Taxonomic Conversational Case-Based Reasoning

Conversational Case-Based Reasoning (CCBR) systems engage a user in a series of questions and answers to retrieve cases that solve his/her current problem. Help-desk and interactive troubleshooting systems are among the most popular implementations of the CCBR methodology. As in traditional CBR systems, features in a CCBR system can be expressed at varying levels of ion. In this paper, we identify the sources of abstraction and argue that they are uncontrollable in applications typically targeted by CCBR systems. We that ignoring abstraction in CCBR can cause representational inconsistencies, adversely affect retrieval and conversation performance, and lead to case indexing and maintenance problems. We propose an integrated methodology called Taxonomic CCBR that uses feature taxonomies for abstraction to correct these problems. We describe the benefits and limitations of our approach and examine issues for future research.

• Type: Conference
• Year: 2001
• Where: ICCBR
• Authors: Kalyan Moy Gupta
• Keywords: Automated Reasoning, CCBR, CCBR Methodology, CCBR Systems, ICCBR 2001