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.
@article{arxiv.1604.04795,
title = {KOGNAC: Efficient Encoding of Large Knowledge Graphs},
author = {Jacopo Urbani and Sourav Dutta and Sairam Gurajada and Gerhard Weikum},
journal= {arXiv preprint arXiv:1604.04795},
year = {2016}
}