Generation of Personalized Knowledge Graphs Based on GCN

Education must follow the principle of teaching students in accordance with their aptitude. In this paper, we propose a novel method to generate personalized knowledge graphs based on graph convolutional network. We have summarized the methods of evaluating the difficulty of exercises, and apply them to the generation of knowledge graph. After that, the adjacency matrix corresponding to the knowledge graph and the eigenvectors corresponding to the nodes are used as inputs of the graph convolutional network, and the semi-supervised leaning node classification is adopted to continuously iterate the training to optimize the graph convolution neural network model. Meanwhile, the graph convolutional neural network is used to generate personalized knowledge graph for each student, more accurate personalized services can be provided. The experimental results show that our method can make a better to realize in-depth personalized services.