Temporal Knowledge Graph Reasoning (TKGR) is the process of utilizing temporal information to capture complex relations within a Temporal Knowledge Graph (TKG) to infer new knowledge. Conventional methods in TKGR typically depend on deep learning algorithms or temporal logical rules. However, deep learning-based TKGRs often lack interpretability, whereas rule-based TKGRs struggle to effectively learn temporal rules that capture temporal patterns. Recently, Large Language Models (LLMs) have demonstrated extensive knowledge and remarkable proficiency in temporal reasoning. Consequently, the employment of LLMs for Temporal Knowledge Graph Reasoning (TKGR) has sparked increasing interest among researchers. Nonetheless, LLMs are known to function as black boxes, making it challenging to comprehend their reasoning process. Additionally, due to the resource-intensive nature of fine-tuning, promptly updating LLMs to integrate evolving knowledge within TKGs for reasoning is impractical. To address these challenges, in this paper, we propose a Large Language Models-guided Dynamic Adaptation (LLM-DA) method for reasoning on TKGs. Specifically, LLM-DA harnesses the capabilities of LLMs to analyze historical data and extract temporal logical rules. These rules unveil temporal patterns and facilitate interpretable reasoning. To account for the evolving nature of TKGs, a dynamic adaptation strategy is proposed to update the LLM-generated rules with the latest events. This ensures that the extracted rules always incorporate the most recent knowledge and better generalize to the predictions on future events. Experimental results show that without the need of fine-tuning, LLM-DA significantly improves the accuracy of reasoning over several common datasets, providing a robust framework for TKGR tasks.

翻译：时序知识图谱推理（TKGR）是利用时序信息捕捉时序知识图谱（TKG）中复杂关系以推断新知识的过程。传统的TKGR方法通常依赖于深度学习算法或时序逻辑规则。然而，基于深度学习的TKGR方法往往缺乏可解释性，而基于规则的TKGR方法则难以有效学习能够捕捉时序模式的时序规则。最近，大语言模型（LLMs）展现出广泛的知识储备和卓越的时序推理能力。因此，利用LLMs进行时序知识图谱推理（TKGR）已引起研究者日益浓厚的兴趣。尽管如此，LLMs通常被视为黑箱模型，其推理过程难以理解。此外，由于微调过程资源消耗巨大，无法及时更新LLMs以整合TKG中不断演化的知识进行推理。为应对这些挑战，本文提出了一种大语言模型引导的动态适应（LLM-DA）方法，用于在TKG上进行推理。具体而言，LLM-DA利用LLMs的能力分析历史数据并提取时序逻辑规则。这些规则揭示了时序模式并促进了可解释的推理。考虑到TKG的演化特性，本文提出了一种动态适应策略，利用最新事件更新LLM生成的规则。这确保了所提取的规则始终包含最新知识，并能更好地泛化到对未来事件的预测上。实验结果表明，无需微调，LLM-DA在多个常用数据集上显著提高了推理准确性，为TKGR任务提供了一个鲁棒的框架。