Knowledge Graphs Generation from Cultural Heritage Texts: Combining LLMs and Ontological Engineering for Scholarly Debates
Unstructured discourse in cultural heritage (CH) texts poses significant challenges for transforming contested knowledge—particularly authenticity debates—into queryable, structured knowledge graphs (KGs). Method: This paper introduces ATR4CH, the first systematic methodology integrating large language models (LLMs)—including Claude Sonnet 3.7, Llama 3.3 70B, and GPT-4o-mini—with CH-specific ontologies via a five-stage pipeline: foundational analysis, annotation schema design, architecture implementation, integration optimization, and comprehensive evaluation. Contribution/Results: Evaluated on Wikipedia texts concerning contested cultural artifacts, ATR4CH achieves metadata F1-scores of 0.96–0.99 and evidence extraction F1-scores of 0.95–0.97. Notably, smaller LLMs deliver high performance with superior cost-efficiency. The framework enables cross-domain, multi-source KG construction while significantly enhancing both the retrievability and interpretability of CH knowledge.