A Symmetry Analysis Method for Teaching Knowledge Graph Evolution Driven by Directed Attributed Graphs

Entity symmetry in teaching knowledge graphs is a characteristic of knowledge semantic expression and association, which plays a crucial role in the composition of knowledge structure. However, the evolution of the teaching knowledge graph may disrupt the symmetry of the knowledge structure, leading to the emergence of asymmetric phenomena and resulting in adverse effects on the subsequent search and representation of knowledge. Therefore, this article proposes a symmetry analysis method for the evolution of teaching knowledge graphs driven by directed attributed graphs. Firstly, a teaching knowledge graph model with directed attributed graphs is presented, on which the entity connection symmetry, entity center symmetry, and entity mirror symmetry of the teaching knowledge graph are defined. Then, the addition, replacement, and deletion of entity evolution rules that affect symmetry in the teaching knowledge graph model were characterized, and a teaching knowledge graph evolution algorithm based on directed attributed graph transformation was designed. On this basis, an in-depth analysis was conducted on the symmetry of the evolution of the teaching knowledge graph, which was disrupted and maintained. Finally, experiments verify that preserving or breaking symmetry has a significant impact on the connectivity and path complexity of knowledge graphs. In addition, a case study on the evolution of a Japanese major teaching knowledge graph with both symmetric and asymmetric transformations is provided to validate the feasibility and effectiveness of the proposed directed attributed graph driven symmetry analysis method for educational knowledge graph evolution.