Premise Selection for Theorem Proving by Deep Graph Embedding

Mingzhe Wang; Yihe Tang; Jian Wang; Jia Deng

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arXiv · 1709.09994

Premise Selection for Theorem Proving by Deep Graph Embedding

Mingzhe Wang·Yihe Tang·Jian Wang·Jia Deng

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Abstract

We propose a deep learning-based approach to the problem of premise selection: selecting mathematical statements relevant for proving a given conjecture. We represent a higher-order logic formula as a graph that is invariant to variable renaming but still fully preserves syntactic and semantic information. We then embed the graph into a vector via a novel embedding method that preserves the information of edge ordering. Our approach achieves state-of-the-art results on the HolStep dataset, improving the classification accuracy from 83% to 90.3%.

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BibTeX RIS

Mingzhe Wang, Yihe Tang, Jian Wang, Jia Deng. 2017-09-28. Premise Selection for Theorem Proving by Deep Graph Embedding. https://arxiv.org/abs/1709.09994

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