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Modeling Relational Data with Graph Convolutional Networks
Published on Jul 10, 20182034 Views
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
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Chapter list
Modeling Relational Data with Graph Convolutional Networks00:00
Deep Learning for data on “grids” - 100:34
Deep Learning for data on “grids” - 201:55
Traditional vs. “deep” end-to-end learning02:14
Graph-structured / relational data - 102:59
Graph-structured / relational data - 203:13
Graph-structured / relational data - 303:30
Graph-structured / relational data - 403:36
Graph Convolutional Networks (GCNs)03:51
Recap: Convolutional neural networks (on grids)04:50
Graph convolutional networks (GCNs)06:08
Relational extension of GCNs08:59
How to scale?10:41
R-GCNs for entity classification and link prediction13:18
What do learned representations look like?14:54
Untitled15:34
Toy example (semi-supervised learning)15:52
R-GCNs for entity classification and link prediction16:27
Entity classification experiments18:12
Link prediction experiments - 119:41
Link prediction experiments - 221:16
Conclusions and future work22:14
Further reading23:28