Enhancing Multi-Hop Knowledge Graph Reasoning with Reward Shaping Techniques

The author employs reinforcement learning strategies, specifically the REINFORCE algorithm, to address challenges in multi-hop Knowledge Graph Reasoning due to incomplete data. By refining reward shaping through pre-trained embeddings and Prompt Learning, the study aims to improve accuracy and robustness in knowledge inference.