  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.
