KOGNAC: Efficient encoding of large knowledge graphs

Jacopo Urbani, Sourav Dutta, Sairam Gurajada, Gerhard Weikum

Research output: Contribution to JournalArticleAcademicpeer-review

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.

Original languageEnglish
Pages (from-to)3896-3902
Number of pages7
JournalIJCAI International Joint Conference on Artificial Intelligence
Volume2016-January
Publication statusPublished - 2016

Fingerprint

Dive into the research topics of 'KOGNAC: Efficient encoding of large knowledge graphs'. Together they form a unique fingerprint.

Cite this