Exploiting Taxonomic and Causal Relations in Conversational Case Retrieval

Abstract

Conversational case-based reasoning (CCBR) systems engage their users in a series of questions and answers and present them with cases that are most applicable to their decision problem. In previous research, we introduced the Taxonomic CCBR methodology, an extension of standard CCBR that improved performance by organizing features related by abstraction into taxonomies. We recently extended this methodology to include causal relations between taxonomies and claimed that it could yield additional performance gains. In this paper, we formalize the causal extension of Taxonomic CCBR, called Causal CCBR, and empirically assess its benefits using a new methodology for evaluating CCBR performance. Evaluation of Taxonomic and Causal CCBR systems in troubleshooting and customer support domains demonstrates that they significantly outperform the standard CCBR approach. In addition, Causal CCBR outperforms Taxonomic CCBR to the extent causal relations are incorporated in the case bases.