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

• URL: http://arxiv.org/abs/2405.19255v1
• Date: Wed, 29 May 2024 16:40:31 GMT
• Title: Towards Next-Generation Urban Decision Support Systems through AI-Powered Generation of Scientific Ontology using Large Language Models -- A Case in Optimizing Intermodal Freight Transportation
• Authors: Jose Tupayachi, Haowen Xu, Olufemi A. Omitaomu, Mustafa Can Camur, Aliza Sharmin, Xueping Li,
• Abstract summary: This study investigates 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. The outcomes of our methodology are knowledge graphs in widely adopted ontology languages (e.g., OWL, RDF, SPARQL)
• Score: 1.6230958216521798
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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