Faceted Semantic Search

Abstract

The increasing amount of data on the Web bears potential for addressing complex information needs more effectively. Instead of keyword search and browsing along links between results, users can specify their needs in terms of complex queries and obtain precise answers right away. However, browsing is also essential on the Web of data as users might not always know a specific query language and more im- portantly, might not know the data. Particularly in cases where the information need is fuzzy, browsing is useful for exploring the data. Faceted search allows users to browse along facets. However, work on faceted search so far has been focused on search rather than browsing. In this paper, we propose a facet ranking scheme that targets the browsing experience. When there are too many facets given, user obtain a ranked list of facets, where the rank represents the facets browse-ability. Furthermore, facets might be associated with a large amount of values. Also target- ing browse-ability, we propose clustering mechanisms to decompose such facets into more fine-grained sub-facets. By means of a task-based evaluation, we demonstrate that the proposed solution enables more effective browsing, when compared to the state of the art that is rather focused on search-ability.