Supporting Dialogue Inferencing in Conversational Case-Based Reasoning

Dialogue inferencing is the knowledge-intensive process of inferring aspects of a user's problem from its partial description. Conversational case-based reasoning (CCBR) systems, which interactively and incrementally elicit a user's problem description, suffer from poor retrieval efficiency (i.e., they prompt the user with questions that the user has already implicitly answered) unless they perform dialogue inferencing. The standard method for dialogue inferencing in CCBR systems requires library designers to supply explicit inferencing rules. This approach is problematic (e.g., maintenance is difficult). We introduce an alternative approach in which the CCBR system guides the library designer in building a domain model. This model and the partial problem description are then given to a query retrieval system (PARKA-DB) to infer any implied answers during a conversation. In an initial empirical evaluation in the NaCoDAE CCBR tool, our approach improved retrieval efficiency without sacrificing retrieval precision.