Proceedings Abstracts of the Twenty-Fifth International Joint Conference on Artificial Intelligence

KOGNAC: Efficient Encoding of Large Knowledge Graphs / 3896
Jacopo Urbani, Sourav Dutta, Sairam Gurajada, Gerhard Weikum

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.