• Corpus ID: 5686519

Differentiable Learning of Logical Rules for Knowledge Base Completion

@article{Yang2017DifferentiableLO,
  title={Differentiable Learning of Logical Rules for Knowledge Base Completion},
  author={Fan Yang and Zhilin Yang and William W. Cohen},
  journal={ArXiv},
  year={2017},
  volume={abs/1702.08367}
}
Learned models composed of probabilistic logical rules are useful for many tasks, such as knowledge base completion. Unfortunately this learning problem is difficult, since determining the structure of the theory normally requires solving a discrete optimization problem. In this paper, we propose an alternative approach: a completely differentiable model for learning sets of first-order rules. The approach is inspired by a recently-developed differentiable logic, i.e. a subset of first-order… 

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