Abstract
Though subgraph matching has been extensively studied as a query paradigm in semantic web and social network data environments, a user can get a large number of answers in response to a query. Just like Google does, these answers can be shown to the user in accordance with an importance ranking. In this paper, we present scalable algorithms to find the top-K answers to a practically important subset of SPARQL-queries, denoted as importance queries, via a suite of pruning techniques. We test our algorithms on multiple real-world graph data sets, showing that our algorithms are efficient even on networks with up to 6M vertices and 15M edges and far more efficient than popular triple stores.
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Ovelgönne, M., Park, N., Subrahmanian, V.S., Bowman, E.K., Ogaard, K.A. (2013). Personalized Best Answer Computation in Graph Databases. In: Alani, H., et al. The Semantic Web – ISWC 2013. ISWC 2013. Lecture Notes in Computer Science, vol 8218. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41335-3_30
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DOI: https://doi.org/10.1007/978-3-642-41335-3_30
Publisher Name: Springer, Berlin, Heidelberg
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