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Software Technology Research
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App. Res. of Comp. Printed Article
Vol. 42, 2025 No. 2
530-538

Knowledge graph link prediction based on multi-feature extraction and contrastive learning

Li Huayu
Li Haiyang
Wang Cuicui
Man Xiaojun
College of Computer Science & Technology, China University of Petroleum(East China), Qingdao Shandong 266580, China
DOI: 10.19734/j.issn.1001-3695.2024.06.0238

Abstract

Aiming to address issues such as the single perspective of traditional knowledge graph link prediction methods, limi-ted consideration of complex interactions between nodes during training, and the low quality of constructed negative triplets, this paper proposed a novel link prediction approach. This method aimed to fully utilize the interactions between nodes in the knowledge graph and the interactive information implied by the graph structure, considering the identification of missing facts in triplets from multiple feature perspectives. Firstly, it obtained embedded representations of nodes from different perspectives through various node feature extraction methods and aggregated neighboring node features to enhance their entity semantic information. Secondly, multiple convolution operations were employed to extract global relationships and transitional features between entities and relations, handling the interaction of entity and relation information through deep feature extraction. Lastly, by introducing contrastive learning, it intervened in the construction of negative triplets, simultaneously enhancing the features of negative triplets to improve the quality of constructed triplets. Finally, it filtered out predictive entities by calculating cosine similarity. The experimental results show that the proposed method improves several evaluation metrics in the knowledge graph link prediction task compared to the comparison model, and also verifies the effectiveness of the proposed method in dealing with complex knowledge graphs with multiple relationships.

Foundation Support

山东省自然科学基金资助项目(ZR2020MF140)
中国石油大学(华东)研究生创新基金资助项目(22CX04035A)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2024.06.0238
Publish at: Application Research of Computers Printed Article, Vol. 42, 2025 No. 2
Section: Software Technology Research
Pages: 530-538
Serial Number: 1001-3695(2025)02-029-0530-09

Publish History

[2025-02-05] Printed Article

Cite This Article

李华昱, 李海洋, 王翠翠, 等. 基于多特征提取和对比学习的知识图谱链接预测 [J]. 计算机应用研究, 2025, 42 (2): 530-538. (Li Huayu, Li Haiyang, Wang Cuicui, et al. Knowledge graph link prediction based on multi-feature extraction and contrastive learning [J]. Application Research of Computers, 2025, 42 (2): 530-538. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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