A Model-Agnostic Approach for Semantic Table Augmentation in Digital Twin Government Using LLMs and Domain-Specific Ontologies

The Digital Twin Government (DTG) paradigm seeks to digitally replicate vast and heterogeneous governmental resources for decision-making and service delivery in real-world scenarios. In this paper, we introduce a Semantic Table Augmentation (STA) framework that automates the semantic enrichment of diverse tabular data using Large Language Models (LLMs). First, we propose the Digital Civil Complaint Ontology (DCCO) that expresses entities and their relationships in civil complaint management under DTG contexts. Our context-driven prompt templates enable the deployment of LLMs in a model-agnostic manner. Finally, we evaluate the performance of our proposed methods on synthetic datasets using cutting-edge LLMs against state-of-the-art method.