James Geller, Huanying Gu, and Michael Halper. Semantic refinement and error correction in large terminological knowledge bases, Data & Knowledge Engineering, 45(1), 2003, pp. 1-32.

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.