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Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 42, 2025 No. 1
93-99

Knowledge graph completion method based on bipartite graphs and attention mechanism

Zhou Yue
Fan Yongsheng
Sang Binbin
Zhou Yan
College of Computer & Information Science, Chongqing Normal University, Chongqing 401331, China
DOI: 10.19734/j.issn.1001-3695.2024.06.0186

Abstract

To address the issue of existing knowledge graph completion methods' limited capability in capturing structural information within knowledge graphs, this paper proposed a novel model that leveraged bipartite graphs and an attention mechanism to acquire global structural insights and facilitate automatic knowledge graph completion. This model firstly constructed two subgraphs centered on entities and relationships to capture potential useful information about entity neighborhood and relationship structures, and inputted the information formed by the two subgraphs into the encoder to better update entity and relationship structure information. Then, it used attention mechanisms to adaptively learn important interaction features between updated entities and relationships. Finally, it inputted the feature vectors containing global structural information into the decoder, and it actively employed a scoring function to assess and predict scores for the input feature edges, ultimately utilizing the predicted outcomes to accomplish the task of knowledge graph completion. Comparing the performance of the proposed method with the baseline method on the FB15K-237 and NELL995 datasets, the MRR and hits@10 evaluation indicators achieved significant improvements of 5.1, 8.8, and 3.4, 2.2 percentage points, respectively. At the same time, on the WN18RR dataset, these two indicators also were improved by 0.1 and 1.9 percentage points, respectively. The experimental results show that established model proactively adopts a structure that effectively captures the global structural information of the knowledge graph, thereby significantly enhancing the expression ability and predictive performance of the model.

Foundation Support

国家自然科学基金青年科学基金资助项目(62306054)
重庆师范大学研究生科研创新资助项目(YKC23022)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2024.06.0186
Publish at: Application Research of Computers Printed Article, Vol. 42, 2025 No. 1
Section: Algorithm Research & Explore
Pages: 93-99
Serial Number: 1001-3695(2025)01-013-0093-07

Publish History

[2025-01-05] Printed Article

Cite This Article

周粤, 范永胜, 桑彬彬, 等. 基于双子图和注意力机制的知识图谱补全方法 [J]. 计算机应用研究, 2025, 42 (1): 93-99. (Zhou Yue, Fan Yongsheng, Sang Binbin, et al. Knowledge graph completion method based on bipartite graphs and attention mechanism [J]. Application Research of Computers, 2025, 42 (1): 93-99. )

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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