LLM-assisted Knowledge Graph Engineering: Experiments with ChatGPT
This addresses the problem of reducing the workload and expertise needed for KGE, but it is incremental as it applies an existing LLM to a specific domain.
The paper tackles the challenge of Knowledge Graph Engineering (KGE), which requires extensive expertise and effort, by exploring the potential of ChatGPT to assist in KGE tasks, showing that it can support development and management processes.
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.