Combining Semantic Query Disambiguation and Expansion to Improve Intelligent Information Retrieval

We show in this paper how Semantic Query Disambiguation (SQD) combined with Semantic Query Expansion (SQE) can improve the effectiveness of intelligent information retrieval. Firstly, we propose and assess a possibilistic-based approach mixing SQD and SQE. This approach is based on corpus analysis using co-occurrence graphs modeled by possibilistic networks. Indeed, our model for relevance judgment uses possibility theory to take advantage of a double measure (possibility and necessity). Secondly, we propose and evaluate a probabilistic circuit-based approach combining SQD and SQE in an intelligent information retrieval context. In this approach, both SQD and SQE tasks are based on a graph data model, in which circuits between its nodes (words) represent the probabilistic scores for their semantic proximities. In order to compare the performance of these two approaches, we perform our experiments using the standard ROMANSEVAL test collection for the SQD task and the CLEF-2003 benchmark for the SQE process in French monolingual information retrieval evaluation. The results show the impact of SQD on SQE based on the recall/precision standard metrics for both the possibilistic and the probabilistic circuit-based approaches. Besides, the results of the possibilistic approach outperform the probabilistic ones, since it takes into account of imprecision cases.