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.

翻译：时间知识图谱补全（TKGC）是一项利用已建立的时序结构知识预测未来时间点缺失事件链接的挑战性任务。鉴于大语言模型（LLMs）固有的强大生成能力，本文提出了一种新颖方法，将时序链接预测概念化为历史事件链语境下的事件生成任务。我们采用高效微调方法，使LLMs适应特定图结构文本信息及时序时间线中发现的模式。此外，我们引入基于结构的历史数据增强与反向知识整合机制，以强化LLMs对结构信息的感知能力，从而增强其推理性能。我们在多个广泛使用的数据集上开展全面实验，发现微调后的模型在多项指标上超越现有基于嵌入的模型，达到最优结果。同时，通过充分的消融实验，我们探究了LLMs执行结构化时序知识推理任务时的关键影响因素。