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.

翻译：将人工智能模型融入各类优化系统的趋势日益显著。然而，解决复杂的城市与环境管理问题通常需要深厚的领域科学与信息学专业知识。这类专业知识对于从数据与仿真中推导出支持科学决策的结论至关重要。在此背景下，我们探索了利用预训练大型语言模型的潜力。通过采用ChatGPT API作为推理核心，我们构建了一个集成自然语言处理、基于方法本体的提示调优与Transformer技术的完整工作流。该工作流能够基于现有研究文献及城市数据集与仿真的技术手册，自动创建面向特定场景的本体。本方法生成的成果是采用广泛使用的本体语言（如OWL、RDF、SPARQL）构建的知识图谱。这些图谱通过增强数据与元数据建模、整合复杂数据集、耦合多领域仿真模型以及构建决策指标与工作流，有力促进了城市决策支持系统的开发。我们通过对比分析评估了本方法的可行性，将AI生成的本体与主流本体软件教程中广泛使用的Pizza Ontology进行了对比验证。最后，我们通过一个真实案例研究——通过整合多领域数据与仿真知识库来优化复杂的多式联运货运系统以支持科学决策——展示了本方法的实际应用价值。