Temporal Knowledge Graph Completion (TKGC) is a challenging task of
predicting missing event links at future timestamps by leveraging established
temporal structural knowledge. Given the formidable generative capabilities
inherent in LLMs (LLMs), this paper proposes a novel approach to conceptualize
temporal link prediction as an event generation task within the context of a
historical event chain. We employ efficient fine-tuning methods to make LLMs
adapt to specific graph textual information and patterns discovered in temporal
timelines. Furthermore, we introduce structure-based historical data
augmentation and the integration of reverse knowledge to emphasize LLMs'
awareness of structural information, thereby enhancing their reasoning
capabilities. We conduct thorough experiments on multiple widely used datasets
and find that our fine-tuned model outperforms existing embedding-based models
on multiple metrics, achieving SOTA results. We also carry out sufficient
ablation experiments to explore the key influencing factors when LLMs perform
structured temporal knowledge inference tasks.

本论文提出了一种新颖的方法，将时间链接预测视为历史事件链中的事件生成任务，并通过有效的微调方法将 LLMs 适应特定的图文本信息和时间线中发现的模式。此外，我们引入了基于结构的历史数据增强和逆向知识的整合，以强调 LLMs 对结构信息的认知，从而提高其推理能力。通过在多个广泛使用的数据集上进行彻底的实验，我们发现我们的微调模型在多个度量标准上优于现有的基于嵌入的模型，达到了最先进水平，并进行了充分的消融实验，探索 LLMs 在执行结构化时间知识推断任务时的关键影响因素。