KNOWLEDGE ELICITATION AND KNOWLEDGE REPRESENTATION IN A LARGE DOMAIN WITH MULTIPLE EXPERTS

被引:15
|
作者
BARRETT, AR [1 ]
EDWARDS, JS [1 ]
机构
[1] ASTON UNIV,ASTON BUSINESS SCH,ASTON TRIANGLE,BIRMINGHAM B4 7ET,ENGLAND
关键词
D O I
10.1016/0957-4174(94)E0007-H
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This paper describes the knowledge elicitation and knowledge representation aspects of a system being developed to help with the design and maintenance of relational data bases. The size and complexity of this domain mean that the system requires both knowledge-based and conventional algorithmic components. In addition, the domain contains multiple experts, but any given expert's knowledge of this large domain is only partial. The paper discusses the methods and techniques used for knowledge elicitation, which was based on a ''broad and shallow'' approach at first, moving to a ''narrow and deep'' one later, and describes the models used for knowledge representation, which were based on a layered ''generic and variants'' approach.
引用
收藏
页码:169 / 176
页数:8
相关论文
共 50 条