  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.
