Measures of semantic similarity and relatedness in the biomedical domain

  • Authors:

  • Ted Pedersen;Serguei V. S. Pakhomov;Siddharth Patwardhan;Christopher G. Chute

  • Affiliations:

  • Department of Computer Science, 1114 Kirby Drive, University of Minnesota, Duluth, MN 55812, USA;Division of Biomedical Informatics, Mayo College of Medicine, Rochester, MN, USA;School of Computing, University of Utah, Salt Lake City, UT, USA;Division of Biomedical Informatics, Mayo College of Medicine, Rochester, MN, USA

  • Venue:
  • Journal of Biomedical Informatics
  • Year:
  • 2007

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Abstract

Measures of semantic similarity between concepts are widely used in Natural Language Processing. In this article, we show how six existing domain-independent measures can be adapted to the biomedical domain. These measures were originally based on WordNet, an English lexical database of concepts and relations. In this research, we adapt these measures to the SNOMED-CT^(R) ontology of medical concepts. The measures include two path-based measures, and three measures that augment path-based measures with information content statistics from corpora. We also derive a context vector measure based on medical corpora that can be used as a measure of semantic relatedness. These six measures are evaluated against a newly created test bed of 30 medical concept pairs scored by three physicians and nine medical coders. We find that the medical coders and physicians differ in their ratings, and that the context vector measure correlates most closely with the physicians, while the path-based measures and one of the information content measures correlates most closely with the medical coders. We conclude that there is a role both for more flexible measures of relatedness based on information derived from corpora, as well as for measures that rely on existing ontological structures.