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Knowledge Graph Processing Made (more) Simple

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dc.contributor.authorLausen, Georg
dc.contributor.editorMayr, Heinrich C.
dc.contributor.editorRinderle-Ma, Stefanie
dc.contributor.editorStrecker, Stefan
dc.date.accessioned2020-05-14T07:16:10Z
dc.date.available2020-05-14T07:16:10Z
dc.date.issued2020
dc.description.abstractKnowledge graphs based on RDF and SPARQL are gaining popularity for integrated semantic representation of structured and unstructured data. As knowledge graphs in practical applications tend to become huge, distributed processing using Apache Spark SQL and Hadoop on top of a compute cluster is attractive. For the corresponding relational representation of a knowledge graph, a simple relational design using only one single table is proposed. Consequently no time consuming relational design considerations are required and newly discovered RDF data can be integrated with nearly no extra additional relational design effort.en
dc.identifier.isbn978-3-88579-698-5
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/33122
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartof40 Years EMISA 2019
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-304
dc.subjectKnowledge Graph
dc.subjectRDF
dc.subjectSPARQL
dc.subjectApache Spark SQL
dc.subjectParquet
dc.subjectHadoop
dc.titleKnowledge Graph Processing Made (more) Simpleen
dc.typeText/Conference Paper
gi.citation.endPage138
gi.citation.publisherPlaceBonn
gi.citation.startPage135
gi.conference.date15.-17. May, 2019
gi.conference.locationTutzing, Germany
gi.conference.sessiontitleInvited Talk

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P304 - 40 Years EMISA 2019