Advances in Knowledge Graph Embedding Based on Graph Neural Networks

As graph neural networks continue to develop, knowledge graph embedding methods based on graph neural networks are receiving increasing attention from researchers. Compared with traditional methods, they can better handle the diversity and complexity of entities, and capture the multiple features and complex relationships of entities, thereby improving the representation ability and application value of knowledge graphs. This paper firstly outlines the development history of knowledge graphs and the basic concepts of knowledge graphs and graph neural networks. Secondly, it focuses on discussing the design ideas and algorithm frameworks of knowledge graph embedding based on graph convolution, graph neural networks, graph attention, and graph autoencoders. Then, it describes the performance of graph neural network knowledge graph embedding in tasks such as link prediction, entity alignment, knowledge graph reasoning, and knowledge graph completion, while supplementing some research on commonsense knowledge graphs with graph neural networks. Finally, this paper makes a comprehensive summary, and future research directions are outlined with respect to some challenges and issues in knowledge graph embedding.