Query Expansion based on Concept Clique for Markov Network Information Retrieval Model

Abstract

Query expansion is a common technique used to improve retrieval effectiveness. In this paper, we propose a novel query expansion technique based on concept clique for Markov network information retrieval model. This technique strengthens the simple relationships between terms in the following two ways:(1) terms in a clique express a similar concept and will be added into query expansion, so that it is effective to expanded into some terms with low similar to query terms but highly related to query topic; (2) query term dependencies are used to select concept cliques as candidates. The selection of concept cliques based on a connected graph is effective to avoid topic drift during expanding polysemous query terms. Experiments on several collections show that new approach makes significant improvements and more effective on collections with polysemous terms.