Knowledge engineering: principles and methods
Data & Knowledge Engineering - Special jubilee issue: DKE 25
Ontology Matching
Integration of Ontology Data through Learning Instance Matching
WI '06 Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence
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This paper introduces an ontology mapping approach based on word and context similarity (WCONS) to find equivalence relation between concepts from two different ontologies, using Levenshtein distance and Tversky’s similarity model. The context of each concept is expanded to four kinds of facet contexts for context similarity computing, which are structure facet context, relation facet context, attribute facet context and instance facet context. A preliminary experiment is then conducted using ontologies #101, #301, #302, #303 and #304 in benchmark suite of OAEI 2007, indicating that WCONS can be evidently helpful to discovering semantic mappings for ontology integration