Evidence-driven retrieval in textual CBR: Bridging the gap between retrieval and reuse

Abstract

The most similar case may not always be the most appropriate one to guide a problem-solving process. It is often important that a retrieved past case can be easily adapted to a target problem. The presented work deals with the retrieval and adaptation in textual casebased reasoning (TCBR) where cases are described textually. In TCBR, it is common to use similarity-based retrieval methods from information retrieval where adaptability of the retrieved cases is not considered. In this paper we introduce a novel case retrieval method called evidencedriven retrieval (EDR). It uses the notion of evidence to determine which parts of the new problem text have been useful in the past solutions and will be used in the adaptation to a new problem. This allows EDR to retrieve cases that are not only similar but also adaptable. We evaluated EDR as part of our TCBR approach that aims to support human experts in root cause analysis of transportation incidents. This approach relies on causal knowledge automatically extracted from incident reports from the Transportation Safety Board of Canada, which are used as textual cases in our experiments. The results for EDR are compared with information retrieval methods traditionally applied in TCBR.