Incremental Iterative Retrieval and Browsing for Efficient Conversational CBR Systems

被引:0
|
作者
Igor Jurisica
Janice Glasgow
John Mylopoulos
机构
[1] University of Toronto,Faculty of Information Studies
[2] Queen's University,Department of Computing and Information Science
[3] University of Toronto,Department of Computer Science
来源
Applied Intelligence | 2000年 / 12卷
关键词
knowledge base technology; case-based reasoning; performance evaluation; context-based iterative browsing and retrieval;
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学科分类号
摘要
A case base is a repository of past experiences that can be used for problem solving. Given a new problem, expressed in the form of a query, the case base is browsed in search of “similar” or “relevant” cases. Conversational case-based reasoning (CBR) systems generally support user interaction during case retrieval and adaptation. Here we focus on case retrieval where users initiate problem solving by entering a partial problem description. During an interactive CBR session, a user may submit additional queries to provide a “focus of attention”. These queries may be obtained by relaxing or restricting the constraints specified for a prior query. Thus, case retrieval involves the iterative evaluation of a series of queries against the case base, where each query in the series is obtained by restricting or relaxing the preceding query.
引用
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页码:251 / 268
页数:17
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