logo
CanyamAI summaries for academic research
language
MobileMobile
  • Phone FeatureWeChat Mini Program
EnglishEnglish
noticeNotice
BlogBlog
delete
arrow
Return

Knowledge modelling for a generic refinement framework

delete1999-10-01
delete2
delete
OA
AI
S
Susan Craw
DOI：10.1016/S0950-7051(99)00018-0delete
deleteOriginal
deleteShare
deleteSave
View PDF
Abstract

Abstract

En 中文
Refinement tools assist with debugging the knowledge-based system (KBS), thus easing the well-known knowledge acquisition bottleneck, and the more recently recognised maintenance overhead. The existing refinement tools were developed for specific rule-based KBS environments, and have usually been applied to artificial or academic applications. Hence, there is a need for tools which are applicable to industrial applications. However, it would be wasteful to develop separate refinement tools for individual shells; instead, the KRUSTWorks project is developing reusable components applicable to a variety of KBS environments. This paper develops a knowledge representation that embodies a KBS's rulebase and its reasoning, and permits the implementation of core refinement procedures, which are generally applicable and can ignore KBS-specific details. Such a representation is an essential stage in the construction of a generic automated knowledge refinement framework, such as KRUSTWorks. Experience from applying this approach to CLIPS, POWERMODEL and PFES KBSs indicates its feasibility for a wider variety of industrial KBSs. (C) 1999 Elsevier Science B.V. All rights reserved.
Keywords:
knowledge refinement
knowledge representation
knowledge acquisition
AI Summary

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.

Journal

K
Knowledge-Based Systems
IF:
7.6
Papers:
1.2W
Citations:
4.5W

Organization

No organization information available