Abstract: Knowledge graph completion is a research hotspot in recent years, and it has broad application prospects in downstream applications, such as knowledge question answering, recommended system and intelligent search, etc. However, most of the completion methods ignore the dynamic characteristics of the knowledge graphs, many of which the facts will change over time. The new temporal knowledge graph completion methods take into account the limitations of the previous by incorporating time information, enabling the dynamic changes of the knowledge graph over time to be well captured. In response to the big potential of temporal knowledge graph completion methods in research fields such as social networks, transportation, finance and trade with complex time dependent characteristics, this paper summarizes temporal knowledge graph completion techniques. Based on the main different principle of model usage, completion methods based on logical rules, tensor decomposition, translation model, neural networks, deep reinforcement learning, and language model are summarized. The commonly used evaluation indicators, datasets, core ideas, advantages and disadvantages, applicable scenarios, and improvements on corresponding static models of existing methods are summarized. Finally, it looks forward to the future research directions.

Key words: knowledge graph, knowledge reasoning, link prediction, temporal knowledge graph completion

摘要： 知识图谱补全是近年来的研究热点，在下游应用中，如知识问答、推荐系统和智能搜索等都有着广泛的应用前景。然而，大部分补全方法忽略了知识图谱的动态特性，其中许多的事实都会随着时间的变化而发生改变。新兴的时序知识图谱补全方法考虑到了以往补全方法的局限性，在其中加入了时间信息，使得知识图谱随时间的动态变化也能很好地被捕获。针对时序知识图谱补全方法在社交网络、交通运输、金融贸易等动态变化且具有复杂时间依赖特性的研究领域所拥有的巨大潜力，梳理了时序知识图谱补全技术。根据模型主要使用原理的不同，总结了基于逻辑规则、张量分解、平移模型、神经网络、深度强化学习和语言模型的补全方法，归纳了现有方法的常用评价指标、公开数据集、核心思想、优缺点、适用场景以及在对应静态模型上的改进。最后，对时序知识图谱补全方法的未来研究方向进行了展望。

关键词: 知识图谱, 知识推理, 链接预测, 时序知识图谱补全