Semantic refinement and error correction in large terminological knowledge bases

Research output: Contribution to journalArticlepeer-review

33 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)1-32
Number of pages32
JournalData and Knowledge Engineering
Issue number1
StatePublished - Apr 2003

All Science Journal Classification (ASJC) codes

  • Information Systems and Management


  • Concept hierarchy
  • Semantic error correction
  • Semantic refinement
  • Semantic type
  • Terminological knowledge base


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