LLM-assisted Knowledge Graph Engineering: Experiments with ChatGPT
Lars-Peter Meyer Claus Stadler Johannes Frey Norman Radtke Kurt Junghanns Roy Meissner Gordian Dziwis Kirill Bulert Michael Martin
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.