Improving Difficult Queries by Leveraging Clusters in Term Graph

Term graphs, in which the nodes correspond to distinct lexical units (words or phrases) and the weighted edges represent semantic relatedness between those units, have been previously shown to be beneficial for ad-hoc IR. In this paper, we experimentally demonstrate that indiscriminate utilization of term graphs for query expansion limits their retrieval effectiveness. To address this deficiency, we propose to apply graph clustering to identify coherent structures in term graphs and utilize these structures to derive more precise query expansion language models. Experimental evaluation of the proposed methods using term association graphs derived from document collections and popular knowledge bases (ConceptNet and Wikipedia) on TREC datasets indicates that leveraging semantic structure in term graphs allows to improve the results of difficult queries through query expansion.