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2007
Springer

An Empirical Study of Instance-Based Ontology Matching

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An Empirical Study of Instance-Based Ontology Matching
Instance-based ontology mapping is a promising family of solutions to a class of ontology alignment problems. It crucially depends on measuring the similarity between sets of annotated instances. In this paper we study how the choice of co-occurrence measures affects the performance of instance-based mapping. To this end, we have implemented a number of different statistical cooccurrence measures. We have prepared an extensive test case using vocabularies of thousands of terms, millions of instances, and hundreds of thousands of co-annotated items. We have obtained a human Gold Standard judgement for part of the mapping-space. We then study how the different co-occurrence measures and a number of algorithmic variations perform on our benchmark dataset as compared against the Gold Standard. Our systematic study shows excellent results of instance-based matching in general, where the more simple measures often outperform more sophisticated statistical co-occurrence measures.
Antoine Isaac, Lourens van der Meij, Stefan Schlob
Added 09 Jun 2010
Updated 09 Jun 2010
Type Conference
Year 2007
Where SEMWEB
Authors Antoine Isaac, Lourens van der Meij, Stefan Schlobach, Shenghui Wang
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