On the Merging of Domain-Specific Heterogeneous Ontologies Using WordNet and Web Pattern-Based Queries

Maree Mohammed () and Belkhatir Mohammed ()
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Maree Mohammed: Faculty of Information Technology, Monash University, Malaysia
Belkhatir Mohammed: Faculty of Computer Science, University of Lyon & CNRS, France

Journal of Information & Knowledge Management (JIKM), 2011, vol. 10, issue 01, 23-36

Abstract: Ontologies are a basic interest in various computer science disciplines such as semantic web, information retrieval, database design, etc. They aim at providing a formal, explicit and shared conceptualisation and understanding of common domains between different communities. In addition, they allow for concepts and their constraints of a specific domain to be explicitly defined. However, the distributed nature of ontology development and the differences in viewpoints of ontology engineers have resulted in the so-called "semantic heterogeneity" between ontologies. Semantic heterogeneity constitutes the major obstacle against achieving interoperability between ontologies. To overcome this obstacle, we present a multipurpose framework which exploits the WordNet generic knowledge base for the following purposes: (i) discovering and correcting the incorrect semantic relations between the concepts of the ontology in a specific domain, this step is a primary step of ontology merging; (ii) merging domain-specific ontologies through computing semantic relations between their concepts; (iii) handling the issue ofmissing conceptsin WordNet through the acquisition of statistical information on the Web; (iv) enriching WordNet with these missing concepts. An experimental instantiation of the framework and comparisons with state-of-the-art syntactic- and semantic-based systems validate our proposal.

Keywords: Knowledge management and representation; ontology merging; semantic and web-based statistical techniques; WordNet; precision/recall experimental evaluation (search for similar items in EconPapers)
Date: 2011
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DOI: 10.1142/S0219649211002808

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