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Matching Unstructured Vocabularies Using a Background Ontology

  • Zharko Aleksovski
  • Michel Klein
  • Warner ten Kate
  • Frank van Harmelen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4248)

Abstract

Existing ontology matching algorithms use a combination of lexical and structural correspondence between source and target ontologies. We present a realistic case-study where both types of overlap are low: matching two unstructured lists of vocabulary used to describe patients at Intensive Care Units in two different hospitals. We show that indeed existing matchers fail on our data. We then discuss the use of background knowledge in ontology matching problems. In particular, we discuss the case where the source and the target ontology are of poor semantics, such as flat lists, and where the background knowledge is of rich semantics, providing extensive descriptions of the properties of the concepts involved. We evaluate our results against a Gold Standard set of matches that we obtained from human experts.

Keywords

Background Knowledge Target Concept Semantic Match Ontology Match Ontology Alignment 
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 2006

Authors and Affiliations

  • Zharko Aleksovski
    • 1
    • 2
  • Michel Klein
    • 2
  • Warner ten Kate
    • 1
  • Frank van Harmelen
    • 2
  1. 1.Philips ResearchEindhoven
  2. 2.Vrije UniversiteitAmsterdam

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