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Investigating the Use of Semantic Relatedness at Document and Passage Level for Query Augmentation

This paper documents an approach that i) uses graphs to capture the semantic relatedness between terms in text and ii) augmenting queries with those terms deemed to be semantically related to the query terms. In building the graphs we use a relatively straightforward approach based on term locations; we investigate approaches that aid query improvement by capturing the semantic relatedness that is extracted at passage level as well as the complete document level. Semantic relatedness between is represented on a graph, where the terms are stored as nodes and the strength of their connection is recorded as an edge weight. In this fashion, we recorded the degree of connection between terms and use this to suggest possible additional words for improving the precision of a query. We compare the results of both approaches to a traditional approach and present a number of experiments at passage and document level. Our findings are that the approaches investigated achieve a competitive standard against a well known baseline.