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Large scale distributed spatio-temporal reasoning using real-world knowledge graphs

Mantle Matthew, Batsakis Sotirios, Antoniou, Grigoris

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URIhttp://purl.tuc.gr/dl/dias/1BD24A3B-2CA4-41C0-AD17-78C6E80FFB0B-
Identifierhttps://doi.org/10.1016/j.knosys.2018.08.035-
Identifierhttps://www.sciencedirect.com/science/article/pii/S0950705118304313-
Languageen-
Extent13 pagesen
TitleLarge scale distributed spatio-temporal reasoning using real-world knowledge graphsen
CreatorMantle Matthewen
CreatorBatsakis Sotiriosen
CreatorΜπατσακης Σωτηριοςel
CreatorAntoniou, Grigorisen
PublisherElsevieren
Content SummaryMost of the existing work in the field of Qualitative Spatial Temporal Reasoning (QSTR) has focussed on comparatively small constraint networks that consist of hundreds or at most thousands of relations. Recently we have seen the emergence of much larger qualitative spatial knowledge graphs that feature hundreds of thousands and millions of relations. Traditional approaches to QSTR are unable to reason over networks of such size. In this article we describe ParQR, a parallel, distributed implementation of QSTR techniques that addresses the challenge of reasoning over large-scale qualitative spatial and temporal datasets. We have implemented ParQR using the Apache Spark framework, and evaluated our approach using both large scale synthetic datasets and real-world knowledge graphs. We show that our approach scales effectively, is able to handle constraint networks consisting of millions of relations, and outperforms current distributed implementations of QSTR.en
Type of ItemPeer-Reviewed Journal Publicationen
Type of ItemΔημοσίευση σε Περιοδικό με Κριτέςel
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2020-10-23-
Date of Publication2019-
SubjectDistributed computingen
SubjectKnowledge graphsen
SubjectParallel computingen
SubjectQualitative reasoningen
Bibliographic CitationM. Mantle, S. Batsakis and G. Antoniou, "Large scale distributed spatio-temporal reasoning using real-world knowledge graphs," Knowl.-Based Syst., vol. 163, pp. 214-226, Jan. 2019. doi: 10.1016/j.knosys.2018.08.035en

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