We further show that factorization models for link prediction such as DistMult can be significantly improved by enriching them with an encoder model to accumulate evidence over multiple inference steps in the relational graph, demonstratin…

We demonstrate the effectiveness of R-GCNs as a stand-alone model for entity classification. Michael Schlichtkrull, et al., "Modeling Relational Data with Graph Convolutional Networks" https://arxiv.org/abs/1703.06103 関係構造を表すグラフ…

R-GCNs are related to a recent class of neural networks operating on graphs, and are developed specifically to deal with the highly multi-relational data characteristic of realistic knowledge bases. Michael Schlichtkrull, et al., "Modeling…

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 (rec…

Despite the great effort invested in their creation and maintenance, even the largest (e.g., Yago, DBPedia or Wikidata) remain incomplete. Michael Schlichtkrull, et al., "Modeling Relational Data with Graph Convolutional Networks" https://…

Knowledge graphs enable a wide variety of applications, including question answering and information retrieval. Michael Schlichtkrull, et al., "Modeling Relational Data with Graph Convolutional Networks" https://arxiv.org/abs/1703.06103 関…