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Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 42, 2025 No. 1
106-110

Multi-modal entity alignment method based on adaptive mutual information

Gao Yongjie1a,2
Dang Jianwu1b,2
Zhang Xiquan1b,2
Zheng Aiguo3
1. a. The Ministry of Education Key Laboratory of Optoelectronic Technology & Intelligent Control, b. School of Electronics & Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China
2. National Virtual Simulation Teaching Center for Rail Transit Information & Control, Lanzhou 730070, China
3. Tianjin Telecommunication Section of China Railway Beijing Group Co. , Ltd. , Tianjin 300143, China
DOI: 10.19734/j.issn.1001-3695.2024.06.0193

Abstract

Multi-modal entity alignment is a critical step in the process of knowledge fusion. However, heterogeneous multi-modal knowledge graphs exhibit significant structural differences, and their multi-modal information is often incomplete, leading to suboptimal alignment outcomes when using current multi-modal entity alignment methods. To address these issues, this paper proposed a multi-modal entity alignment method based on adaptive mutual information. On the one hand, it designed an adaptive fusion mechanism to reduce modal differences and dynamically assign weights based on the contribution of modal information. On the other hand, it introduced mutual information as an additional feature to enhance the representation of entity features. Finally, it performed entity alignment using entity similarity calculations. Experimental results on five common datasets show that the MAMEA outperforms current baseline models, with a maximum improvement of 1.8% and a minimum improvement of 1.4% in the hits@1 metric, and a maximum improvement of 1.4% and a minimum improvement of 0.8% in the MRR metric. These results demonstrate that the proposed model can effectively enhance the performance of multi-modal entity alignment.

Foundation Support

国家自然科学基金资助项目(62067006,62367005)
中央引导地方科技发展资金资助项目(332140068864)
甘肃省高校科研创新平台重大培育项目(2024CXPT-17)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2024.06.0193
Publish at: Application Research of Computers Printed Article, Vol. 42, 2025 No. 1
Section: Algorithm Research & Explore
Pages: 106-110
Serial Number: 1001-3695(2025)01-015-0106-05

Publish History

[2025-01-05] Printed Article

Cite This Article

高永杰, 党建武, 张希权, 等. 基于互信息自适应的多模态实体对齐方法 [J]. 计算机应用研究, 2025, 42 (1): 106-110. (Gao Yongjie, Dang Jianwu, Zhang Xiquan, et al. Multi-modal entity alignment method based on adaptive mutual information [J]. Application Research of Computers, 2025, 42 (1): 106-110. )

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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