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
展开
  • 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)

版权

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。

Rule extraction and reasoning for fusing relation and structure encoding

  • Jimi HU ,
  • Weibing WAN ,
  • Feng CHENG ,
  • Yuming ZHAO
Expand
  • 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

Copyright

, 2025, Copyright reserved © 2025.

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.

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.

参考文献

1 ZHANG N Y, LI L, CHEN X, et al. Multimodal analogical reasoning over knowledge graphs [EB/OL]. (2023-03-01)[2023-07-01]. https://arxiv.org/abs/2210.00312.
2 HU W H, FEY M, REN H Y, et al. OGB-LSC: A large-scale challenge for machine learning on graphs [EB/OL]. (2021-10-20)[2023-07-01]. https://doi.org/10.48550/arXiv.2103.09430.
3 HOGAN A, BLOMQVIST E, COCHEZ M, et al.. Knowledge graphs. ACM Computing Surveys, 2021, 54 (4): 71.
4 GUO Q Y, ZHUANG F Z, QIN C, et al... A survey on knowledge graph-based recommender systems. IEEE Transactions on Knowledge and Data Engineering, 2022, 34 (8): 3549- 3568.
5 李智威. 医疗知识图谱构建和智能问答研究 [D]. 长春: 吉林大学, 2022.
6 翟岩慧, 何煦, 李德玉, 等.. 融合决策蕴涵的知识图谱推理方法. 计算机科学与探索, 2023, 17 (11): 2743- 2754.
7 付瑞, 李剑宇, 王笳辉, 等.. 面向领域知识图谱的实体关系联合抽取. 华东师范大学学报(自然科学版), 2021, (5): 24- 36.
8 BORDES A, USUNIER N, GARCIA-DURAN A, et al. Translating embeddings for modeling multi-relational data [C]// Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. Red Hook, NY, US: Curran Associates Inc., 2013: 2787-2795.
9 SUN Z Q, DENG Z H, NIE J Y, et al. RotatE: Knowledge graph embedding by relational rotation in complex space [EB/OL]. (2019-02-26)[2023-07-01]. https://doi.org/10.48550/arXiv.1902.10197.
10 CHEN S X, LIU X D, GAO J F, et al. HittER: Hierarchical transformers for knowledge graph embeddings [C]// Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics (ACL), 2021: 10395–10407.
11 SCHLICHTKRULL M, KIPF T N, BLOEM P, et al. Modeling relational data with graph convolutional networks [C]// The Semantic Web: 15th International Conference, ESWC 2018. Berlin: Springer-Verlag, 2018: 593–607.
12 XIAN Y K, FU Z H, MUTHUKRISHNAN S, et al. Reinforcement knowledge graph reasoning for explainable recommendation [C]// SIGIR’19: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieva. ACM, 2019: 285–294.
13 ORTONA S, MEDURI V V, PAPOTTI P. Robust discovery of positive and negative rules in knowledge bases [C]// 2018 IEEE 34th International Conference on Data Engineering (ICDE). IEEE, 2018: 1168-1179.
14 SADEGHIAN A, ARMANDPOUR M, DING P, et al. DRUM: End-to-end differentiable rule mining on knowledge graphs [C]// NIPS’19: Proceedings of the 33rd International Conference on Neural Information Processing Systems. Red Hook NY, United States: Curran Associates Inc., 2019: 15347–15357.
15 XU Z Z, YE P, CHEN H, et al. Ruleformer: Context-aware rule mining over knowledge graph [C]// Proceedings of the 29th International Conference on Computational Linguistics. International Committee on Computational Linguistics, 2022: 2551–2560.
16 LIN Q K, MAO R, LIU J, et al.. Fusing topology contexts and logical rules in language models for knowledge graph completion. Information Fusion, 2023, 90, 253- 264.
17 VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need [C]// NIPS’17: Proceedings of the 31st International Conference on Neural Information Processing Systems. Red Hook NY, United States: Curran Associates Inc., 2017: 6000?6010.
18 HUMPHREYS B L, FIOL G D, XU H.. The UMLS knowledge sources at 30: Indispensable to current research and applications in biomedical informatics. Journal of the American Medical Informatics Association, 2020, 27 (10): 1499- 1501.
19 ISLAM M K, ARIDHI S, SMAIL-TABBONE M.. Negative sampling and rule mining for explainable link prediction in knowledge graphs. Knowledge-Based Systems, 2022, 250, 109083.
20 FENG W Y, ZHA D R, WANG L, et al. Convolutional 3D embedding for knowledge graph completion [C]// 2022 IEEE 25th International Conference on Computer Supported Cooperative Work in Design (CSCWD). IEEE, 2022: 1197-1202.
21 YANG F, YANG Z L, COHEN W W. Differentiable learning of logical rules for knowledge base reasoning [C]// Proceedings of the 31st International Conference on Neural Information Processing Systems. Red Hook, NY, United States: Curran Associates Inc., 2017: 2316-2325.
22 XIE Z W, ZHOU G Y, LIU J, et al. ReInceptionE: Relation-aware inception network with joint local-global structural information for knowledge graph embedding [C]// Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics(ACL), 2020: 5929-5939.
23 KUMAR S, MALLIK A, KHETARPAL A, et al.. Influence maximization in social networks using graph embedding and graph neural network. Information Sciences, 2022, 607, 1617- 1636.
24 BERAHMAND K, NASIRI E, ROSTAMI M, et al.. A modified DeepWalk method for link prediction in attributed social network. Computing, 2021, 103, 2227- 2249.
25 LIU R M, KRISHNAN A.. PecanPy: A fast, efficient and parallelized Python implementation of node2vec. Bioinformatics, 2021, 37 (19): 3377- 3379.
26 LIU P F, YUAN W Z, FU J L, et al.. Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing. ACM Computing Surveys, 2023, 55 (9): 195.
27 HUANG J N, LI Z Y, CHEN B H, et al. Scallop: From probabilistic deductive databases to scalable differentiable reasoning [C]// Advances in Neural Information Processing Systems 34 (NeurIPS 2021). 2021: 25134-25145.
文章导航

/