Query Expansion for IR using Knowledge-Based Relatedness

The limitations of keyword-only approaches to information retrieval were recognized since the early days, specially in cases where different but closely-related words are used in the query and the relevant document. Query expansion techniques like pseudo-relevance feedback rely on the target document set in order to bridge the gap between those words, but they might suffer from topic drift. This paper explores the use of knowledge-based semantic relatedness in order to bridge the gap between query and documents. We performed query expansion, with positive effects over some language modeling baselines.