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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-
Αναγνωριστικόhttps://doi.org/10.1016/j.knosys.2018.08.035-
Αναγνωριστικόhttps://www.sciencedirect.com/science/article/pii/S0950705118304313-
Γλώσσαen-
Μέγεθος13 pagesen
ΤίτλοςLarge scale distributed spatio-temporal reasoning using real-world knowledge graphsen
ΔημιουργόςMantle Matthewen
ΔημιουργόςBatsakis Sotiriosen
ΔημιουργόςΜπατσακης Σωτηριοςel
ΔημιουργόςAntoniou, Grigorisen
ΕκδότηςElsevieren
ΠερίληψηMost 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
ΤύποςPeer-Reviewed Journal Publicationen
ΤύποςΔημοσίευση σε Περιοδικό με Κριτέςel
Άδεια Χρήσηςhttp://creativecommons.org/licenses/by/4.0/en
Ημερομηνία2020-10-23-
Ημερομηνία Δημοσίευσης2019-
Θεματική ΚατηγορίαDistributed computingen
Θεματική ΚατηγορίαKnowledge graphsen
Θεματική ΚατηγορίαParallel computingen
Θεματική ΚατηγορίαQualitative reasoningen
Βιβλιογραφική ΑναφοράM. 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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