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Enhancing Multi-Hop Knowledge Graph Reasoning through Reward Shaping Techniques

Chen Li, Haotian Zheng, Yiping Sun, Cangqing Wang, Liqiang Yu, Che Chang, Xinyu Tian, Bo Liu

TL;DR

This research elucidates the employment of reinforcement learning strategies, notably the REINFORCE algorithm, to navigate the intricacies inherent in multi-hop KG-R, and investigates the efficacy of pretrained BERT embeddings and Prompt Learning methodologies to refine the reward shaping process.

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.

Enhancing Multi-Hop Knowledge Graph Reasoning through Reward Shaping Techniques

TL;DR

This research elucidates the employment of reinforcement learning strategies, notably the REINFORCE algorithm, to navigate the intricacies inherent in multi-hop KG-R, and investigates the efficacy of pretrained BERT embeddings and Prompt Learning methodologies to refine the reward shaping process.

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.
Paper Structure (6 sections, 3 figures, 1 table, 1 algorithm)

This paper contains 6 sections, 3 figures, 1 table, 1 algorithm.

Table of Contents

  1. Introduction
  2. Prior Work
  3. Reward Shaping
  4. Problem Statement
  5. Methodology
  6. Experiments

Figures (3)

  • Figure 1: In an incomplete knowledge graph, missing connections, indicated by dashed lines, may be inferred from existing data, represented by solid lines, suggesting a latent relational structure awaiting extrapolation from the established informational framework.
  • Figure 2: Comprehensive framework for enhancing rewards through applying prompt-based learning.
  • Figure 3: Comprehensive framework for enhancing rewards through applying prompt-based learning.