Semantic refinement and error correction in large terminological knowledge bases
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. © 2002 Elsevier Science B.V. All rights reserved.
Data and Knowledge Engineering
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Geller, James; Gu, Huanying; Perl, Yehoshua; and Halper, Michael, "Semantic refinement and error correction in large terminological knowledge bases" (2003). Kean Publications. 2693.