Abstract: Few-shot knowledge graph completion (FKGC) is a new research hotspot in the field of knowledge graph completion, which aims to complete knowledge graph with a few samples of data. This task is of great importance in practical application and the fields of knowledge graph. In order to further promote the development of the field of FKGC, this paper summarizes and analyzes the current methods. Firstly, this paper describes the concept of FKGC and related content. Secondly, three types of FKGC methods are summarized based on technical methods, including scale learning-based methods, meta learning-based methods, and other model-based methods. In addition, this paper analyzes and summarizes each method from the perspectives of model core, model ideas, advantages and disadvantages, etc. Then, the datasets and evaluation indexes of FKGC method are summarized, and the FKGC method is analyzed from two aspects of model characteristics and experimental results. Finally, starting from the practical problems, this paper summarizes the difficult problems of the current FKGC task, analyses the difficulties behind the problems, gives the corresponding solutions, and prospects several development directions that deserve attention in this field in the future.

Key words: knowledge graph, knowledge graph completion, few-shot learning, few-shot knowledge graph comp-letion (FKGC)

摘要： 少样本知识图谱补全（FKGC）是目前知识图谱补全任务的一个研究热点，旨在拥有少量样本数据的情况下，完成知识图谱补全任务。该任务在实际应用和知识图谱领域都有着重要的研究意义,为了进一步促进FKGC领域的发展，对目前各类方法进行了全面总结和分析。首先，描述了FKGC的概念和相关内容；其次，以技术方法作为分类依据，归纳总结出三类FKGC方法，包括基于度量学习的方法、基于元学习的方法以及基于其他模型的方法，并从模型核心、模型思路、优缺点等角度对每种方法进行分析和总结；然后，汇总了FKGC方法的数据集和评价指标，并从模型特点和实验结果两方面对FKGC方法进行分析与归纳；最后，从实际问题出发，总结了目前FKGC任务的难点问题，分析了问题背后的困难，给出了相应的解决方法，同时展望了该领域未来值得关注的几个发展方向。

关键词: 知识图谱, 知识图谱补全, 少样本学习, 少样本知识图谱补全（FKGC）