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JAMIA 2009;16:346-353 doi:10.1197/jamia.M2931
  • Original Investigation
  • Research Paper

Using Semantic and Structural Properties of the Unified Medical Language System to Discover Potential Terminological Relationships

  1. Chintan O Patela,
  2. James J Ciminoa,b
  1. aDepartment of Biomedical Informatics, Columbia University, New York, NY
  2. bLaboratory for Informatics Development, NIH Clinical Center Bethesda, MD
  1. Correspondence: Chintan O. Patel, Department of Biomedical Informatics, Columbia University, Vanderbilt Clinic, 5th Floor, 622 West 168th Street, New York, NY 10032; e-mail: <chintan.patel{at}dbmi.columbia.edu>
  • Received 17 July 2008
  • Accepted 15 January 2009

Abstract

Objective To use the semantic and structural properties in the Unified Medical Language System (UMLS) Metathesaurus to characterize and discover potential relationships.

Design The UMLS integrates knowledge from several biomedical terminologies. This knowledge can be used to discover implicit semantic relationships between concepts. In this paper, the authors propose a problem-independent approach for discovering potential terminological relationships that employs semantic abstraction of indirect relationship paths to perform classification and analysis of network theoretical measures such as topological overlap, preferential attachment, graph partitioning, and number of indirect paths. Using different versions of the UMLS, the authors evaluate the proposed approach's ability to predict newly added relationships.

Measurements Classification accuracy, precision-recall.

Results Strong discriminative characteristics were observed with a semantic abstraction based classifier (classification accuracy of 91%), the average number of indirect paths, preferential attachment, and graph partitioning to identify potential relationships. The proposed relationship prediction algorithm resulted in 56% recall in top 10 results for new relationships added to subsequent versions of the UMLS between 2005 and 2007.

Conclusions The UMLS has sufficient knowledge to enable discovery of potential terminological relationships.

Footnotes

  • ↵† Throughout this paper, controlled terms will be presented in italics.

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