从PDF课件自动构建教育知识图谱，效率提升十倍，准确率提高17.5%。
An Optimized Pipeline for Automatic Educational Knowledge Graph Construction
- 分页生成知识图谱后合并，形成完整课程知识结构。
- 在MOOC平台测试中，准确率显著提升，处理速度提高十倍。
- 适合需要高效构建教育知识图谱的教研与平台开发人员。
自动构建教育知识图谱（EduKG）对从学习材料中提取有意义的知识表示至关重要。尽管关注度上升，但实现可扩展且可靠地自动生成EduKG仍具挑战。本文提出一种从PDF学习材料自动构建EduKG的统一流程：先逐页生成片级知识图谱，再融合为完整课程知识图谱。在自研MOOC平台CourseMapper上评估表明，原始方法准确率较低，尤其在对知识可靠性要求高的教育场景中表现不足。为此，我们在多个流程环节引入针对性优化，使最终系统准确率提升17.5%，处理效率提高十倍。该方案提供了一套端到端、可扩展、适应多种教育场景的自动化构建路径，支持更优的学习内容语义表达。
原文摘要 · Abstract (English)
The automatic construction of Educational Knowledge Graphs (EduKGs) is essential for domain knowledge modeling by extracting meaningful representations from learning materials. Despite growing interest, identifying a scalable and reliable approach for automatic EduKG generation remains a challenge. In an attempt to develop a unified and robust pipeline for automatic EduKG construction, in this study we propose a pipeline for automatic EduKG construction from PDF learning materials. The process begins with generating slide-level EduKGs from individual pages/slides, which are then merged to form a comprehensive EduKG representing the entire learning material. We evaluate the accuracy of the EduKG generated from the proposed pipeline in our MOOC platform, CourseMapper. The observed accuracy, while indicative of partial success, is relatively low particularly in the educational context, where the reliability of knowledge representations is critical for supporting meaningful learning. To address this, we introduce targeted optimizations across multiple pipeline components. The optimized pipeline achieves a 17.5% improvement in accuracy and a tenfold increase in processing efficiency. Our approach offers a holistic, scalable and end-to-end pipeline for automatic EduKG construction, adaptable to diverse educational contexts, and supports improved semantic representation of learning content.
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