Towards Next-Generation Urban Decision Support Systems through AI-Powered Construction of Scientific Ontology Using Large Language Models—A Case in Optimizing Intermodal Freight Transportation

• Published in: Smart cities (Basel) 2024-10, Vol.7 (5), p.2392-2421
• Main Authors: Tupayachi, Jose, Xu, Haowen, Omitaomu, Olufemi A., Camur, Mustafa Can, Sharmin, Aliza, Li, Xueping
• Format: Article
• Language: English
• Subjects: Adaptive systems; Artificial intelligence; Chatbots; Complex systems; Data analysis; Data integration; Decision analysis; Decision making; Decision support systems; Environmental management; Freight transportation; Graphs; Intermodal; intermodal freight transportation; KNOWLEDGE MANAGEMENT AND PRESERVATION; Knowledge representation; Large language models; Methodology; Multiple criterion; Natural language processing; Ontology; Optimization; Performance evaluation; Qualitative analysis; Reasoning; Reasoning programs; Smart cities; Software; Task complexity; urban decision support system; Workflow
• ISSN: 2624-6511; 2624-6511
• DOI: 10.3390/smartcities7050094

The incorporation of Artificial Intelligence (AI) models into various optimization systems is on the rise. However, addressing complex urban and environmental management challenges often demands deep expertise in domain science and informatics. This expertise is essential for deriving data and simulation-driven insights that support informed decision-making. In this context, we investigate the potential of leveraging the pre-trained Large Language Models (LLMs) to create knowledge representations for supporting operations research. By adopting ChatGPT-4 API as the reasoning core, we outline an applied workflow that encompasses natural language processing, Methontology-based prompt tuning, and Generative Pre-trained Transformer (GPT), to automate the construction of scenario-based ontologies using existing research articles and technical manuals of urban datasets and simulations. From these ontologies, knowledge graphs can be derived using widely adopted formats and protocols, guiding various tasks towards data-informed decision support. The performance of our methodology is evaluated through a comparative analysis that contrasts our AI-generated ontology with the widely recognized pizza ontology, commonly used in tutorials for popular ontology software. We conclude with a real-world case study on optimizing the complex system of multi-modal freight transportation. Our approach advances urban decision support systems by enhancing data and metadata modeling, improving data integration and simulation coupling, and guiding the development of decision support strategies and essential software components.