Study on complement of knowledge map of educational resources based on semi-supervised learning

In order to improve the effectiveness of completing educational resource knowledge graphs, a complement method of knowledge map of educational resources based on semi-supervised learning is studied. The relationship path features of the education resource knowledge graph are extracted using a path sorting algorithm. Within the interactive connection graph attention network of the semi-supervised deep learning algorithm, the embedding vectors of the knowledge graph are inputted to obtain the encoded representation of contextual features for the embedding vector entities, and the encoded feature matrix is constructed. The semantic matching model tensor decomposition is used to encode the feature matrix and calculate the scores for each triple. The triple with the highest score is selected as the completion result of the knowledge graph. The experimental results show that this method achieves high values in average reciprocal rank, Hits@10, Hits@3, and Hits@1, indicating a good completion effect of the knowledge graph.