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dc.contributor.authorGutberlet, Tim
dc.contributor.authorSauerbier, Janik
dc.contributor.editorKönig-Ries, Birgitta
dc.contributor.editorScherzinger, Stefanie
dc.contributor.editorLehner, Wolfgang
dc.contributor.editorVossen, Gottfried
dc.date.accessioned2023-02-23T14:00:22Z
dc.date.available2023-02-23T14:00:22Z
dc.date.issued2023
dc.identifier.isbn978-3-88579-725-8
dc.identifier.urihttp://dl.gi.de/handle/20.500.12116/40386
dc.description.abstractIn this paper, we focus on the problem of identifying all rules that entail a certain target fact given a knowledge graph and a set of previously learned rules. This problem is relevant in the context of link prediction and explainability. We propose an efficient approach using relational database technology including indexing, filtering and pre-computing methods. Our experiments demonstrate the efficiency of our approach and the effect of various optimizations on different datasets like YAGO3-10, WN18RR and FB15k-237 using rules learned by the bottom up rule learner AnyBURL.en
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofBTW 2023
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-331
dc.subjectKnowledge graphs
dc.subjectRelational databases
dc.subjectLink prediction
dc.subjectExplainability
dc.titleWhich Rules Entail this Fact? - An Efficient Approach Using RDBMSsen
dc.typeText/Conference Paper
dc.identifier.doi10.18420/BTW2023-76
gi.conference.locationDresden, Germany
gi.conference.date06.-10. März 2023
gi.citation.startPage1091
gi.citation.endPage1097
gi.citation.publisherPlaceBonn


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