Designing a Data Model for Extracting Historical Knowledge from Latin Inscriptions Using Large Language Models

Abstract

　This study presents an approach to structured extraction of historical knowledge from Latin inscriptions using LLMs. A data model covering person-/ communities, their careers, benefactions, and social relationships was designed based on epigraphic expertise. Structured JSON data was extracted from 7,597 inscriptions across 12 cities in Roman North Africa, validated through a two-stage verification process, and converted into 314,456 RDF triples using a custom ontology. This enables new possibilities including content-based search and analysis.