Convolutional 2D Knowledge Graph Embeddings

In this work, we introduce a convolutional neural network model, ConvE, for the task of link prediction. ConvE applies 2D convolution directly on embeddings, thus inducing spatial structure in embedding space. To scale to large knowledge graphs and prevent overfitting due to over-parametrization, previous work seeks to reduce parameters by performing simple transformations in embedding space. We take inspiration from computer vision, where convolution is able to learn multiple layers of non-linear features while reducing the number of parameters through weight sharing. Applied naively, convolutional models for link prediction are computationally costly. However, by predicting all links simultaneously we…

• UC
  University College London
• H2
  Horizon 2020 Framework Programme