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An Empirical Study of Instance-Based Ontology Matching

  • Antoine Isaac
  • Lourens van der Meij
  • Stefan Schlobach
  • Shenghui Wang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4825)

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.

Keywords

Information Gain Mapping Index Ontology Mapping Pointwise Mutual Information Equivalent Concept 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Antoine Isaac
    • 1
    • 2
  • Lourens van der Meij
    • 1
    • 2
  • Stefan Schlobach
    • 1
  • Shenghui Wang
    • 1
    • 2
  1. 1.Vrije Universiteit Amsterdam 
  2. 2.Koninklijke Bibliotheek, Den Haag 

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