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App. Res. of Comp. Accepted Paper
Vol. 43, 2026 No. 1

Relation extraction method integrating liquid neural networks and hierarchical graph convolution

Li Ziliang
Li Xingchun
School of Electronics & Information Engineering, Wuyi University, Jiangmen Guangdong 529020, China
DOI: 10.19734/j.issn.1001-3695.2025.06.0183

Abstract

This paper proposed a relation extraction model named BLGAM to address limitations in modeling long-distance dependencies and understanding complex semantics. The model first applied BERT to encode contextual semantics and obtain initial text representations. It used a liquid neural network based on a closed-form continuous-time solution to capture dynamic temporal features and model long-distance dependencies. The model then used dependency syntax and entity structures to construct a multi-level graph convolutional network for extracting local and global structural features. An attention-gating mechanism fused temporal and structural features, and a multi-layer perceptron enhanced relation recognition accuracy and robustness. Experiments on NYT and WebNLG datasets achieved F1 scores of 92.6% and 92.1%, respectively, surpassing mainstream baselines. Results demonstrate the superiority of liquid neural networks in long-distance dependency modeling and dynamic information capturing, and the complementary role of multi-level graph convolutional networks in uncovering implicit entity relationships. The method provides an efficient solution for relation extraction in complex semantic scenarios.

Foundation Support

广东省本科高校教学质量与教学改革工程建设项目(GDJX2023013)
五邑大学教学质量工程与教学改革工程项目(JX2023012)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2025.06.0183
Publish at: Application Research of Computers Accepted Paper, Vol. 43, 2026 No. 1

Publish History

[2025-09-13] Accepted Paper

Cite This Article

李子亮, 李兴春. 融合液态神经网络与多层级图卷积的关系抽取方法 [J]. 计算机应用研究, 2026, 43 (1). (2025-09-17). https://doi.org/10.19734/j.issn.1001-3695.2025.06.0183. (Li Ziliang, Li Xingchun. Relation extraction method integrating liquid neural networks and hierarchical graph convolution [J]. Application Research of Computers, 2026, 43 (1). (2025-09-17). https://doi.org/10.19734/j.issn.1001-3695.2025.06.0183. )

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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