Capturing Knowledge Dynamics in the Evolving Knowledge Space Graph Using Temporal Relations and Knowledge Aging

Most knowledge representation models in tutoring systems treat knowledge as a static resource. In real world scenarios, knowledge evolves over time as circumstances change. Consequently, the information embedded in a tutoring model may become outdated or require revision, especially in rapidly changing domains such as software development. The Evolving Knowledge Space Graph model addresses this issue by introducing abstract time to represent knowledge dynamics. This paper analyzes the temporal dependencies within the Evolving Knowledge Space Graph model, introducing “before” and “after” relations, and extending the model to capture knowledge aging. Additionally, it proposes metrics for assessing knowledge differences and complexity. Finally, the feasibility of the approach is demonstrated through a case study that transforms the publicly available OpenJDK JMC system’s knowledge into an Evolving Knowledge Space Graph.