"Semantic refinement and error correction in large terminological knowl" by James Geller, Huanying Gu et al.
 

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.

Publication Title

Data and Knowledge Engineering

First Page Number

1

Last Page Number

32

DOI

10.1016/S0169-023X(02)00153-2

This document is currently not available here.

Share

COinS