• ISSN 0258-2724
  • CN 51-1277/U
  • EI Compendex
  • Scopus
  • Indexed by Core Journals of China, Chinese S&T Journal Citation Reports
  • Chinese S&T Journal Citation Reports
  • Chinese Science Citation Database
Volume 57 Issue 6
Dec.  2022
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Article Contents
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

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

doi: 10.3969/j.issn.0258-2724.20210134
  • Received Date: 23 Feb 2021
  • Rev Recd Date: 12 Jul 2021
  • Available Online: 24 Aug 2022
  • Publish Date: 17 Nov 2021
  • 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.

     

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