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App. Res. of Comp. Accepted Paper
Vol. 42, 2025 No. 6

Multimodal entity alignment based on dual-generator shared-adversarial network

Feng Guang1
Zheng Runting2
Liu Tianxiang2
Yang Yanru2
Lin Jianzhong1
Zhong Ting1
Huang Rongcan2
Xiang Feng2
Li Weichen2
1. School of Automation, Guangdong University of Technology, Guangzhou 510006, China
2. School of Computer Science, Guangdong University of Technology, Guangzhou 510006, China
DOI: 10.19734/j.issn.1001-3695.2024.11.0467

Abstract

In the field of education, knowledge graph fusion plays a crucial role. As a core technology of knowledge graph fusion, entity alignment aims to identify equivalent entity pairs across multiple knowledge graphs. Most existing entity alignment methods assume that each source entity has a corresponding entity in the target knowledge graph. However, when using cross-lingual and cross-graph entity sets, the problem of dangling entities arises. To address this issue, this paper proposes the Dual-Generator Shared-Adversarial Network Entity Alignment model (DGSAN-EA) . This model utilizes partial parameter sharing and an optimal selection strategy to train two generators, selecting the optimal generator to conditionally generate new entities across knowledge graphs, thereby enhancing the dataset and solving the dangling entity problem. Furthermore, a progressive fusion strategy and the introduction of a distribution consistency loss function effectively resolve the distortion of fused feature information and the misalignment between modalities in multimodal entity alignment. Validation on multiple public datasets shows that compared to existing multimodal entity alignment models, DGSAN-EA achieves higher Hist@k and MMR scores, demonstrating its effectiveness in entity alignment tasks.

Foundation Support

国家自然科学基金重点项目(62237001)
广东省哲学社会科学青年项目(GD23YJY08)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2024.11.0467
Publish at: Application Research of Computers Accepted Paper, Vol. 42, 2025 No. 6

Publish History

[2025-03-10] Accepted Paper

Cite This Article

冯广, 郑润庭, 刘天翔, 等. 基于生成对抗网络与渐进式融合的多模态实体对齐 [J]. 计算机应用研究, 2025, 42 (6). (2025-03-10). https://doi.org/10.19734/j.issn.1001-3695.2024.11.0467. (Feng Guang, Zheng Runting, Liu Tianxiang, et al. Multimodal entity alignment based on dual-generator shared-adversarial network [J]. Application Research of Computers, 2025, 42 (6). (2025-03-10). https://doi.org/10.19734/j.issn.1001-3695.2024.11.0467. )

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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