Reducing CLASSIC to practice: Knowledge representation theory meets reality

被引:16
作者
Brachman, RJ
McGuinness, DL
Patel-Schneider, PF
Borgida, A
机构
[1] AT&T Labs Res, Florham Pk, NJ 07932 USA
[2] Stanford Univ, Knowlege Syst Lab, Stanford, CA 94305 USA
[3] Bell Labs, Database Syst Res Dept, Murray Hill, NJ 07974 USA
[4] Rutgers State Univ, Dept Comp Sci, Piscataway, NJ 08855 USA
关键词
knowledge representation; description logics; KL-One; CLASSIC; AI applications; technology transfer;
D O I
10.1016/S0004-3702(99)00078-8
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Most recent key developments in research on knowledge representation (KR) have been of the more theoretical sort, involving worst-case complexity results, solutions to technical challenge problems, etc. While some of this work has influenced practice in Artificial Intelligence, it is rarely-if ever-made clear what is compromised when the transition is made from relatively abstract theory to the real world. CLASSIC is a description logic with an ancestry of extensive theoretical work (tracing back over twenty years to KL-ONE), and several novel contributions to KR theory. Basic research on CLASSIC paved the way for an implementation that has been used significantly in practice, including by users not versed in KR theory. In moving from a pure logic to a practical tool, many compromises and changes of perspective were necessary. We report on this transition and articulate some of the profound influences practice can have on relatively idealistic theoretical work. We have found that CLASSIC has been quite useful in practice, yet still strongly retains most of its original spirit, but much of our thinking and many details had to change along the way. (C) 1999 Published by Elsevier Science B.V. All rights reserved.
引用
收藏
页码:203 / 237
页数:35
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