Applying Large Language Models in Knowledge Graph-based Enterprise Modeling: Challenges and Opportunities
This addresses the problem of automating enterprise modeling for businesses and researchers, but it is incremental as it builds on existing knowledge graph and LLM methods.
The paper tackles the application of large language models (LLMs) in knowledge graph-based enterprise modeling, finding that LLM-based model generations show minimal variability but are limited to specific tasks, with reliability decreasing for more complex tasks, and human expert supervision is crucial for accuracy.
The role of large language models (LLMs) in enterprise modeling has recently started to shift from academic research to that of industrial applications. Thereby, LLMs represent a further building block for the machine-supported generation of enterprise models. In this paper we employ a knowledge graph-based approach for enterprise modeling and investigate the potential benefits of LLMs in this context. In addition, the findings of an expert survey and ChatGPT-4o-based experiments demonstrate that LLM-based model generations exhibit minimal variability, yet remain constrained to specific tasks, with reliability declining for more intricate tasks. The survey results further suggest that the supervision and intervention of human modeling experts are essential to ensure the accuracy and integrity of the generated models.