An Overview of Research on Knowledge Graph Completion Based on Graph Neural Network

[Objective] This paper summarizes the knowledge graph completion methods based on graph neural network through research and literature review. [Coverage] Withknowledge graph completionas search terms to retrieve literature from the Web of Science, DBLP and CNKI, a total of 79 representative literature were screened out for review. [Methods] Based on the model structure, three knowledge graph completion methods based on graph neural networks were summarized, including graph convolutional neural networks, graph attention networks, and graph auto encoder. [Results] Using common data sets and evaluation indicators for knowledge graph completion tasks, the effects of various models were comparatively analyzed in terms of MRR, MR, Hit@k and other performance evaluations, and prospects for future research were suggested. [Limitations] In the comparison of experimental results, only the evaluation results of some widely used models on the FB15K-237 and WN18RR datasets are discussed, the comparison of all models on the same dataset is lacking. [Conclusions] Compared with the representation learning model and the neural network model, the graph neural network model has better performance, but it still faces difficulties such as high complexity of model relationships, over-smoothness, and poor scalability and universality. © 2024 Chinese Academy of Sciences. All rights reserved.