Coreference Resolution Improves Educational Knowledge Graph Construction

An educational knowledge graph provides students and teachers with detailed knowledge organization and a clear concept structure by extracting knowledge points and relationships from textbooks. A high-fidelity knowledge graph is essential for precise teaching and personalized learning. However, as an important step in knowledge graph construction, coreference resolution is often ignored or left to the end. This neglect leads to a loss in the high fidelity of the knowledge graph and may also cause clearly underestimated focuses and fewer associations between knowledge points, although the ratio of the pronouns to the entire corpus is very small (less than 5‰). In this paper, a rule and semantic-based method is proposed to resolve coreference in the knowledge graph constructed from a biology textbook. Compared with the other three algorithms, it has a better precision ratio and recall ratio. By comparing the two knowledge graphs constructed before and after coreference resolution, it can be seen that the focus has changed significantly to better align with the text, and the associations between the knowledge points are more consistent with intuition. This outcome suggests that coreference resolution improves the high fidelity of an educational knowledge graph.