ML Anthology

KOGNAC: Efficient Encoding of Large Knowledge Graphs

Jacopo Urbani, Sourav Dutta, Sairam Gurajada, Gerhard Weikum
IJCAI 2016 pp. 3896-3902
/ijcai/2016/urbani2016ijcai-kognac/

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. PDF

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Cite

Text

Urbani et al. "KOGNAC: Efficient Encoding of Large Knowledge Graphs." International Joint Conference on Artificial Intelligence, 2016.

Markdown

[Urbani et al. "KOGNAC: Efficient Encoding of Large Knowledge Graphs." International Joint Conference on Artificial Intelligence, 2016.](https://mlanthology.org/ijcai/2016/urbani2016ijcai-kognac/)

BibTeX

@inproceedings{urbani2016ijcai-kognac,
  title     = {{KOGNAC: Efficient Encoding of Large Knowledge Graphs}},
  author    = {Urbani, Jacopo and Dutta, Sourav and Gurajada, Sairam and Weikum, Gerhard},
  booktitle = {International Joint Conference on Artificial Intelligence},
  year      = {2016},
  pages     = {3896-3902},
  url       = {https://mlanthology.org/ijcai/2016/urbani2016ijcai-kognac/}
}