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The tale of sansa spark

: Ermilov, Ivan; Lehmann, Jens; Sejdiu, Gezim; Bühmann, Lorenz; Westphal, Patrick; Stadler, Claus; Bin, Simon; Chakraborty, Nilesh; Petzka, Henning; Saleem, Muhammad; Ngonga Ngomo, Axel-Cyrille; Jabeen, Hajira

Volltext (PDF; )

Nikitina, N.:
ISWC 2017 Posters & Demonstrations and Industry Tracks. Proceedings. Online resource : Co-located with 16th International Semantic Web Conference (ISWC 2017), Vienna, Austria, October 23rd to 25th, 2017
Vienna, 2017 (CEUR Workshop Proceedings 1963)
Paper 552, 4 S.
International Semantic Web Conference (ISWC) <16, 2017, Vienna>
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IAIS ()

We demonstrate the open-source Semantic Analytics Stack (SANSA), which can perform scalable analysis of large-scale knowledge graphs to facilitate applications such as link prediction, knowledge base completion and reasoning. The motivation behind this work lies in the lack of scalable methods for analytics which exploit expressive structures underlying semantically structured knowledge bases. The demonstration is based on the BigDataEurope technical platform, which utilizes Docker technology. We present various examples of using SANSA in the form of interactive Spark notebooks, which are executed with Apache Zeppelin. The technical platform and the notebooks are available on SANSA Github and can be deployed on any Docker-enabled host, locally or in a Docker Swarm cluster.