Authors:
Rihab Idoudi
1
;
Karim Saheb Ettabaa
2
;
Kamel Hamrouni
3
and
Basel Solaiman
2
Affiliations:
1
Université Tunis ElManar and Telecom Bretagne, Tunisia
;
2
Telecom Bretagne, France
;
3
Université Tunis ElManar, Tunisia
Keyword(s):
Fuzzy C-Medoid, Ontology Aligning, Semantic Similarity, Similarity Measures.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Data Engineering
;
Enterprise Information Systems
;
Health Information Systems
;
Information Systems Analysis and Specification
;
Knowledge Engineering and Ontology Development
;
Knowledge Management
;
Knowledge-Based Systems
;
Ontologies and the Semantic Web
;
Ontology Engineering
;
Society, e-Business and e-Government
;
Symbolic Systems
;
Web Information Systems and Technologies
Abstract:
Recently, several ontologies have been proposed for real life domains, where these propositions are large and voluminous due to the complexity of the domain. Consequently, Ontology Aligning has been attracting a great deal of interest in order to establish interoperability between heterogeneous applications. Although, this research has been addressed, most of existing approaches do not well capture suitable correspondences when the size and structure vary vastly across ontologies. Addressing this issue, we propose in this paper a fuzzy clustering based alignment approach which consists on improving the ontological structure organization. The basic idea is to perform the fuzzy clustering technique over the ontology’s concepts in order to create clusters of similar concepts with estimation of medoids and membership degrees. The uncertainty is due to the fact that a concept has multiple attributes so to be assigned to different classes simultaneously. Then, the ontologies are aligned ba
sed on the generated fuzzy clusters with the use of different similarity techniques to discover correspondences between conceptual entities.
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