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KOGNAC:Efficient encoding of large knowledge graphs
- Source :
- Urbani, J, Dutta, S, Gurajada, S & Weikum, G 2016, ' KOGNAC : Efficient encoding of large knowledge graphs ', IJCAI International Joint Conference on Artificial Intelligence, vol. 2016-January, pp. 3896-3902 . < https://www.ijcai.org/Proceedings/16/Papers/548.pdf >
- Publication Year :
- 2016
-
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.
- Subjects :
- InformationSystems_DATABASEMANAGEMENT
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Journal :
- Urbani, J, Dutta, S, Gurajada, S & Weikum, G 2016, ' KOGNAC : Efficient encoding of large knowledge graphs ', IJCAI International Joint Conference on Artificial Intelligence, vol. 2016-January, pp. 3896-3902 . < https://www.ijcai.org/Proceedings/16/Papers/548.pdf >
- Accession number :
- edsair.od......4612..a3b5c4c45241b6778f6db6ae17f1e3ce