In accordance with regulations and requirements, the editorial department's website domain has been changed to arocmag.cn. The original domain (arocmag.com) has been redirecting to new domain since Jan. 1st, 2025.
See More
Algorithm Research & Explore
|
App. Res. of Comp. Printed Article
Vol. 42, 2025 No. 1
71-77

Hierarchical reinforcement learning knowledge reasoning method integrating Bi-LSTM and multi-head attention

Li Weijuna,b
Liu Shixiaa
Liu Xueyanga
Ding Jianpinga
Su Yileia
Wang Ziyia
a. College of Computer Science & Technology, b. Key Laboratory of Images & Graphics Intelligent Processing of State Ethnic Affairs Commission, North Minzu University, Yinchuan 750021, China
DOI: 10.19734/j.issn.1001-3695.2024.06.0197

Abstract

Knowledge reasoning is a critical task in knowledge graph completion and has garnered significant academic attention. Addressing issues such as poor interpretability, inability to utilize hidden semantic information, and sparse rewards, this paper proposed a hierarchical reinforcement learning method integrating Bi-LSTM and multi-head attention mechanisms. The knowledge graph was clustered via spectral clustering, enabling agents to reason between clusters and entities. The Bi-LSTM and multi-head attention mechanism module processed the agent's historical information, effectively uncovering and utilizing hidden semantic information in the knowledge graph. The high-level agent selected the cluster containing the target entity through a hierarchical policy network, guiding the low-level agent in entity reasoning. Reinforcement learning allows the agents to solve interpretability issues, and a mutual reward mechanism addresses sparse rewards by rewarding agents' action choices and search paths. Experimental results on FB15K-237, WN18RR, and NELL-995 datasets show that the proposed method captures long-term dependencies in sequential data for long-path reasoning, outperforming similar methods in reasoning tasks.

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)
中央高校科研资助项目(2022PT_S04,2021JCYJ12)
国家自然科学基金资助项目(62066038,61962001)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2024.06.0197
Publish at: Application Research of Computers Printed Article, Vol. 42, 2025 No. 1
Section: Algorithm Research & Explore
Pages: 71-77
Serial Number: 1001-3695(2025)01-010-0071-07

Publish History

[2025-01-05] Printed Article

Cite This Article

李卫军, 刘世侠, 刘雪洋, 等. 融合Bi-LSTM与多头注意力的分层强化学习推理方法 [J]. 计算机应用研究, 2025, 42 (1): 71-77. (Li Weijun, Liu Shixia, Liu Xueyang, et al. Hierarchical reinforcement learning knowledge reasoning method integrating Bi-LSTM and multi-head attention [J]. Application Research of Computers, 2025, 42 (1): 71-77. )

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.

Indexed & Evaluation

  • The Second National Periodical Award 100 Key Journals
  • Double Effect Journal of China Journal Formation
  • the Core Journal of China (Peking University 2023 Edition)
  • the Core Journal for Science
  • Chinese Science Citation Database (CSCD) Source Journals
  • RCCSE Chinese Core Academic Journals
  • Journal of China Computer Federation
  • 2020-2022 The World Journal Clout Index (WJCI) Report of Scientific and Technological Periodicals
  • Full-text Source Journal of China Science and Technology Periodicals Database
  • Source Journal of China Academic Journals Comprehensive Evaluation Database
  • Source Journals of China Academic Journals (CD-ROM Version), China Journal Network
  • 2017-2019 China Outstanding Academic Journals with International Influence (Natural Science and Engineering Technology)
  • Source Journal of Top Academic Papers (F5000) Program of China's Excellent Science and Technology Journals
  • Source Journal of China Engineering Technology Electronic Information Network and Electronic Technology Literature Database
  • Source Journal of British Science Digest (INSPEC)
  • Japan Science and Technology Agency (JST) Source Journal
  • Russian Journal of Abstracts (AJ, VINITI) Source Journals
  • Full-text Journal of EBSCO, USA
  • Cambridge Scientific Abstracts (Natural Sciences) (CSA(NS)) core journals
  • Poland Copernicus Index (IC)
  • Ulrichsweb (USA)