  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.
