Fast Case Retrieval Nets for Textual Data

Case Retrieval Networks (CRNs) facilitate flexible and efficient retrieval in Case-Based Reasoning (CBR) systems. While CRNs scale up well to handle large numbers of cases in the case-base, the retrieval efficiency is still critically determined by the number of feature values (referred to as Information Entities) and by the nature of similarity relations defined over the feature space. In textual domains it is typical to perform retrieval over large vocabularies with many similarity interconnections between words. This can have adverse effects on retrieval efficiency for CRNs. This paper proposes an extension to CRN, called the Fast Case Retrieval Network (FCRN) that eliminates redundant computations at run time. Using artificial and real-world datasets, it is demonstrated that FCRNs can achieve significant retrieval speedups over CRNs, while maintaining retrieval effectiveness.

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