Cultural heritage materials, once digitized, remain largely unstructured: human-readable, but not systematically queryable, linkable, or analyzable. Large language models have improved access to such content, yet they cannot guarantee epistemic traceability. Viewsari addresses this gap by treating knowledge extraction from historical text as an interpretive activity, recording agents, prompts, software versions, and source contexts for each extracted statement.

The approach is neuro-symbolic: the Viewsari ontology (symbolic component) provides a formal schema that structures and constrains extraction tasks assigned to a large language model (neural component). Vasari's Lives (1568) serves as testbed — a founding document of art history characterized by implicit entity references, attributions presented as facts, and long-tail entities absent from standard knowledge bases.

The three questions this dissertation set out to answer. Click each one to see the short answer and where to explore it on this site.