Ontology Knowledge Mining for Ontology Alignment

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 in...

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Main Authors: Rihab Idoudi, Karim Saheb Ettabaa, Basel Solaiman, Kamel Hamrouni
Format: Article
Language:English
Published: Atlantis Press 2016-09-01
Series:International Journal of Computational Intelligence Systems
Subjects:
Online Access:https://www.atlantis-press.com/article/25868735/view
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spelling doaj-97949b23e2484717a8001d9d42b9a4da2020-11-25T01:42:38ZengAtlantis PressInternational Journal of Computational Intelligence Systems 1875-68832016-09-019510.1080/18756891.2016.1237187Ontology Knowledge Mining for Ontology AlignmentRihab IdoudiKarim Saheb EttabaaBasel SolaimanKamel HamrouniAs 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.https://www.atlantis-press.com/article/25868735/viewknowledge miningHierarchical Fuzzy clusteringOntology AlignmentSimilarity techniques
collection DOAJ
language English
format Article
sources DOAJ
author Rihab Idoudi
Karim Saheb Ettabaa
Basel Solaiman
Kamel Hamrouni
spellingShingle Rihab Idoudi
Karim Saheb Ettabaa
Basel Solaiman
Kamel Hamrouni
Ontology Knowledge Mining for Ontology Alignment
International Journal of Computational Intelligence Systems
knowledge mining
Hierarchical Fuzzy clustering
Ontology Alignment
Similarity techniques
author_facet Rihab Idoudi
Karim Saheb Ettabaa
Basel Solaiman
Kamel Hamrouni
author_sort Rihab Idoudi
title Ontology Knowledge Mining for Ontology Alignment
title_short Ontology Knowledge Mining for Ontology Alignment
title_full Ontology Knowledge Mining for Ontology Alignment
title_fullStr Ontology Knowledge Mining for Ontology Alignment
title_full_unstemmed Ontology Knowledge Mining for Ontology Alignment
title_sort ontology knowledge mining for ontology alignment
publisher Atlantis Press
series International Journal of Computational Intelligence Systems
issn 1875-6883
publishDate 2016-09-01
description 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.
topic knowledge mining
Hierarchical Fuzzy clustering
Ontology Alignment
Similarity techniques
url https://www.atlantis-press.com/article/25868735/view
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AT baselsolaiman ontologyknowledgeminingforontologyalignment
AT kamelhamrouni ontologyknowledgeminingforontologyalignment
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