Modeling Relational Data with Graph Convolutional Networks

  • Michael Schlichtkrull
  • Thomas N. KipfEmail author
  • Peter Bloem
  • Rianne van den Berg
  • Ivan Titov
  • Max Welling
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10843)


Knowledge graphs enable a wide variety of applications, including question answering and information retrieval. Despite the great effort invested in their creation and maintenance, even the largest (e.g., Yago, DBPedia or Wikidata) remain incomplete. We introduce Relational Graph Convolutional Networks (R-GCNs) and apply them to two standard knowledge base completion tasks: Link prediction (recovery of missing facts, i.e. subject-predicate-object triples) and entity classification (recovery of missing entity attributes). R-GCNs are related to a recent class of neural networks operating on graphs, and are developed specifically to handle the highly multi-relational data characteristic of realistic knowledge bases. We demonstrate the effectiveness of R-GCNs as a stand-alone model for entity classification. We further show that factorization models for link prediction such as DistMult can be significantly improved through the use of an R-GCN encoder model to accumulate evidence over multiple inference steps in the graph, demonstrating a large improvement of 29.8% on FB15k-237 over a decoder-only baseline.



We would like to thank Diego Marcheggiani, Ethan Fetaya, and Christos Louizos for helpful discussions and comments. This project is supported by the European Research Council (ERC StG BroadSem 678254), the SAP Innovation Center Network and the Dutch National Science Foundation (NWO VIDI 639.022.518).


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.University of AmsterdamAmsterdamThe Netherlands
  2. 2.Vrije Universiteit AmsterdamAmsterdamThe Netherlands
  3. 3.University of EdinburghEdinburghUK
  4. 4.Canadian Institute for Advanced ResearchTorontoCanada

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