Relevance is a multidimensional concept, not only consisting of linguistic-only properties but also enriched by various other relevance dimensions that are largely orthogonal to the topicality (i.e. content-based relevance) of a document. The question is how to capture such dimensions of relevance effectively in a retrieval model. In this paper we propose a model where we regard additional relevance dimensions independent (given a document instantiation). The independence assumption is made because it is very difficult to predict influence of relevance dimensions a-priori. The model also reflects our belief that modeling of additional knowledge with prior probabilities (in a probabilistic setting) is a counter-intuitive approach because of 1) the orthogonality of additional relevance dimensions and 2) the difficulty to reliably (re-)estimate dimension models, due to possible `noise' introduced by nondimension related priors. Also, relevance feedback needs to be able to handle multiple dimensions of relevance effectively. Feedback in the model is done with dimension-specific feedback sets. We can only report informally on the results of our model; based on the experimental scenarios performed, the model is appearing to perform very well, although quantitative assessments using an assessed collection are necessary to confirm this and draw further conclusions.

Katholieke Universiteit Leuven
Dutch-Belgian Information Retrieval Workshop
Database Architectures

List, J. A., & de Vries, A. (2002). XML-IR: coverage as a part of relevance. In Proceedings of Dutch-Belgian IR Workshop 2002 (3) (pp. 7–12). Katholieke Universiteit Leuven.