Temporal knowledge graphs (TKGs) can effectively model the ever-evolving
nature of real-world knowledge, and their completeness and enhancement can be
achieved by reasoning new events from existing ones. However, reasoning
accuracy is adversely impacted due to an imbalance between new and recurring
events in the datasets. To achieve more accurate TKG reasoning, we propose an
attention masking-based contrastive event network (AMCEN) with local-global
temporal patterns for the two-stage prediction of future events. In the
network, historical and non-historical attention mask vectors are designed to
control the attention bias towards historical and non-historical entities,
acting as the key to alleviating the imbalance. A local-global message-passing
module is proposed to comprehensively consider and capture multi-hop structural
dependencies and local-global temporal evolution for the in-depth exploration
of latent impact factors of different event types. A contrastive event
classifier is used to classify events more accurately by incorporating
local-global temporal patterns into contrastive learning. Therefore, AMCEN
refines the prediction scope with the results of the contrastive event
classification, followed by utilizing attention masking-based decoders to
finalize the specific outcomes. The results of our experiments on four
benchmark datasets highlight the superiority of AMCEN. Especially, the
considerable improvements in Hits@1 prove that AMCEN can make more precise
predictions about future occurrences.

提出了基于注意力掩蔽的对比事件网络（AMCEN），通过局部 - 全局时间模式对未来事件进行两步预测，以更准确地推理时态知识图谱中的事件，从而解决数据集中新事件和重复事件之间不平衡影响推理准确性的问题。在 AMCEN 网络中，设计了历史和非历史注意力掩蔽向量，以控制对历史和非历史实体的注意偏向，从而减轻不平衡。提出了局部 - 全局消息传递模块，全面考虑和捕捉多跳结构依赖和局部 - 全局时间演变，深入探索不同事件类型的潜在影响因素。使用对比事件分类器将局部 - 全局时间模式纳入对比学习，更准确地分类事件。因此，AMCEN 通过对比事件分类的结果细化预测范围，并利用基于注意力掩蔽的解码器最终确定特定结果。在四个基准数据集上的实验结果突出了 AMCEN 的优越性，特别是在 Hits@1 方面的显著改进，证明 AMCEN 可以更精确地预测未来事件的发生。