Abstract
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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Isaac, A., van der Meij, L., Schlobach, S., Wang, S. (2007). An Empirical Study of Instance-Based Ontology Matching. In: Aberer, K., et al. The Semantic Web. ISWC ASWC 2007 2007. Lecture Notes in Computer Science, vol 4825. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76298-0_19
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DOI: https://doi.org/10.1007/978-3-540-76298-0_19
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