Using Medical Test Collection Relevance Judgements to Identify Ontological Relationships Useful for Query Expansion

In this paper we describe an innovative query expansion evaluation framework (QEEF) which discovers the ontological and algorithmic characteristics that drive successful query expansion. The method consists of identifying UMLS (Unified Medical Language System) concepts in the Ohsumed corpus queries and documents, and then applying variety of query expansion algorithms to the query concepts, both individually and at the query level. We analyse the results, discovering the characteristics of high relevance medical query expansions. We directly evaluate query expansion success, and this enables discovery of the relationship between the UMLS facets and this success. The paper details the methods used, and then discusses the influence of both UMLS attributes, and choice of query expansion algorithm, on query expansion success.