计算机应用研究2025，Vol.42Issue(1)：71-77,7.DOI:10.19734/j.issn.1001-3695.2024.06.0197
融合Bi-LSTM与多头注意力的分层强化学习推理方法
Hierarchical reinforcement learning knowledge reasoning method integrating Bi-LSTM and multi-head attention
摘要
Abstract
Knowledge reasoning is a critical task in knowledge graph completion and has garnered significant academic atten-tion.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.关键词
知识推理/分层强化学习/Bi-LSTM/多头注意力机制Key words
knowledge reasoning/layered reinforcement learning/Bi-LSTM/multi-head attention mechanism分类
计算机与自动化引用本文复制引用
李卫军,刘世侠,刘雪洋,丁建平,苏易礌,王子怡..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):71-77,7.基金项目
宁夏高等学校科学研究项目(NYG2024086) （NYG2024086）
宁夏自然科学基金资助项目 (2021AAC03215) （2021AAC03215）
中央高校科研资助项目(2022PT_S04,2021JCYJ12) （2022PT_S04,2021JCYJ12）
国家自然科学基金资助项目(62066038,61962001) （62066038,61962001）
