An empirical study of instance-based ontology matching. Isaac, A., van der Meij, L., Schlobach, S., & Wang, S. In Proceedings of the 20th Belgium-Netherlands Conference on Artificial Intelligence (BNAIC 2008), volume 4825, of Lecture Notes in Computer Science, pages 253-266, 2007. Springer.
Website abstract bibtex 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 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. 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.
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