An empirical study of instance-based ontology matching

  • Authors:

  • Antoine Isaac;Lourens Van Der Meij;Stefan Schlobach;Shenghui Wang

  • Affiliations:

  • Vrije Universiteit Amsterdam and Koninklijke Bibliotheek, Den Haag;Vrije Universiteit Amsterdam and Koninklijke Bibliotheek, Den Haag;Vrije Universiteit Amsterdam;Vrije Universiteit Amsterdam and Koninklijke Bibliotheek, Den Haag

  • Venue:
  • ISWC'07/ASWC'07 Proceedings of the 6th international The semantic web and 2nd Asian conference on Asian semantic web conference
  • Year:
  • 2007

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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.