International Conference on Case-Based Reasoning

Case-Based Reasoning Research and Development pp 351-365 | Cite as

Evidence-Driven Retrieval in Textual CBR: Bridging the Gap Between Retrieval and Reuse

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9343)

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 case-based 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 evidence-driven 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.

Keywords

Textual CBR Incident analysis Causal relations  Adaptation-guided retrieval Adaptation 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of Computer ScienceNorwegian University of Science and TechnologyTrondheimNorway

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