YIN, Mengtian (University of Cambridge), Huang, Tao (University of Cambridge), Wong, Sin Ming (University of Cambridge), Pauwels, Pieter (Eindhoven University of Technology), Economides, George (Department for Transport), Brilakis, Ioannis (University of Cambridge)

Chain-Of-Thought-Based Knowledge Extraction from Heterogeneous Infrastructure Database for Integrated Transportation Asset Management

Scheduled for presentation during the Invited Session "S18a-Innovative Applications of LLM in Multimodal Transportation Systems" (TH-LM-T18), Thursday, November 20, 2025, 12:10−12:30, Southport 3

2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC), November 18-21, 2025, Gold Coast, Australia

This information is tentative and subject to change. Compiled on October 18, 2025

Abstract

The fragmentation of infrastructure information systems has long been an obstacle to integrated transportation asset management (TAM). This paper presents a novel method for automatic knowledge extraction and ontology modelling from heterogeneous TAM databases using large language mod- els (LLMs). The method adopts a Chain-of-Thought frame- work to decompose the complex ontology modelling process into atomic tasks, harnessing the semantic understanding and reasoning capabilities of LLMs. As a result, class entities, class hierarchies, and relations are generated to construct an ontology model that supports semantic interoperability across diverse TAM systems. The method’s performance was evaluated using four sets of TAM database schemas from UK road agencies. The results show that the overall recall rate for entity generation reaches 89.5% compared to the standard ontology. Furthermore, the accuracy rates for entity classification and relation classification are 82.1% and 75.6%, respectively, demonstrating the effectiveness of the proposed LLM-based approach in addressing data fragmentation issues in transportation information systems.