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Distributed semantic analytics using the SANSA stack

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

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D'Amato, C.:
The Semantic Web - ISWC 2017 : 16th International Semantic Web Conference, Vienna, Austria, October 21-25, 2017. Proceedings, Part II
Cham: Springer International Publishing, 2017 (Lecture Notes in Computer Science 10588)
ISBN: 978-3-319-68204-4 (electronic)
ISBN: 978-3-319-68203-7 (print)
S.147-155
International Semantic Web Conference (ISWC) <16, 2017, Vienna>
Englisch
Konferenzbeitrag
Fraunhofer IAIS ()

Abstract
A major research challenge is to perform scalable analysis of large-scale knowledge graphs to facilitate applications like link prediction, knowledge base completion and reasoning. Analytics methods which exploit expressive structures usually do not scale well to very large knowledge bases, and most analytics approaches which do scale horizontally (i.e., can be executed in a distributed environment) work on simple feature-vector-based input. This software framework paper describes the ongoing Semantic Analytics Stack (SANSA) project, which supports expressive and scalable semantic analytics by providing functionality for distributed computing on RDF data.

: http://publica.fraunhofer.de/dokumente/N-473880.html