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Modeling Relational data with Graph convolutional NetworksMichael Schlichtkrull University of N. Kipf University of BloemVU van den BergUniversity of TitovUniversity of WellingUniversity of Amsterdam, CIFAR graphs enable a wide variety of applications, in-cluding question answering and information retrieval. De-spite the great effort invested in their creation and mainte-nance, even the largest ( , Yago, DBPedia or Wikidata)remain incomplete. We introduce Relational Graph Convo-lutional Networks (R-GCNs) and apply them to two standardknowledge base completion tasks: Link prediction (recoveryof missing facts, subject-predicate-object triples) and en-tity classification (recovery of missing entity attributes). R-GCNs are related to a recent class of neural networks operat-ing on graphs, and are developed specifically to deal with thehighly multi- Relational data characteristic of realistic knowl-edge bases.
Modeling Relational Data with Graph Convolutional Networks Michael Schlichtkrull University of Amsterdam m.s.schlichtkrull@uva.nl Thomas N. Kipf University of …
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