LengthPath: The Length Reward of Knowledge Graph Reasoning Based on Deep Reinforcement Learning

• Published in: 2024 International Joint Conference on Neural Networks (IJCNN) pp. 1 - 8
• Main Authors: Ling-Xiao, Xu, Lin, Feng, Zi-Hao, Li, Ling, Yue, Qiu-Ping, Shuai, Jie-Wei, Li
• Format: Conference Proceeding
• Language: English
• Published: IEEE 30.06.2024
• Subjects: Cognition; Deep reinforcement learning; Knowledge Graph Reasoning; Knowledge graphs; length reward; Neural networks; Reinforcement Learning; Semantics; Statistical analysis; Trajectory
• DOI: 10.1109/IJCNN60899.2024.10650504

Knowledge Graph (KG) always suffers from incompleteness. Knowledge Graph Reasoning (KGR) aims to predict the unknown entity or find reasoning paths for relations over incomplete KG. However, multi-hop reasoning still facing challenges, because the process of reasoning usually experiences the neighbor information issue. Prior works just use path efficiency as a part of reward function and do not utilize the neighbor information effectively. In order to deal with the situation, we propose the length reward in the Reinforcement Learning (RL) framework to represent the length of reasoning paths and use the neighbor information effectively. Our model utilizes the position information to design reward function. To solve this problem of save the semantic information of entity neighbors and historical trajectory information, we propose a new GRU-GAT framework to capture neighbor feature of the current entity and the target entity. Experimental results on NELL-995 and FB15K237 demonstrate the effectiveness of our model and our model can identify a more balanced route for every relation.