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.

• Type: Conference
• Year: 2007
• Where: SEMWEB
• Authors: Antoine Isaac, Lourens van der Meij, Stefan Schlobach, Shenghui Wang
• Keywords: Co-occurrence Measures, Different Co-occurrence Measures, SEMWEB 2007, Statistical Co-occurrence Measures