Temporal knowledge graphs, representing the dynamic relationships and interactions between entities over time, have been identified as a promising approach for event forecasting. However, a limitation of most temporal knowledge graph reasoning methods is their heavy reliance on the recurrence or periodicity of events, which brings challenges to inferring future events related to entities that lack historical interaction. In fact, the current state of affairs is often the result of a combination of historical information and underlying factors that are not directly observable. To this end, we investigate the limits of historical information for temporal knowledge graph extrapolation and propose a new event forecasting model called Contrastive Event Network (CENET) based on a novel training framework of historical contrastive learning. CENET learns both the historical and non-historical dependency to distinguish the most potential entities that best match the given query. Simultaneously, by launching contrastive learning, it trains representations of queries to probe whether the current moment is more dependent on historical or non-historical events. These representations further help train a binary classifier, whose output is a boolean mask, indicating the related entities in the search space. During the inference process, CENET employs a mask-based strategy to generate the final results. We evaluate our proposed model on five benchmark graphs. The results demonstrate that CENET significantly outperforms all existing methods in most metrics, achieving at least 8.3% relative improvement of Hits@1 over previous state-of-the-art baselines on event-based datasets.

翻译：时序知识图谱通过捕捉实体间随时间变化的动态关系和交互，已被视为一种有前景的事件预测方法。然而，大多数时序知识图谱推理方法的一个局限在于，它们过度依赖事件的重复性或周期性，这为推断与缺乏历史交互的实体相关的未来事件带来了挑战。事实上，当前的事态往往是历史信息与不可直接观测的潜在因素共同作用的结果。为此，我们研究了历史信息在时序知识图谱外推中的极限，并提出了一种基于新型历史对比学习训练框架的事件预测模型——对比事件网络（CENET）。CENET通过学习历史依赖与非历史依赖，以区分最符合给定查询的潜在实体。同时，通过启动对比学习，它训练查询的表示来探明当前时刻更依赖历史事件还是非历史事件。这些表示进一步用于训练一个二元分类器，其输出为布尔掩码，指示搜索空间中的相关实体。在推理过程中，CENET采用基于掩码的策略生成最终结果。我们在五个基准图上评估了所提模型。结果表明，CENET在大多数指标上显著优于所有现有方法，在基于事件的数据集上的Hits@1指标相比之前的最优基线实现了至少8.3%的相对提升。