Semantic refinement and error correction in large terminological knowledge bases

Document Type

Article

Publication Date

4-1-2003

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. © 2002 Elsevier Science B.V. All rights reserved.

Identifier

0037375448 (Scopus)

Publication Title

Data and Knowledge Engineering

External Full Text Location

https://doi.org/10.1016/S0169-023X(02)00153-2

ISSN

0169023X

First Page

1

Last Page

32

Issue

1

Volume

45

Fund Ref

National Institute of Standards and Technology

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