用可到达性预训练提升时间知识图谱推理的路径探索效率
Reachability-Aware Pretraining for Efficient Target-Oriented Path Exploration in Temporal Knowledge Graph Reasoning
- 通过可到达性预训练注入先验知识，指导智能体避开无效路径
- 在ICEWS14/05-15/18上训练效率显著提升，性能优于基线方法
- 适合需要高效多跳推理的时间知识图谱任务研究者
时间知识图谱（TKG）推理中的外推设定旨在从历史数据中预测未来时间戳事件。现有基于强化学习（RL）的多跳推理方法因能生成可解释的路径追踪结果而突出，但其训练中奖励稀疏，且动作空间随时间动态变化，导致探索效率低下，影响整体性能。为此，我们提出RAPTOR（可到达性感知预训练），一种自监督预训练方法，通过学习候选动作对目标实体的可达性，为智能体注入可到达性归纳偏置。该方法减少对无望路径的探索，为下游RL微调提供强初始化。在ICEWS14、ICEWS05-15和ICEWS18数据集上的实验表明，使用RAPTOR预训练后，训练效率明显提升，性能持续优于传统基线，验证了其在增强基于强化学习的多跳推理方法方面的有效性。
原文摘要 · Abstract (English)
Temporal Knowledge Graph (TKG) reasoning under the extrapolation setting focuses on forecasting future time-stamped events (facts) from historical data in a temporal knowledge graph. Existing approaches, reinforcement learning (RL)-based multi-hop reasoning methods are prominent for TKG reasoning because they produce human-interpretable predictions via explicit multi-hop path tracing. However, during RL training, rewards are typically sparse, and exploration is highly inefficient due to the vast, time-evolving action space. These issues hinder efficient training and often limit overall performance. To address these challenges, we propose RAPTOR (Reachability-Aware Pretraining for Efficient Target-Oriented Path Exploration), a self-supervised pretraining method that injects a reachability-aware inductive bias to the agent. By learning to estimate the reachability of candidate actions to the target entity, RAPTOR reduces exploration over unpromising paths and provides a strong initialization for downstream RL fine-tuning. Experimental results on the ICEWS14, ICEWS05-15, and ICEWS18 datasets demonstrate that RAPTOR pretraining markedly improves the training efficiency and consistently outperforms conventional baselines, establishing it as an effective approach for enhancing RL-based multi-hop reasoning methods for TKG reasoning.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。