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

Inductive relation prediction based on sentence transformer and dual attention mechanism

Li Weijuna,b
Liu Xueyanga
Liu Shixiaa
Wang Ziyia
Ding Jianpinga
Su Yileia
a. College of Computer Science & Technology, b. Key Laboratory of lmages & Graphics Intelligent Processing of State Ethnic Affairs Commission, North Minzu University, Yinchuan 750021, China
DOI: 10.19734/j.issn.1001-3695.2024.11.0442

Abstract

Relation prediction is a important task in knowledge graph completion, aimed at predicting missing relationships between entities. Existing inductive relation prediction methods often face challenges in adequately modeling semantic and structural information. To address this issue, we propose an inductive relation prediction model based on sentence transformation and a dual-attention mechanism. The proposed method enhances entity semantic representations by automatically retrieving descriptions and incorporates a dual-attention mechanism, which considers edge and relation awareness, to accurately model the complex interactions between entities. First, we extract the closed subgraph of the target triple and use a random walk strategy to search for multi-hop relational paths. These triples and paths are then transformed into natural language sentences, generating semantically rich sentence embeddings. Next, we update the subgraph embeddings using GCN and bidirectional GRU, combining sentence and subgraph embeddings to capture both structural and semantic information. Experimental results on three public datasets—WN18RR, FB15k-237, and NELL-995—demonstrate that the proposed method outperforms existing methods in both transformation and inductive relation prediction tasks, validating the importance of the dual-attention mechanism and sentence transformation in improving model performance. This approach effectively enhances the accuracy and efficiency of relation prediction in knowledge graphs.

Foundation Support

宁夏高等学校科学研究项目(NYG2024086)
宁夏自然科学基金资助项目(2021AAC03215)
中央高校科研(2022PT_S04,2021JCYJ12)
国家自然科学基金资助项目(62066038,61962001)
北方民族大学研究生创新项目(YCX24127)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2024.11.0442
Publish at: Application Research of Computers Accepted Paper, Vol. 42, 2025 No. 6

Publish History

[2025-03-03] Accepted Paper

Cite This Article

李卫军, 刘雪洋, 刘世侠, 等. 基于句子转换和双注意力机制的归纳关系预测 [J]. 计算机应用研究, 2025, 42 (6). (2025-03-10). https://doi.org/10.19734/j.issn.1001-3695.2024.11.0442. (Li Weijun, Liu Xueyang, Liu Shixia, et al. Inductive relation prediction based on sentence transformer and dual attention mechanism [J]. Application Research of Computers, 2025, 42 (6). (2025-03-10). https://doi.org/10.19734/j.issn.1001-3695.2024.11.0442. )

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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