Differentiable Learning of Logical Rules for Knowledge Base Reasoning

Fan Yang; Zhilin Yang; William W. Cohen

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arXiv · 1702.08367

Differentiable Learning of Logical Rules for Knowledge Base Reasoning

Fan Yang·Zhilin Yang·William W. Cohen

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Abstract

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.

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BibTeX RIS

Fan Yang, Zhilin Yang, William W. Cohen. 2017-02-27. Differentiable Learning of Logical Rules for Knowledge Base Reasoning. https://arxiv.org/abs/1702.08367

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