Temporal characteristics are prominently evident in a substantial volume of knowledge, which underscores the pivotal role of Temporal Knowledge Graphs (TKGs) in both academia and industry. However, TKGs often suffer from incompleteness for three main reasons: the continuous emergence of new knowledge, the weakness of the algorithm for extracting structured information from unstructured data, and the lack of information in the source dataset. Thus, the task of Temporal Knowledge Graph Completion (TKGC) has attracted increasing attention, aiming to predict missing items based on the available information. In this paper, we provide a comprehensive review of TKGC methods and their details. Specifically, this paper mainly consists of three components, namely, 1)Background, which covers the preliminaries of TKGC methods, loss functions required for training, as well as the dataset and evaluation protocol; 2)Interpolation, that estimates and predicts the missing elements or set of elements through the relevant available information. It further categorizes related TKGC methods based on how to process temporal information; 3)Extrapolation, which typically focuses on continuous TKGs and predicts future events, and then classifies all extrapolation methods based on the algorithms they utilize. We further pinpoint the challenges and discuss future research directions of TKGC.

翻译：时间特性在大量知识中显著存在，这突显了时间知识图谱（TKGs）在学术界和工业界的关键作用。然而，由于三个主要原因，TKGs常常面临不完整性问题：新知识的不断涌现、从非结构化数据中提取结构化信息的算法薄弱，以及源数据集信息缺失。因此，时间知识图谱补全（TKGC）任务日益受到关注，旨在基于现有信息预测缺失项。本文对TKGC方法及其细节进行了全面综述。具体而言，本文主要包括三个部分：1）背景，涵盖TKGC方法的基础知识、训练所需的损失函数，以及数据集和评估协议；2）插值，通过相关可用信息估计和预测缺失元素或元素集合，并进一步根据处理时间信息的方式对相关TKGC方法进行分类；3）外推，通常聚焦于连续TKGs并预测未来事件，然后基于所用算法对所有外推方法进行分类。我们进一步指出了TKGC面临的挑战并讨论了未来研究方向。