Modeling Relational Data with Graph Convolutional Networks

Michael Schlichtkrull, Thomas N. Kipf*, Peter Bloem, Rianne van den Berg, Ivan Titov, Max Welling

*Corresponding author for this work

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review

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Abstract

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.

Original languageEnglish
Title of host publicationThe Semantic Web
Subtitle of host publication15th International Conference, ESWC 2018, Heraklion, Crete, Greece, June 3–7, 2018, Proceedings
EditorsAldo Gangemi, Roberto Navigli, Maria-Esther Vidal, Pascal Hitzler, Raphaël Troncy, Laura Hollink, Anna Tordai, Mehwish Alam
PublisherSpringer/Verlag
Pages593-607
Number of pages15
ISBN (Electronic)9783319934174
ISBN (Print)9783319934167
DOIs
Publication statusPublished - 2018
Event15th International Conference on Extended Semantic Web Conference, ESWC 2018 - Heraklion, Greece
Duration: 3 Jun 20187 Jun 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10843 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference15th International Conference on Extended Semantic Web Conference, ESWC 2018
Country/TerritoryGreece
CityHeraklion
Period3/06/187/06/18

Funding

Acknowledgements. 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).

FundersFunder number
Horizon 2020 Framework Programme678254
H2020 European Research Council
European Research Council
Nederlandse Organisatie voor Wetenschappelijk OnderzoekVIDI 639.022.518

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