Temporal Knowledge Graph (TKG) reasoning focuses on predicting events through historical information within snapshots distributed on a timeline. Existing studies mainly concentrate on two perspectives of leveraging the history of TKGs, including capturing evolution of each recent snapshot or correlations among global historical facts. Despite the achieved significant accomplishments, these models still fall short of (1) investigating the influences of multi-granularity interactions across recent snapshots and (2) harnessing the expressive semantics of significant links accorded with queries throughout the entire history, especially events exerting a profound impact on the future. These inadequacies restrict representation ability to reflect historical dependencies and future trends thoroughly. To overcome these drawbacks, we propose an innovative TKG reasoning approach towards \textbf{His}torically \textbf{R}elevant \textbf{E}vents \textbf{S}tructuring ($\mathsf{HisRES}$). Concretely, $\mathsf{HisRES}$ comprises two distinctive modules excelling in structuring historically relevant events within TKGs, including a multi-granularity evolutionary encoder that captures structural and temporal dependencies of the most recent snapshots, and a global relevance encoder that concentrates on crucial correlations among events relevant to queries from the entire history. Furthermore, $\mathsf{HisRES}$ incorporates a self-gating mechanism for adaptively merging multi-granularity recent and historically relevant structuring representations. Extensive experiments on four event-based benchmarks demonstrate the state-of-the-art performance of $\mathsf{HisRES}$ and indicate the superiority and effectiveness of structuring historical relevance for TKG reasoning.

翻译：时间知识图谱（TKG）推理侧重于通过时间线上快照分布的历史信息预测事件。现有研究主要关注利用TKG历史的两个视角，包括捕捉每个近期快照的演化过程或全局历史事实间的关联。尽管取得了显著成就，这些模型仍存在不足：（1）未能充分探究跨近期快照的多粒度交互影响；（2）未能有效利用整个历史中与查询相关的显著链接的丰富语义，尤其是对未来产生深远影响的事件。这些局限性限制了模型全面反映历史依赖与未来趋势的表征能力。为克服上述不足，我们提出一种创新的TKG推理方法——**His**torically **R**elevant **E**vents **S**tructuring（历史相关事件结构建模，$\mathsf{HisRES}$）。具体而言，$\mathsf{HisRES}$包含两个擅长对TKG中历史相关事件进行结构建模的独特模块：多粒度演化编码器，捕捉最新快照的结构与时间依赖；全局相关编码器，聚焦整个历史中与查询相关事件的关键关联。此外，$\mathsf{HisRES}$引入自门控机制，自适应融合多粒度近期与历史相关结构表征。在四个基于事件的基准数据集上的广泛实验表明，$\mathsf{HisRES}$达到了最先进性能，并验证了历史相关结构建模用于TKG推理的优越性与有效性。