Construction and Application of a Knowledge Graph-Based Educational Question Answering System for Probability Theory and Mathematical Statistics

Abstract

In 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.