Abstract
Faceted search enables users to browse and discover relevant items from a large collection such as the Web of data. Existing faceted search solutions assume a precise information need, and thus optimise relevance, interestingness, and costs of fulfilling an information need. In this paper, we propose a complementary solution. Instead of assuming a search scenario (i.e., a user has a precise information need), our solution targets a browsing scenario (i.e., a user has a fuzzy need). We aim to support users in exploring an unknown collection of items, thereby allowing them to discover new or unfamiliar items of interest. Our approach comprises mechanisms for grouping facets and facet values and facet ranking. Via a task-based evaluation, we demonstrate that the proposed solution enables more effective browsing compared to the state-of-the-art, given fuzzy information needs.KeywordsFuzzy InformationFacet ModelConjunctive QueryRanking SchemeVariable BindingThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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