Temporal knowledge graphs (TKGs) have become useful resources for numerous Artificial Intelligence applications, but they are far from completeness. Inferring missing events in temporal knowledge graphs is a fundamental and challenging task. However, most existing methods solely focus on entity features or consider the entities and relations in a disjoint manner. They do not integrate the features of entities and relations in their modeling process. In this paper, we propose TPmod, a tendency-guided prediction model, to predict the missing events for TKGs (extrapolation). Differing from existing works, we propose two definitions for TKGs: the Goodness of relations and the Closeness of entity pairs. More importantly, inspired by the attention mechanism, we propose a novel tendency strategy to guide our aggregated process. It integrates the features of entities and relations, and assigns varying weights to different past events. What is more, we select the Gate Recurrent Unit (GRU) as our sequential encoder to model the temporal dependency in TKGs. Besides, the Softmax function is employed to generate the final decreasing group of candidate entities. We evaluate our model on two TKG datasets: GDELT-5 and ICEWS-250. Experimental results show that our method has a significant and consistent improvement compared to state-of-the-art baselines.

中文翻译：

---

TPmod：时间知识图完成的趋势引导预测模型

时间知识图 (TKG) 已成为许多人工智能应用程序的有用资源，但它们远非完整。在时间知识图中推断缺失的事件是一项基本且具有挑战性的任务。然而，大多数现有方法仅关注实体特征或以不相交的方式考虑实体和关系。他们没有在建模过程中集成实体和关系的特征。在本文中，我们提出了 TPmod，一种趋势引导的预测模型，用于预测 TKG 的缺失事件（外推）。与现有工作不同，我们提出了 TKG 的两个定义：善良的关系和亲近的实体对。更重要的是，受注意力机制的启发，我们提出了一种新的趋势策略来指导我们的聚合过程。它整合了实体和关系的特征，并为不同的过去事件分配了不同的权重。更重要的是，我们选择门循环单元（GRU）作为我们的顺序编码器来模拟 TKG 中的时间依赖性。此外，Softmax 函数用于生成候选实体的最终递减组。我们在两个 TKG 数据集上评估我们的模型：GDELT-5 和 ICEWS-250。实验结果表明，与最先进的基线相比，我们的方法具有显着且一致的改进。