Differentiable Learning of Logical Rules for Knowledge Base Reasoning
  We study the problem of learning probabilistic first-order logical rules for
knowledge base reasoning. This learning problem is difficult because it
requires learning the parameters in a continuous space as well as the structure
in a discrete space. We propose a framework, Neural Logic Programming, that
combines the parameter and structure learning of first-order logical rules in
an end-to-end differentiable model. This approach is inspired by a
recently-developed differentiable logic called TensorLog, where inference tasks
can be compiled into sequences of differentiable operations. We design a neural
controller system that learns to compose these operations. Empirically, our
method outperforms prior work on multiple knowledge base benchmark datasets,
including Freebase and WikiMovies.
