Abstract
Many Web applications require efficient querying of large Knowledge Graphs (KGs). We propose KOGNAC, a dictionary-encoding algorithm designed to improve SPARQL querying with a judicious combination of statistical and semantic techniques. In KOGNAC, frequent terms are detected with a frequency approximation algorithm and encoded to maximise compression. Infrequent terms are semantically grouped into ontological classes and encoded to increase data locality. We evaluated KOGNAC in combination with state-of-the-art RDF engines, and observed that it significantly improves SPARQL querying on KGs with up to 1B edges.
Original language | English |
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Pages (from-to) | 3896-3902 |
Number of pages | 7 |
Journal | IJCAI International Joint Conference on Artificial Intelligence |
Volume | 2016-January |
Publication status | Published - 2016 |