Survey on Few-Shot Knowledge Graph Completion Technology

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.

Keywords

• knowledge graph; knowledge graph completion; few-shot learning; few-shot knowledge graph comp-letion (fkgc)