Frances++: LLM-Based Semantic Enrichment and Spatial Graphs for Digitized Historical Collections

This paper presents major enhancements to frances, a platform for exploring digitized historical collections using LLM-based semantic and spatial enrichments. We focus on three corpora from the National Library of Scotland (NLS): the Gazetteers of Scotland, the Broadsides, and the Encyclopaedia Britannica. Using a flexible, prompt-based LLM pipeline, we extract over 50,000 structured articles from the Gazetteers, adapting to diverse typographic layouts. For Broadsides, we align OCR-derived text (extracted using defoe) with manually corrected transcriptions published by NLS, linking both sources for improved access and reuse. Historical text is further improved using a fine-tuned Llama2 model selected after evaluating several LLMs for post-OCR correction. To enrich geographic information, we combine Stanza NER, in-text coordinate parsing, and the Edinburgh Geoparser, enabling both modern georesolution and preservation of historical geography. We extend the Heritage Textual Ontology (HTO) to support article-level records, spatiotemporal entities, and in-text annotations via CRMgeo and Web Annotation standards. Resulting RDF knowledge graphs are deployed on a GeoSPARQL-enabled Fuseki server and indexed in Elasticsearch to support full-text, semantic, and spatial search. The upgraded frances interface offers entity highlighting, historical maps, and provenance tracking, demonstrating a scalable pipeline for structured, enriched access to OCRed heritage texts.