LLM-assisted Knowledge Graph Engineering: Experiments with ChatGPT

Knowledge Graphs (KG) provide us with a structured, flexible, transparent, cross-system, and collaborative way of organizing our knowledge and data across various domains in society and industrial as well as scientific disciplines. KGs surpass any other form of representation in terms of effectiveness. However, Knowledge Graph Engineering (KGE) requires in-depth experiences of graph structures, web technologies, existing models and vocabularies, rule sets, logic, as well as best practices. It also demands a significant amount of work. Considering the advancements in large language models (LLMs) and their interfaces and applications in recent years, we have conducted comprehensive experiments with ChatGPT to explore its potential in supporting KGE. In this paper, we present a selection of these experiments and their results to demonstrate how ChatGPT can assist us in the development and management of KGs.

Kompetenzerwerbsförderung durch E-Assessment: Individuelle Kompetenzerfassung am Beispiel des Fachs Mathematik

In this article, we present a concept of how micro- and e-assessments can be used for the mathematical domain to automatically determine acquired and missing individual skills and, based on these information, guide individuals to acquire missing or additional skills in a software-supported process. The models required for this concept are a digitally prepared and annotated e-assessment item pool, a digital modeling of the domain that includes topics, necessary competencies, as well as introductory and continuative material, as well as a digital individual model, which can reliably record competencies and integrates aspects about the loss of such.