Enhancing Multi-Hop Knowledge Graph Reasoning through Reward Shaping Techniques

TL;DR

This paper improves multi-hop knowledge graph reasoning by applying reinforcement learning with reward shaping, leveraging BERT embeddings and prompt learning to address KG incompleteness and enhance inference accuracy.

Contribution

It introduces a novel reward shaping approach using pre-trained language models and prompt learning to improve reinforcement learning-based KG reasoning.

Findings

Enhanced reasoning precision on UMLS benchmark

Effective handling of KG incompleteness issues

Set new standards for KG inference accuracy

Abstract

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.