An evolutionary game theory based approach for query expansion

In Information Retrieval (IR) Systems, an essential technique employed to improve accuracy and efficiency is Query Expansion (QE). QE is the technique that reformulates the original query by adding the relevant terms that aid the retrieval process in generating more relevant outcomes. Numerous methods have been proposed in the literature that generates desirable results, however they do not provide evenly favourable results for all types of queries. One of the primary reasons for this is their inability to capture holistic relationships among the query terms. To tackle this issue, we have proposed a novel technique for QE that leverages a game-theoretic framework to recommend contextually relevant expansion terms for each query. In our approach, the query terms are interpreted as players that play a game with the other terms in the query in order to maximize their payoffs; the payoffs are determined using similarity measures between two query terms in the game. Our framework also works best for disambiguating polysemous query terms. The experimental section presents an analysis of the combination of various similarity and association measures employed in the proposed framework and a comparative analysis against state-of-art approaches. In addition to this, we present our analysis over three datasets, namely AP89, INEX and CLUEWEB in combination with WordNet and BabelNet as knowledge bases. The results show that the proposed work outperforms state-of-art algorithms.

Automated summary

Query Expansion (QE) is a key technique in Information Retrieval (IR) systems to improve accuracy and efficiency, but traditional methods do not provide uniformly favorable results for all types of queries. A novel QE technique based on a game-theoretic framework is proposed in this study, which can better capture relationships among query terms.