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KOGNAC: Efficient Encoding of Large Knowledge Graphs

MPS-Authors
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Dutta,  Sourav
Databases and Information Systems, MPI for Informatics, Max Planck Society;

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Gurajada,  Sairam
Databases and Information Systems, MPI for Informatics, Max Planck Society;

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Weikum,  Gerhard
Databases and Information Systems, MPI for Informatics, Max Planck Society;

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arXiv:1604.04795.pdf
(Preprint), 392KB

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Citation

Urbani, J., Dutta, S., Gurajada, S., & Weikum, G. (2016). KOGNAC: Efficient Encoding of Large Knowledge Graphs. Retrieved from http://arxiv.org/abs/1604.04795.


Cite as: https://hdl.handle.net/11858/00-001M-0000-002B-01C1-3
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.