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Open Access

AInvR: Adaptive Learning Rewards for Knowledge Graph Reasoning Using Agent Trajectories

School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
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Abstract

Multi-hop reasoning for incomplete Knowledge Graphs (KGs) demonstrates excellent interpretability with decent performance. Reinforcement Learning (RL) based approaches formulate multi-hop reasoning as a typical sequential decision problem. An intractable shortcoming of multi-hop reasoning with RL is that sparse reward signals make performance unstable. Current mainstream methods apply heuristic reward functions to counter this challenge. However, the inaccurate rewards caused by heuristic functions guide the agent to improper inference paths and unrelated object entities. To this end, we propose a novel adaptive Inverse Reinforcement Learning (IRL) framework for multi-hop reasoning, called AInvR. (1) To counter the missing and spurious paths, we replace the heuristic rule rewards with an adaptive rule reward learning mechanism based on agent’s inference trajectories; (2) to alleviate the impact of over-rewarded object entities misled by inaccurate reward shaping and rules, we propose an adaptive negative hit reward learning mechanism based on agent’s sampling strategy; (3) to further explore diverse paths and mitigate the influence of missing facts, we design a reward dropout mechanism to randomly mask and perturb reward parameters for the reward learning process. Experimental results on several benchmark knowledge graphs demonstrate that our method is more effective than existing multi-hop approaches.

Keywords

Knowledge Graph Reasoning (KGR)Inverse Reinforcement Learning (IRL)multi-hop reasoning

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Tsinghua Science and Technology
Volume 28 Issue 6,
December 2023
Pages 1101-1114
DOI: 10.26599/TST.2022.9010063
Cite this article:
Zhang H, Lu G, Qin K, et al. AInvR: Adaptive Learning Rewards for Knowledge Graph Reasoning Using Agent Trajectories. Tsinghua Science and Technology, 2023, 28(6): 1101-1114. https://doi.org/10.26599/TST.2022.9010063

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Received: 17 July 2022
Revised: 02 December 2022
Accepted: 07 December 2022
Published: 28 July 2023
© The author(s) 2023.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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