Temporal knowledge graphs (TKGs) have been identified as a promising approach to represent the dynamics of facts along the timeline. The extrapolation of TKG is to predict unknowable facts happening in the future, holding significant practical value across diverse fields. Most extrapolation studies in TKGs focus on modeling global historical fact repeating and cyclic patterns, as well as local historical adjacent fact evolution patterns, showing promising performance in predicting future unknown facts. Yet, existing methods still face two major challenges: (1) They usually neglect the importance of historical information in KG snapshots related to the queries when encoding the local and global historical information; (2) They exhibit weak anti-noise capabilities, which hinders their performance when the inputs are contaminated with noise.To this end, we propose a novel \blue{Lo}cal-\blue{g}lobal history-aware \blue{C}ontrastive \blue{L}earning model (\blue{LogCL}) for TKG reasoning, which adopts contrastive learning to better guide the fusion of local and global historical information and enhance the ability to resist interference. Specifically, for the first challenge, LogCL proposes an entity-aware attention mechanism applied to the local and global historical facts encoder, which captures the key historical information related to queries. For the latter issue, LogCL designs four historical query contrast patterns, effectively improving the robustness of the model. The experimental results on four benchmark datasets demonstrate that LogCL delivers better and more robust performance than the state-of-the-art baselines.

翻译：时态知识图谱（TKG）已被视为沿时间线表示事实动态的有效方法。TKG的外推旨在预测未来发生的未知事实，在多个领域具有重要的实用价值。现有TKG外推研究主要聚焦于建模全局历史事实的重复与循环模式，以及局部历史相邻事实的演化模式，在预测未来未知事实方面展示了良好性能。然而，现有方法仍面临两大挑战：（1）在编码局部和全局历史信息时，通常忽略了知识图谱快照中与查询相关的历史信息的重要性；（2）抗噪能力较弱，当输入数据被噪声污染时，其性能受到阻碍。为此，我们提出了一种新颖的局部-全局历史感知对比学习模型（LogCL）用于TKG推理，该模型采用对比学习更好地引导局部与全局历史信息的融合，并增强抗干扰能力。具体而言，针对第一个挑战，LogCL提出了一种实体感知注意力机制，应用于局部和全局历史事实编码器，以捕获与查询相关的关键历史信息。针对第二个问题，LogCL设计了四种历史查询对比模式，有效提升了模型的鲁棒性。在四个基准数据集上的实验结果表明，LogCL相比当前最先进的基线方法实现了更优且更稳健的性能。