Inferring query models by information flow analysis

Bruza, P. D and Song, D. (2002). Inferring query models by information flow analysis. In: Proceedings of the 11th International ACM Conference on Information and Knowledge Management (CIKM 2002), 4-9 Nov 2002, McLean, Virginia, USA.



The language modelling approach to information retrieval can also be used to compute query models. A query model can be envisaged as an expansion of an initial query. The more prominent query models in the literature have a probabilistic basis. This paper introduces an alternative, non-probabilistic approach to query modelling whereby the strength of information flow is computed between a query Q and a term w. Information flow is a reflection of how strongly w is informationally contained within the query Q. The information flow model is based on Hyperspace Analogue to Language (HAL) vector representations, which reflects the lexical co-occurrence information of terms. Research from cognitive science has demonstrated the cognitive compatibility of HAL representations with human processing. Query models computed from TREC queries by HAL-based information flow are compared experimentally with two probabilistic query language models. Experimental results are provided showing the HAL-based information flow model be superior to query models computed via Markov chains, and seems to be as effective as a probabilistically motivated relevance model.

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