Explass: Exploring Associations between Entities via Top-K Ontological Patterns and Facets

Searching for associations between entities is needed in many areas. On the Semantic Web, it usually boils down to finding paths that connect two entities in an entity-relation graph. Given the increasing volume of data, apart from the efficiency of path finding, recent research interests have focused on how to help users explore a large set of associations that have been found. To achieve this, we propose an approach to exploratory association search, called Explass, which provides a flat list (top-

K

) of clusters and facet values for refocusing and refining the search. Each cluster is labeled with an ontological pattern, which gives a conceptual summary of the associations in the cluster. Facet values comprise classes of entities and relations appearing in associations. To recommend frequent, informative, and small-overlapping patterns and facet values, we exploit ontological semantics, query context, and information theory. We compare Explass with two existing approaches by conducting a user study over DBpedia, and test the statistical significance of the results.