Adnan KhanAdnanProduct & AI
San Francisco Bay Area
Get in touch

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

Read the source

Source

Plain-Language Brief

The authors propose a new method called RAPTOR for improving the efficiency of target-oriented path exploration in temporal knowledge graphs (TKG). The method injects a reachability-aware inductive bias to the agent, allowing it to estimate the reachability of candidate actions to the target entity. This reduces exploration over unpromising paths and provides a strong initialization for downstream reinforcement learning fine-tuning. Experimental results show that RAPTOR pretraining significantly improves training efficiency and outperforms conventional baselines.

What the Document Says

  • The paper discusses Temporal Knowledge Graph (TKG) reasoning in the extrapolation setting, which focuses on predicting future time-stamped events from historical data.
  • Existing RL-based multi-hop reasoning methods are used for TKG reasoning due to their ability to produce human-interpretable predictions via explicit multi-hop path tracing.
  • However, these methods have challenges during RL training: rewards are sparse, and exploration is highly inefficient due to the vast, time-evolving action space.
  • The authors propose a solution called RAPTOR (Reachability-Aware Pretraining for Efficient Target-Oriented Path Exploration).
  • RAPTOR learns to estimate the reachability of candidate actions to the target entity, reducing exploration over unpromising paths.
  • RAPTOR provides a strong initialization for downstream 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)
  • Reinforcement Learning (RL)
  • Multi-hop reasoning
  • Pretraining
  • Reachability-aware inductive bias
  • RAPTOR

Claims Made by the Document

  • Existing RL-based multi-hop reasoning methods have challenges during RL training.
  • The proposed RAPTOR method addresses these challenges by injecting a reachability-aware inductive bias to the agent and improving training efficiency.
  • RAPTOR pretraining consistently outperforms conventional baselines.

Evidence Used

  • The authors mention that existing approaches have sparse rewards and highly inefficient exploration due to the vast, time-evolving action space.
  • The authors propose RAPTOR as a solution and describe its functionality.
  • Experimental results on ICEWS14, ICEWS05-15, and ICEWS18 datasets are provided to demonstrate the effectiveness of RAPTOR.

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

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.