JERAJournal of Electronic Research and Application2208-35022208-3510Bio-Byword Scientific Publishing Pty. Ltd.10.26689/jera.v10i5.14938ArticleConstruction and Application of a Knowledge Graph-Based Educational Question Answering System for Probability Theory and Mathematical Statisticshttps://artdesignp.com/journal/JERA/10/5/10.26689/jera.v10i5.14938HuangYihan20261052026-06-29In recent years, with the rapid development of artificial intelligence and big data technologies, knowledge graphs have gained widespread attention and application. As a fundamental course in mathematics and statistics, Probability Theory and Mathematical Statistics contains complex and highly interconnected knowledge points, making traditional learning methods less effective for understanding its internal logic. Therefore, constructing a knowledge graph and developing a corresponding question-answering system for this subject is of great significance. This project uses the Probability Theory and Mathematical Statistics Tutorial (3rd Edition) as the data source to construct a knowledge graph based on Neo4j. Cypher language and APOC tools were used for data import and graph construction, while Neo4j Bloom was employed for visualization. In addition, a question-answering system was developed using natural language processing techniques and the Flask framework to provide intelligent query services. The system can help students better understand and learn probability theory and mathematical statistics while reducing dependence on traditional textbooks.

Liu Q, Li Y, Duan H, et al., 2016, A Survey of Knowledge Graph Construction Technologies. Journal of Computer Research and Development, 53(3): 582–600.

Zhang J, Zhang X, Wu C, et al., 2022, A Review of Knowledge Graph Construction Technologies. Computer Engineering, 48(3): 23–37.

Seaver E, 2020, Neo4j Graph Data Science Practical Guide. Tsinghua University Press, Beijing, China.

Wang H, Wang X, 2023, Research on the Construction of a Linguistic Terminology Knowledge Graph Based on Neo4j. China Terminology: 8–26.

Mao S, Cheng Y, Pu X, 2020, Probability Theory and Mathematical Statistics Tutorial, 3rd ed. Higher Education Press, Beijing, China.

Zhang L, 2024, Review of Traditional Chinese Medicine Constitution Intervention Knowledge Graph Construction Based on Neo4j. Computer Knowledge and Technology, 20(10): 125–128.

Wang Y, Yang W, Hong S, 2025, Research on Key Technologies for Emotional Disorder Knowledge Graph Construction. Intelligent Computer and Applications, 15(2): 39–45.

Li X, Ma Z, Tian Z, 2025, Construction and Application of Zhejiang Intangible Cultural Heritage Knowledge Graph. Journal of Zhejiang Normal University (Natural Sciences).

Yang G, Xu B, Luo K, 2025, A Survey of Knowledge Graph Technologies: Construction, Reasoning and Typical Applications. Journal of Guizhou University (Natural Sciences), 42(2): 1–10.