International Journal of Computational Intelligence Systems

Volume 9, Issue 5, September 2016, Pages 876 - 887

Ontology Knowledge Mining for Ontology Alignment

Authors
Rihab Idoudi1, 2, Karim Saheb Ettabaa2, Basel Solaiman2, Kamel Hamrouni1
1Université Tunis ElManar, Ecole Nationale d’Ingénieurs de Tunis, Tunis, 1200, Tunisie
2Telecom Bretagne, ITI Laboratory, 29238, Brest, France
Received 19 November 2015, Accepted 8 May 2016, Available Online 1 September 2016.
DOI
https://doi.org/10.1080/18756891.2016.1237187How to use a DOI?
Keywords
knowledge mining; Hierarchical Fuzzy clustering; Ontology Alignment; Similarity techniques
Abstract

As the ontology alignment facilitates the knowledge exchange among the heterogeneous data sources, several methods have been introduced in literature. Nevertheless, few of them have been interested in decreasing the problem complexity and reducing the research space of correspondences between the input ontologies.This paper presents a new approach for ontology alignment based on the ontology knowledge mining. The latter consists on producing for each ontology a hierarchical structure of fuzzy conceptual clusters, where a concept can belong to several clusters simultaneously. Each level of the hierarchy reflects the knowledge granularity degree of the knowledge base in order to improve the effectiveness and speediness of the information retrieval. Actually, such method allows the knowledge granularity analyze between the ontologies and facilitates several ontology engineering techniques. The ontology alignment process is performed iteratively over the produced hierarchical structure of the fuzzy clusters using semantic techniques. Once the correspondent clusters are identified, we consider both syntactic and structural characteristics of their correspondent entities. The proposed approach has been tested over the OAEI benchmark dataset and some real mammographic ontologies since this work is a part of CMCU project for Mammographic images analysis for Assistance Diagnostic Breast Cancer. The system performs good results in the terms of precision and recall with respect to other alignment system.

Copyright
© 2016. the authors. Co-published by Atlantis Press and Taylor & Francis
Open Access
This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

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Journal
International Journal of Computational Intelligence Systems
Volume-Issue
9 - 5
Pages
876 - 887
Publication Date
2016/09/01
ISSN (Online)
1875-6883
ISSN (Print)
1875-6891
DOI
https://doi.org/10.1080/18756891.2016.1237187How to use a DOI?
Copyright
© 2016. the authors. Co-published by Atlantis Press and Taylor & Francis
Open Access
This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

Cite this article

TY  - JOUR
AU  - Rihab Idoudi
AU  - Karim Saheb Ettabaa
AU  - Basel Solaiman
AU  - Kamel Hamrouni
PY  - 2016
DA  - 2016/09/01
TI  - Ontology Knowledge Mining for Ontology Alignment
JO  - International Journal of Computational Intelligence Systems
SP  - 876
EP  - 887
VL  - 9
IS  - 5
SN  - 1875-6883
UR  - https://doi.org/10.1080/18756891.2016.1237187
DO  - https://doi.org/10.1080/18756891.2016.1237187
ID  - Idoudi2016
ER  -