Modelling learning objects (LO) within their context enables the learner to advance from a basic, remembering-level, learning objective to a higher-order one, i.e., a level with an application- and analysis objective. While hierarchical data models are commonly used in digital learning platforms, using graph-based models enables representing the context of LOs in those platforms. This leads to a foundation for personalized recommendations of learning paths. In this paper, the transformation of hierarchical data models into knowledge graph (KG) models of LOs using text mining is introduced and evaluated. We utilize custom text mining pipelines to mine semantic relations between elements of an expert-curated hierarchical model. We evaluate the KG structure and relation extraction using graph quality-control metrics and the comparison of algorithmic semantic-similarities to expert-defined ones. The results show that the relations in the KG are semantically comparable to those defined by domain experts, and that the proposed KG improves representing and linking the contexts of LOs through increasing graph communities and betweenness centrality.

翻译：在学习对象（LO）的上下文中对其进行建模，可使学习者从基础记忆型学习目标进阶至高阶目标（即具有应用与分析目标的学习层级）。虽然数字学习平台普遍采用层次化数据模型，但基于图的模型能够表征这些平台中学习对象的上下文关系，从而为个性化学习路径推荐奠定基础。本文提出并评估了利用文本挖掘将层次化数据模型转化为学习对象知识图谱（KG）的方法。我们采用自定义文本挖掘流程，从专家策源的层次化模型中挖掘元素间的语义关系，并通过图质量控制指标及算法语义相似度与专家定义的比对来评估知识图谱结构与关系抽取效果。结果表明，知识图谱中的关系在语义上与领域专家定义的关系具有可比性，且所提知识图谱通过增强图社区结构与中介中心性，显著提升了学习对象上下文的表征与关联能力。