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Source note2 min read

Reachability-Aware Pretraining for Efficient Target-Oriented Path Exploration in Temporal Knowledge Graph Reasoning

Source

arXiv

arxiv.org/abs/2607.14886v1

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Source

Plain-Language Brief

The paper proposes a new method called RAPTOR for temporal knowledge graph (TKG) reasoning under the extrapolation setting. This method aims to improve the efficiency of target-oriented path exploration by using reinforcement learning (RL). The novelty lies in introducing a reachability-aware inductive bias, which reduces exploration over unpromising paths and provides a strong initialization for RL fine-tuning.

What the Document Says

  • The research focuses on Temporal Knowledge Graph (TKG) reasoning under the extrapolation setting.
  • Existing approaches for TKG reasoning use reinforcement learning (RL)-based multi-hop reasoning methods that produce human-interpretable predictions via explicit multi-hop path tracing.
  • However, these methods suffer from sparse rewards and inefficient exploration due to a vast, time-evolving action space during RL training.
  • The paper introduces RAPTOR (Reachability-Aware Pretraining for Efficient Target-Oriented Path Exploration) as a solution.
  • RAPTOR learns to estimate the reachability of candidate actions to the target entity, reducing exploration over unpromising paths and providing a strong initialization for RL fine-tuning.
  • Experimental results show that RAPTOR pretraining improves training efficiency and outperforms conventional baselines on ICEWS14, ICEWS05-15, and ICEWS18 datasets.

Key Concepts Mentioned

  • Temporal Knowledge Graph (TKG) reasoning
  • Reinforcement Learning (RL)
  • Multi-hop reasoning
  • Reachability-Aware Pretraining
  • RAPTOR

Claims Made by the Document

  • Existing RL-based TKG reasoning methods suffer from sparse rewards and inefficient exploration.
  • RAPTOR addresses these issues by learning reachability of candidate actions to the target entity.
  • RAPTOR pretraining improves training efficiency and outperforms conventional baselines.

Evidence Used

  • The text mentions that existing RL-based TKG reasoning methods suffer from sparse rewards and inefficient exploration during RL training.
  • The introduction of RAPTOR is described as a solution to these issues by learning reachability of candidate actions to the target entity.
  • Experimental results on three datasets (ICEWS14, ICEWS05-15, and ICEWS18) show improved training efficiency and better performance compared to conventional baselines.

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

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Notes for Future Q&A

May include interpretation. Labeled where it does.

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Raw Abstract / Excerpt

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.