Graph Convolutional Networks for Extracting and Modeling Relational Data

Graph Convolutional Networks (GCNs) is an effective tool for modeling graph structured data. We investigate their applicability in the context of both extracting semantic relations from text (specifically, semantic role labeling) and modeling relational data (link prediction). For semantic role labeling, we introduce a version of GCNs suited to modeling syntactic dependency graphs and use them as sentence encoders. Relying on these linguistically-informed encoders, we achieve the best reported scores on standard benchmarks for Chinese and English. For link prediction, we propose Relational GCNs (RGCNs), GCNs developed specifically to deal with highly multi-relational data, characteristic of realistic knowledge bases. By explicitly modeling neighbourhoods of entities, RGCNs accumulate evidence over multiple inference steps in relational graphs and yield competitive results on standard link prediction benchmarks.

Joint work with Diego Marcheggiani, Michael Schlichtkrull, Thomas Kipf, Max Welling, Rianna van den Berg and Peter Bloem.