Presented at: 7th International Semantic Web Conference (ISWC2008)
by Shenghui Wang, Gwenn Englebienne, Stefan Schlobach
Finding mappings between compatible ontologies is an important but difficult open problem. Instance-based methods for solving this problem have the advantage of focusing on the most active parts of the ontologies and reflect concept semantics as they are used in the real world. However such methods have not at present been widely investigated in ontology mapping, compared to linguistic and structural techniques. In this paper we approach the mapping problem as a classification problem based on the similarity between instances of concepts. We evaluate the resulting classifier on three different real-world data sets.
Keywords: Classification, Machine learning, Ontology, Similarity, Thesaurus mapping, Semantic Web
Resource URI on the dog food server: http://data.semanticweb.org/conference/iswc/2008/paper/research/229
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