PSemQE : Disambiguating Short Queries Through Personalised Semantic Query Expansion

Abstract

Abstract

Locating items in large information systems can be challenging, especially if the query has multiple senses referring to different items: depending on the context, Amazon may refer to the river, rainforest, or a mythical female warrior. We propose and study Personalised Semantic Query Expansion (PSemQE) as a means of disambiguating short, ambiguous queries in information retrieval systems. This study examines PSemQE’s effectiveness in retrieving relevant documents matching intended senses of ambiguous terms and ranking them higher versus a base query without expansion. Synthetic user profiles focused on narrow domains were generated to model well-defined information needs. Word embeddings trained on these profiles were used to expand queries with semantically related terms. Experiments were conducted on corpora of varying sizes to measure the retrieval of predetermined target articles. Our results show that PSemQE successfully disambiguated polysemous queries and ranked the target ar ticles higher than the base query. Furthermore, PSemQE produces result sets with higher relevance to user interests. Despite limitations like synthetic profiles and cold-start issues, this study shows PSemQE’s potential as an effective query disambiguation engine. Overall, PSemQE can enhance search relevance and user experience by leveraging user information to provide meaningful responses to short, ambiguous queries.