Modeling Relational Data with Graph Convolutional Networks

Understand

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).