An empirical study of instance-based ontology matching

From semanticweb.org

A paper written by Lourens Van der Meij, Antoine Isaac, Shenghui Wang and Stefan Schlobach. It was presented at the ISWC2007+ASWC2007.

[edit] Abstract

Instance-based ontology mapping is a promising family of solutions to a class of ontology alignment problems. Instance-based ontology mapping 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 co-occurrence 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, and 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.

A linked list of all papers is provided in the article on ISWC2007+ASWC2007 papers. This article has originally been created from the ISWC 2007/ASWC 2007 metadata.