Enhancing Conversational Search Agents for Resolving Ambiguity in Knowledge-Intensive Query Scenarios

Abstract

Conversational search agents are designed to interpret user queries in real time, engage in interactive clarification, and seamlessly retrieve information from extensive knowledge sources. However, in many knowledge-intensive domains, ambiguous or underspecified queries complicate the retrieval process. One fundamental challenge arises when the user’s intended context is not made explicit and the system must dynamically disambiguate among possible interpretations. This paper explores novel methods for incorporating advanced inference and integrated representation strategies that address ambiguity at various stages of the conversational pipeline. We propose that ambiguity resolution is best handled through a tight coupling of structural representations and logical formalisms, which can greatly enhance interpretive accuracy. By leveraging latent relationships embedded in discourse and contextual patterns gleaned from historical user interactions, our approach addresses semantic gaps in query interpretation. We detail a framework that systematically aligns user utterances with knowledge graphs using heuristic reasoning and vector-based similarity models to capture thematic overlaps. Through empirical analysis, we demonstrate that such integrated strategies reduce error propagation caused by early misinterpretations and help deliver more reliable responses in real-world settings. Ultimately, our findings underscore the importance of incorporating robust ambiguity resolution mechanisms into conversational interfaces, particularly in domains where precise retrieval is critical for user satisfaction and task success.