Knowledge Representation in Graphs using Convolutional Neural Networks
  Knowledge Graphs (KG) constitute a flexible representation of complex
relationships between entities particularly useful for biomedical data. These
KG, however, are very sparse with many missing edges (facts) and the
visualisation of the mesh of interactions nontrivial. Here we apply a
compositional model to embed nodes and relationships into a vectorised semantic
space to perform graph completion. A visualisation tool based on Convolutional
Neural Networks and Self-Organised Maps (SOM) is proposed to extract high-level
insights from the KG. We apply this technique to a subset of CTD, containing
interactions of compounds with human genes / proteins and show that the
performance is comparable to the one obtained by structural models.
