Relational Graph Convolutional Networks (R-GCNs) can be used for link prediction and entry classification much more efficiently than walk-based models for statistical relational learning. Ensure that your large relational database isn’t missing any data!
Automatically Address Holes in Your Knowledge Base
Post · Mar 20, 2017 17:13 · Share on Twitter
Knowledge bases play a crucial role in many applications, for example question answering and information retrieval. Despite the great effort invested in creating and maintaining them, even the largest representatives (e.g., Yago, DBPedia or Wikidata) are highly 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 attributes of entities). R-GCNs are a generalization of graph convolutional networks, a recent class of neural networks operating on graphs, and are developed specifically to deal with highly multi-relational data, characteristic of realistic knowledge bases. Our methods achieve competitive results on standard benchmarks for both tasks.