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The synergistic application of CBR to IR

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Abstract

In this paper we discuss a hybrid approach combining Case-Based Reasoning (CBR) and Information Retrieval (IR) for the retrieval of full-text documents. Our hybrid CBR-IR approach takes as input a standard symbolic representation of a problem case and retrieves texts of relevant cases from a document collection dramatically larger than the case base available to the CBR system. Our system works by first performing a standard HYPO-style CBR analysis and then using the texts associated with certain important classes of cases found in this analysis to “seed” a modified version of INQUERY's relevance feedback mechanism in order to generate a query composed of individual terms or pairs of terms. Our approach provides two benefits: it extends the reach of CBR (for retrieval purposes) to much larger corpora, and it enables the injection of knowledge-based techniques into traditional IR. We describe our CBR-IR approach and report on on-going experiments.

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Authors and Affiliations

  1. Department of Computer Science, University of Massachusetts, 01003, Amherst, MA

    Edwina L. Rissland & Jody J. Daniels

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  1. Edwina L. Rissland
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  2. Jody J. Daniels
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Additional information

This research was supported by NSF Grant no. EEC-9209623, State/Industry/University Cooperative Research on Intelligent Information Retrieval, Digital Equipment Corporation, and the National Center for Automated Information Research.

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Rissland, E.L., Daniels, J.J. The synergistic application of CBR to IR. Artif Intell Rev 10, 441–475 (1996). https://doi.org/10.1007/BF00130694

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