Using CBR to Drive IR

Abstract

We discuss the use of Case-Based Reasoning (CBR) to drive an Information Retrieval (IR) system. Our hybrid CBR-IR approach takes as input a standard frame-based representation of a problem case, and outputs texts of relevant cases retrieved from a document corpus dramatically larger than the case base available to the CBR system. While the smaller case base is accessible by the usual case-based indexing, and is amenable to knowledge-intensive methods, the larger IR corpus is not. 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. Our system works by first performing a standard HYPO-style CBR analysis, and then using texts associated with certain important cases found in this analysis to "seed" a modified version of IN QUERY's relevance feedback mechanism in order to generate a query. We describe our approach and report on experiments performed in two different legal domains.