Edge-Weighted-Based Graph Neural Network for First-Order Premise Selection

LIU Qinghua; XU Yang; WU Guanfeng; LI Ruijie

doi:10.3969/j.issn.0258-2724.20210134

Volume 57 Issue 6

Dec. 2022

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Journal of Southwest Jiaotong University > 2022 > 57(6): 1368-1375.

LIU Qinghua, XU Yang, WU Guanfeng, LI Ruijie. Edge-Weighted-Based Graph Neural Network for First-Order Premise Selection[J]. Journal of Southwest Jiaotong University, 2022, 57(6): 1368-1375. doi: 10.3969/j.issn.0258-2724.20210134

Citation:

LIU Qinghua, XU Yang, WU Guanfeng, LI Ruijie. Edge-Weighted-Based Graph Neural Network for First-Order Premise Selection[J]. Journal of Southwest Jiaotong University, 2022, 57(6): 1368-1375. doi: 10.3969/j.issn.0258-2724.20210134

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Edge-Weighted-Based Graph Neural Network for First-Order Premise Selection

doi: 10.3969/j.issn.0258-2724.20210134

LIU Qinghua^{1, 2
,},
XU Yang^{2, 3
,
,},
WU Guanfeng^{2, 3},
LI Ruijie⁴

1.
School of Information Science and Technology, Southwest Jiaotong University, Chengdu 611756, China
2.
National-Local Joint Engineering Laboratory of System Credibility Automatic Verification, Southwest Jiaotong University, Chengdu 610031, China
3.
School ofMathematics, Southwest Jiaotong University, Chengdu 611756, China
4.
School of Transportation and Logistics, Southwest Jiaotong University, Chengdu 611756, China

Received Date: 23 Feb 2021
Rev Recd Date: 12 Jul 2021

Available Online: 24 Aug 2022

Publish Date: 17 Nov 2021

Abstract

Abstract

In order to improve the ability of automated theorem provers, the premise selection task emerges. Due to the directional nature of formula graphs, most current graph neural networks can only update nodes in one direction, and cannot encode the order of sub-nodes in the formula graph. To address the above problems, a bidirectional graph with edge types to represent logical formulae and a edge-weight-based graph neural network (EW-GNN) model to encode first-order logic formulae are proposed. The model firstly uses the information of connected nodes to update the feature representation of the corresponding edge type, then calculates the weight of the adjacent node to the central node with the updated edge type feature, and finally uses the information of the adjacent node to update the target node in both directions. The experimental results show the edge-weighted-based graph neural network model performs better in the premise selection task, which can improve the classification accuracy by 1% compared to the best model on the same test dataset.
- graph neural network,
- bidirectional graph,
- premise selection,
- first-order logical formula

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References(29)

References

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