Abstract: Knowledge graph is a semantic network used to describe various entities and concepts that exist in the world, as well as their relationships. In recent years, it has been widely used in fields such as intelligent question answering, intelligent recommendation, and information retrieval. At present, most knowledge graphs are incomplete, therefore, knowledge graph completion has become an important task. Firstly, based on the different methods of model construction, knowledge graph completion models are divided into three categories: traditional knowledge graph completion models, knowledge graph completion models based on neural networks, and knowledge graph completion models based on meta learning. The classification of these three knowledge graph completion models is introduced. Then, the dataset and evaluation indicators used in the knowledge graph completion method are summarized, and detailed comparative analysis is conducted on various models from the perspectives of their advantages and disadvantages. Finally, the knowledge graph completion is summarized and summarized, and future research directions are prospected.

Key words: knowledge graph, translation model, tensor decomposition, neural network, meta learning, knowledge graph completion (KGC)

摘要： 知识图谱是用来描述世界中存在的各种实体和概念以及他们之间的关系的一种语义网络，近年来被广泛应用于智能问答、智能推荐和信息检索等领域。目前，大多数知识图谱都具有不完整性，因此，知识图谱补全成为一项重要的任务。根据模型构造方法的不同，将知识图谱补全模型分为传统知识图谱补全模型、基于神经网络的知识图谱补全模型和基于元学习的知识图谱补全模型三类，对这三种知识图谱补全模型的分类情况进行介绍；总结知识图谱补全方法所使用的数据集和评价指标，并从各个模型优点和不足等方面对各类模型进行详细的对比分析。最后，对知识图谱补全进行归纳与总结，并展望未来的研究方向。

关键词: 知识图谱, 翻译模型, 张量分解, 神经网络, 元学习, 知识图谱补全