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.

时间知识图谱的时序特性在学术界和工业界中具有重要意义，然而，由于新知识的不断涌现、从非结构化数据中提取结构化信息的算法的薄弱性以及源数据集中信息的缺乏，时间知识图谱往往存在不完整性。因此，基于已有信息预测缺失项的任务 —— 时间知识图谱补全引起了越来越多的关注。本文综述了时间知识图谱补全方法及其细节，并包含三个主要组成部分：1）背景，涵盖了时间知识图谱补全方法的基础知识，训练所需的损失函数，以及数据集和评估协议；2）内插，通过相关可用信息估计和预测缺失元素或元素集合，进一步根据处理时间信息的方式对相关时间知识图谱补全方法进行分类；3）外推，主要关注连续时间知识图谱，并预测未来事件，然后根据所使用的算法对所有外推方法进行分类。我们进一步指出了挑战并讨论了时间知识图谱补全的未来研究方向。