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Key Takeaways

  • Reachability-Aware Pretraining for Efficient Target-Oriented Path Exploration in Temporal Knowledge Graph Reasoning Temporal Knowledge Graphs (TKGs) track ho...
  • 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.
Paper AbstractExpand

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.

Reachability-Aware Pretraining for Efficient Target-Oriented Path Exploration in Temporal Knowledge Graph Reasoning
Temporal Knowledge Graphs (TKGs) track how relationships between entities change over time, making them essential for forecasting future events. While reinforcement learning (RL) is a popular way to reason through these graphs by tracing multi-hop paths, it faces a significant hurdle: the "exploration problem." Because the number of possible paths grows exponentially and rewards are often sparse, agents struggle to find the correct path to a target entity, leading to slow and unstable training. This paper introduces RAPTOR, a self-supervised pretraining method designed to guide these agents by teaching them which paths are actually capable of reaching a target before they begin their primary RL training.

The Challenge of Sparse Rewards

In TKG reasoning, an agent must navigate a vast, time-evolving network to find a missing piece of information. Because the agent must satisfy strict temporal constraints—such as ensuring that events in a path occur in a logical, non-decreasing chronological order—many potential paths lead to dead ends. In standard RL, the agent receives a reward only if it successfully reaches the correct target. When most paths are "unreachable" or invalid, the agent spends most of its time exploring useless trajectories, resulting in sparse feedback that hinders learning efficiency and overall accuracy.

How RAPTOR Works

RAPTOR addresses this by injecting a "reachability-aware" bias into the agent before the main RL training begins. The process involves two main steps: 1. Reachable Entity Labeling: The researchers developed an algorithm that performs a backward search from the target entity. By looking at the graph in reverse, the system identifies which entities and actions can successfully reach the target within a set number of steps while respecting temporal rules. 2. Self-Supervised Pretraining: Using these labels, the agent is trained to predict whether a candidate action is "reachable" or "unreachable." By learning to prioritize actions that have a high probability of leading to the target, the agent effectively prunes unpromising paths early on. This provides a strong, informed starting point for the subsequent RL fine-tuning phase.

Performance and Results

The researchers evaluated RAPTOR on three standard datasets: ICEWS14, ICEWS05-15, and ICEWS18. The experimental results show that this pretraining approach significantly improves both the speed of convergence and the final prediction accuracy compared to conventional baseline methods. By focusing the agent’s exploration on feasible paths, RAPTOR demonstrates that incorporating structural knowledge about the graph’s reachability is a highly effective way to enhance multi-hop reasoning in dynamic, time-sensitive environments.

Key Considerations

RAPTOR is specifically designed for RL-based multi-hop reasoning in temporal settings. While it successfully mitigates the issue of inefficient exploration, it relies on the ability to perform a backward search to generate supervision labels. The method is intended to serve as a foundational step that prepares the agent for the more complex task of reinforcement learning, ensuring that the agent is not "blindly" searching the graph but is instead guided by a learned understanding of the graph's connectivity and temporal constraints.

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