βˆ™ 0 βˆ™ share . AU - Titov, Ivan. T1 - Modeling Relational Data with Graph Convolutional Networks. chalothon/Graph-Convolutional-Networks-for-Relational-Link-Prediction 0 Add a new evaluation result row AU - Schlichtkrull, Michael. 03/17/2017 βˆ™ by Michael Schlichtkrull, et al. AU - van den Berg, Rianne. AU - Kipf, Thomas N. AU - Bloem, Peter. M. Schlichtkrull*, T. N. Kipf*, P. Bloem, R. vd Berg, I. Titov, M. Welling, Modeling Relational Data with Graph Convolutional Networks, (ESWC 2018), Best Student Research Paper [Link, PDF (arXiv), code], *equal contribution. This repository contains a TensorFlow implementation of Relational Graph Convolutional Networks (R-GCN), as well as experiments on relational link prediction. Abstract: Graph Convolutional Networks (GCNs) have recently been shown to be quite successful in modeling graph-structured data. Modeling Relational Data with Graph Convolutional Networks . 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. It’s basically an extension of the GCN of Kipf and Welling, 2017. Knowledge graphs enable a wide variety of applications, including question answering and information retrieval. 2.1 Relational Graph Convolutional Networks. Y1 - 2018. Despite the great effort invested in their creation and maintenance, even the largest (e.g., Yago, DBPedia or Wikidata) remain incomplete. This refers to the R-GCN that is used in this paper. However, the primary focus has been on handling simple undirected graphs.

Graph Convolutional Networks for Relational Link Prediction. N2 - Knowledge graphs enable a wide variety of applications, including question answering and information retrieval. Modeling Relational Data with Graph Convolutional Networks. Keywords: Graph Convolutional Networks, Multi-relational Graphs, Knowledge Graph Embeddings, Link Prediction TL;DR: A Composition-based Graph Convolutional framework for multi-relational graphs. Link prediction and entity classification on knowledge graphs. AU - Welling, Max. The main difference is that Kipf and Welling, 2017 was based on operating on local neighborhoods, whereas R-GCN is meant for large-scale relational data. Despite the great effort invested in their creation and maintenance, even the largest (e.g., Yago, DBPedia or Wikidata) remain incomplete. PY - 2018. Knowledge graphs enable a wide variety of applications, including question answering and information retrieval. subject-predicate-object triples) and entity classification (recovery of missing entity attributes).

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