Abstract
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.
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Cheng, G., Zhang, Y., Qu, Y. (2014). Explass: Exploring Associations between Entities via Top-K Ontological Patterns and Facets. In: Mika, P., et al. The Semantic Web – ISWC 2014. ISWC 2014. Lecture Notes in Computer Science, vol 8797. Springer, Cham. https://doi.org/10.1007/978-3-319-11915-1_27
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DOI: https://doi.org/10.1007/978-3-319-11915-1_27
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