华东师范大学学报（自然科学版） >

2025 , Vol. 2025 >Issue 1: 97 - 110

DOI: https://doi.org/10.3969/j.issn.1000-5641.2025.01.008

计算机科学

融合关系和结构编码的规则抽取与推理研究

  • 胡继米 ,
  • 万卫兵 ,
  • 程锋 ,
  • 赵宇明
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  • 1. 上海工程技术大学 电子电气工程学院, 上海　201620
    2. 上海交通大学 自动化系, 上海　200240
万卫兵, 男, 副教授, 硕士生导师, 研究方向为工业大数据与人工智能. E-mail: wbwan@sues.edu.cn

收稿日期: 2023-09-25

  网络出版日期: 2025-01-20

基金资助

科技部科技创新2030 —“新一代人工智能”重大项目 (2020AAA0109300)

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华东师范大学学报期刊社, 2025, 版权所有，未经授权，不得转载、摘编本刊文章，不得使用本刊的版式设计。
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Rule extraction and reasoning for fusing relation and structure encoding

  • Jimi HU ,
  • Weibing WAN ,
  • Feng CHENG ,
  • Yuming ZHAO
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  • 1. School of Electrical and Electronic Engineering, Shanghai University of Engineering Science, Shanghai　201620, China
    2. Department of Automation, Shanghai Jiao Tong University, Shanghai　200240, China

Received date: 2023-09-25

  Online published: 2025-01-20

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, 2025, Copyright reserved © 2025.
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摘要

领域知识图谱拥有不完备性和语义复杂多样性的特点, 从而导致其在规则抽取和选择问题上的不足, 影响了其推理的能力. 针对此问题, 提出了一种融合关系和结构编码的规则抽取模型. 通过提取目标子图中的关系和结构信息并进行特征编码, 从而实现了一种多维度的嵌入表达方法. 设计了融合关系和结构信息的自注意力机制, 使模型能够更好地捕捉输入序列中的依赖关系和局部结构信息, 从而提升了模型对于上下文的理解和表达能力, 进而解决了在语义复杂情况下规则的抽取和选择的问题. 通过在真实汽车部件故障工业数据集和公共数据集的实验, 表明了在链接预测与规则质量评估任务中, 所提出的模型都有一定的提升 (规则长度为3时, mean reciprocal rank (MRR) 平均提升了7.1百分点, Hits@10平均提升了8.6百分点; 规则长度为2时, MRR平均提升了7.4百分点, Hits@10平均提升了3.9百分点), 证实了关系和结构信息对于规则抽取与推理的有效性.

关键词： 工业知识图谱; 规则抽取; 规则推理; 知识补全; 链接预测

本文引用格式

胡继米 , 万卫兵 , 程锋 , 赵宇明 . 融合关系和结构编码的规则抽取与推理研究[J]. 华东师范大学学报（自然科学版）, 2025 , 2025(1) : 97 -110 . DOI: 10.3969/j.issn.1000-5641.2025.01.008

Abstract

The domain knowledge graph exhibits characteristics of incompleteness and semantic complexity, which lead to shortcomings in the extraction and selection of rules, thereby affecting its inferential capabilities. A rule extraction model that integrates relationship and structural encoding is proposed to address this issue. A multidimensional embedding approach is achieved by extracting relational and structural information from the target subgraph and conducting feature encoding. A self-attention mechanism is designed to integrate relational and structural information, enabling the model to capture dependency relationships and local structural information in the input sequence better. This enhancement improves the understanding and expressive capabilities of context of the model, thus addressing the challenges of rule extraction and selection in the complex semantic situations. The experimental results for actual industrial datasets of automotive component failures and public datasets demonstrate improvements in the proposed model for link prediction and rule quality evaluation tasks. When the rule length is 3, an average increase of 7.1 percentage points in the mean reciprocal rank (MRR) and an average increase of 8.6 percentage points in Hits@10 are observed. For a rule length of 2, an average increase of 7.4 percentage points in MRR and an average increase of 3.9 percentage points in Hits@10 are observed. This confirms the effectiveness of relational and structural information in rule extraction and inference.

Key words： industrial knowledge graph; rule extraction; rule reasoning; knowledge completion; link prediction

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