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Incremental iterative retrieval and browsing for efficient conversational CBR systems
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DOI：10.1023/A:1008375309626.png)
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摘要
En 中文
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. This paper considers alternative approaches for implementing iterative browsing in conversational CBR systems. First, we discuss a naive algorithm, which evaluates each query independent of earlier evaluations. Second, we introduce an incremental algorithm, which reuses the results of past query evaluations to minimize the computation required for subsequent queries. In particular, the paper proposes an efficient algorithm for case base browsing and retrieval using database techniques for incremental view maintenance. In addition, the paper evaluates scalability of the proposed algorithm using its performance model. The model is created using algorithmic complexity and experimental evaluation of the system performance.
Keyword:
knowledge base technology
case-based reasoning
performance evaluation
context-based iterative browsing and retrieval
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