Ontology Knowledge Mining for Ontology Alignment. Issue 5 (2nd September 2016)
- Record Type:
- Journal Article
- Title:
- Ontology Knowledge Mining for Ontology Alignment. Issue 5 (2nd September 2016)
- Main Title:
- Ontology Knowledge Mining for Ontology Alignment
- Authors:
- Idoudi, Rihab
Ettabaa, Karim Saheb
Solaiman, Basel
Hamrouni, Kamel - Abstract:
- 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 withAbstract: 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. … (more)
- Is Part Of:
- International journal of computational intelligence systems. Volume 9:Issue 5(2016)
- Journal:
- International journal of computational intelligence systems
- Issue:
- Volume 9:Issue 5(2016)
- Issue Display:
- Volume 9, Issue 5 (2016)
- Year:
- 2016
- Volume:
- 9
- Issue:
- 5
- Issue Sort Value:
- 2016-0009-0005-0000
- Page Start:
- 876
- Page End:
- 887
- Publication Date:
- 2016-09-02
- Subjects:
- knowledge mining -- Hierarchical Fuzzy clustering -- Ontology Alignment -- Similarity techniques
Computational intelligence -- Periodicals
006.305 - Journal URLs:
- http://link.springer.com/ ↗
- DOI:
- 10.1080/18756891.2016.1237187 ↗
- Languages:
- English
- ISSNs:
- 1875-6891
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - BLDSS-3PM
British Library STI - ELD Digital store - Ingest File:
- 1400.xml