  In the realm of computational knowledge representation, Knowledge Graph
Reasoning (KG-R) stands at the forefront of facilitating sophisticated
inferential capabilities across multifarious domains. The quintessence of this
research elucidates the employment of reinforcement learning (RL) strategies,
notably the REINFORCE algorithm, to navigate the intricacies inherent in
multi-hop KG-R. This investigation critically addresses the prevalent
challenges introduced by the inherent incompleteness of Knowledge Graphs (KGs),
which frequently results in erroneous inferential outcomes, manifesting as both
false negatives and misleading positives. By partitioning the Unified Medical
Language System (UMLS) benchmark dataset into rich and sparse subsets, we
investigate the efficacy of pre-trained BERT embeddings and Prompt Learning
methodologies to refine the reward shaping process. This approach not only
enhances the precision of multi-hop KG-R but also sets a new precedent for
future research in the field, aiming to improve the robustness and accuracy of
knowledge inference within complex KG frameworks. Our work contributes a novel
perspective to the discourse on KG reasoning, offering a methodological
advancement that aligns with the academic rigor and scholarly aspirations of
the Natural journal, promising to invigorate further advancements in the realm
of computational knowledge representation.
