Incremental Iterative Retrieval and Browsing for Efficient Conversational CBR Systems

作者:Igor Jurisica, Janice Glasgow, John Mylopoulos

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.

论文关键词:knowledge base technology, case-based reasoning, performance evaluation, context-based iterative browsing and retrieval

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论文官网地址:https://doi.org/10.1023/A:1008375309626