Semantic refinement and error correction in large terminological knowledge bases

  • Authors:

  • James Geller;Huanying Gu;Yehoshua Perl;Michael Halper

  • Affiliations:

  • CS Department, New Jersey Institute of Technology, 323 Dr. King Blvd., Newark, NJ;Department of Health Informatics, University of Medicine and Dentitry, Newark, NJ;CS Department, New Jersey Institute of Technology, 323 Dr. King Blvd., Newark, NJ;Mathematics & Computer Science Department, Kean University, Union, NJ

  • Venue:
  • Data & Knowledge Engineering
  • Year:
  • 2003

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Abstract

Capturing the semantics of concepts in a terminology has been an important problem in AI. A two-level approach has been proposed where concepts are classified into high-level semantic types, with these types constituting a portion of the concepts' semantics. We present an algorithmic methodology for refining such two-level terminologic networks. A new network is produced consisting of "pure" semantic types and intersection types. Concepts are uniquely re-assigned to these new types. Overall, these types form a better conceptual abstraction, with each exhibiting uniform semantics. Using them, it becomes easier to detect classification errors. The methodology is applied to the UMLS.