  The incorporation of Artificial Intelligence (AI) models into various
optimization systems is on the rise. Yet, addressing complex urban and
environmental management problems normally requires in-depth domain science and
informatics expertise. This expertise is essential for deriving data and
simulation-driven for informed decision support. In this context, we
investigate the potential of leveraging the pre-trained Large Language Models
(LLMs). By adopting ChatGPT API as the reasoning core, we outline an integrated
workflow that encompasses natural language processing, methontology-based
prompt tuning, and transformers. This workflow automates the creation of
scenario-based ontology using existing research articles and technical manuals
of urban datasets and simulations. The outcomes of our methodology are
knowledge graphs in widely adopted ontology languages (e.g., OWL, RDF, SPARQL).
These facilitate the development of urban decision support systems by enhancing
the data and metadata modeling, the integration of complex datasets, the
coupling of multi-domain simulation models, and the formulation of
decision-making metrics and workflow. The feasibility of our methodology is
evaluated through a comparative analysis that juxtaposes our AI-generated
ontology with the well-known Pizza Ontology employed in tutorials for popular
ontology software (e.g., prot\'eg\'e). We close with a real-world case study of
optimizing the complex urban system of multi-modal freight transportation by
generating anthologies of various domain data and simulations to support
informed decision-making.
