Reasoning application of knowledge graph fusion-enhanced graph neural networks in English reading comprehension

Key Points

• The aim is to enhance reading comprehension by applying knowledge graph fusion to graph neural network architectures.
• Implemented graph neural networks with knowledge graph fusion techniques.
• Evaluated their effectiveness in improving English reading comprehension.
• Involved various reasoning scenarios to assess comprehension outcomes.
• Graph neural networks improved reading comprehension scores by an average of 15% (95% CI: 9-21, p<0.01).
• Students using the knowledge graph fusion reported higher engagement levels compared to traditional methods.