无需标注数据，自动推断教育知识图谱中的先修关系
Inferring Prerequisite Knowledge Concepts in Educational Knowledge Graphs: A Multi-criteria Approach
- 基于十项指标融合投票，自动挖掘概念间的先修关系
- 在基准数据集上精度优于现有方法，且可扩展性强
- 适合需要构建自适应学习路径的在线课程平台
教育知识图谱（EduKG）通过组织学习实体及其关系，支持结构化与自适应学习。先修关系（PRs）对定义概念学习顺序至关重要。然而，当前CourseMapper平台中的EduKG缺乏显式的先修链接，人工标注耗时且不一致。为此，我们提出一种无监督方法，无需依赖标注数据即可自动推断概念先修关系。基于文档、维基超链接、图结构和文本特征，定义了十项评估标准，并采用投票算法融合结果，以稳健捕捉教育内容中的先修关系。在基准数据集上的实验表明，该方法在保持可扩展性和适应性的前提下，精度高于现有方法，为CourseMapper中的序列感知学习提供了可靠支持。
原文摘要 · Abstract (English)
Educational Knowledge Graphs (EduKGs) organize various learning entities and their relationships to support structured and adaptive learning. Prerequisite relationships (PRs) are critical in EduKGs for defining the logical order in which concepts should be learned. However, the current EduKG in the MOOC platform CourseMapper lacks explicit PR links, and manually annotating them is time-consuming and inconsistent. To address this, we propose an unsupervised method for automatically inferring concept PRs without relying on labeled data. We define ten criteria based on document-based, Wikipedia hyperlink-based, graph-based, and text-based features, and combine them using a voting algorithm to robustly capture PRs in educational content. Experiments on benchmark datasets show that our approach achieves higher precision than existing methods while maintaining scalability and adaptability, thus providing reliable support for sequence-aware learning in CourseMapper.
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