledge area, regardless of its struc-
ture and the formalism with which it is expressed. On the other hand, the user is re-
lieved from manually estimating the meaning of the components of the ontology, after
the modularization process.
The modularization approach we propose is part of the decomposition approaches
of monolithic ontologies [3,4]. It is an extraction method since
it aims to extract a relevant ontology module. The method should allow the user to
express its needs by entering the concepts which interest him. The result is a fragment
composed of concepts and relations that are relevant to the module i.e., which are in
strong semantic relationship with the concepts submitted by the user. We define a
strong semantic relationship between two concepts, as one of the six logic functions
as follows:
─ Identity relationship: it is a semantic relation between two concepts that have the
same syntax, the same attributes and operations. Example: Identity (Person, Per-
son).
─ Synonymy relationship: it is a semantic relation between two concepts that express
the same meaning. Example: Synonymy (Person, Individual).
─ Classification Is-a relationship: two concepts where one is expressing a particular
case of the other. Example: Is-a (Student, Person).
─ Homonymy relationship: the same concept can have two different meanings. Ex-
ample: Homonymy (Bug, Bug). The first one means an insect. The second one
means a fault in a computer system.
─ Equivalence relationship: a semantic relationship between two concepts that play
the same role. Example: Equivalence (Teacher, Professor).
─ Antonymy relationship: is used between two concepts totally disjoint. Example:
Antonymy (Registered, Visitor).
For example, in an ontology that describes the human anatomy, the user is only in-
terested in the anatomy of the foot. The method should extract a coherent module,
semantically rich on the foot, from the ontology of departure.
Our approach is based on two basic steps:
─ 1st step: Identifying concepts that are in strong semantic relationship with external
concepts.
─ 2nd step: composition of the module based on the concepts identified in Step 1. All
concepts that appear in the definition of the concepts identified are considered part
of the module.
The goal is to allow a program to extract automatically a single part of an ontology
without human intervention and without restrictions on the ontology structure. This
will help programs to satisfy their requirements by reusing directly ontology portions.
References
1. Chainbi, W.: An Ontology Based Multi-Agent System Conceptual Model, In Special Issue
of the International Journal of Computer Applications in Technology, Inderscience Pub-
lishers, Vol. 31, Nos. 1/2, pp. 35-44, (2008)
2. Cuenca Grau,B., Horrocks, I., Kazakov, Y., Sattler, U.: Just the Right Amount: Extracting
Modules from Ontologies. In International World Wide Web conference, (2007)
3. Seidenberg, J.: Web Ontology Segmentation: Extraction, Transformation, Evaluation. In
Heiner Stuckenschmidt, Christine Parent, Stefano Spaccapietra (Eds.), Modular Ontologies
concepts, Theories and Techniques for Knowledge Modularization, Springer-Verlag Ber-
lin, Heidelberg, LNCS 5445, pages 211-243, (2009)
4. Stuckenschmidt, H., Klein, M.: Strutured-based Partitioning of large concept hierarchies.
In Proceedings of the 3rd International Semantic Web Conference, (2004)