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arXiv · 1604.04795

KOGNAC: Efficient Encoding of Large Knowledge Graphs

Jacopo Urbani·Sourav Dutta·Sairam Gurajada·Gerhard Weikum
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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.

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Jacopo Urbani, Sourav Dutta, Sairam Gurajada, Gerhard Weikum. 2016-07-10. KOGNAC: Efficient Encoding of Large Knowledge Graphs. https://arxiv.org/abs/1604.04795

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