Ontology knowledge mining for ontology conceptual enrichment. Issue 2 (3rd April 2019)

• Record Type: Journal Article
• Authors: Idoudi, Rihab; Saheb Ettabaa, Karim; Solaiman, Basel; Hamrouni, Kamel
• Is Part Of: Knowledge management research & practice. Volume 17:Issue 2(2019)
• Page Start: 151
• Page End: 160
• Publication Date: 2019-04-03
• Subjects: Hierarchical Fuzzy clustering -- ontology -- alignment -- semantic similarity; Organizational learning -- Periodicals; Intellectual capital -- Periodicals; Knowledge management -- Periodicals; 658.403
• DOI: 10.1080/14778238.2018.1538599
• Languages: English
• ISSNs: 1477-8238

Abstract

ABSTRACT: Actually, to accomplish knowledge sharing, specific parts derived from existing ontological resources are employed. Therefore, several researchers have been interested in merging these knowledge-bases by enriching target ontology with novel knowledge coming from source ones, they use either statistical models or expert's intervention to provide the relevance and placement of new concepts. Nevertheless, real world ontologies are large size, thus, the enrichment/merging process turns to be time consuming and hard to handle. To cope with these limitations, we propose an ontology knowledge mining based approach for ontology conceptual enrichment. First we reorganize both ontological structures by defining hierarchies of reduced conceptual clusters grouping similar concepts of targeted thematic. Then, we proceed to align both hierarchical structures to detect similar clusters. Finally, we proceed to enrich the source hierarchy with different clusters of the target structure. The results of tests performed with our method on real domain ontologies show their effectiveness.