Data Brief. 2026 Sep 3;69:113224. doi: 10.1016/j.dib.2026.113224. eCollection 2026 Aug.

ABSTRACT

Medieval and Renaissance Latin geographical works constitute a major source for understanding how space, places, and territories were described and conceptualised in pre-modern Europe. However, information about these works, their manuscript transmission, and the places they mention remains dispersed across catalogues, archives, and specialised scholarship. Here we present the IMAGO knowledge graph, a semantically structured dataset representing 343 Latin geographical works written between the 6th and the 15th centuries. The dataset integrates curated information provided by domain experts, including authors, works, manuscripts, printed editions, libraries, literary genres, and mentioned places. Data were initially collected in tabular form and subsequently enriched through semi-automatic reconciliation with external authority sources such as Wikidata and the MIRABILE digital archive. Domain experts further expanded the dataset using a dedicated annotation tool. The curated data were transformed into an OWL 2 DL knowledge graph aligned with the IMAGO ontology and published following FAIR and Linked Open Data principles. The knowledge graph was validated through automated reasoning, expert review, and query-based evaluation. The resulting dataset enables systematic exploration of textual, bibliographic, and spatial relationships within medieval and Renaissance geographical literature and supports reuse in historical, philological, and digital humanities research.

PMID:42757014 | PMC:PMC13583948 | DOI:10.1016/j.dib.2026.113224

PeerJ Comput Sci. 2024 Oct 22;10:e2242. doi: 10.7717/peerj-cs.2242. eCollection 2024.

ABSTRACT

Narratives play a crucial role in human communication, serving as a means to convey experiences, perspectives, and meanings across various domains. They are particularly significant in scientific communities, where narratives are often utilized to explain complex phenomena and share knowledge. This article explores the possibility of integrating large language models (LLMs) into a workflow that, exploiting the Semantic Web technologies, transforms raw textual data gathered by scientific communities into narratives. In particular, we focus on using LLMs to automatically create narrative events, maintaining the reliability of the generated texts. The study provides a conceptual definition of narrative events and evaluates the performance of different smaller LLMs compared to the requirements we identified. A key aspect of the experiment is the emphasis on maintaining the integrity of the original narratives in the LLM outputs, as experts often review texts produced by scientific communities to ensure their accuracy and reliability. We first perform an evaluation on a corpus of five narratives and then on a larger dataset comprising 124 narratives. LLaMA 2 is identified as the most suitable model for generating narrative events that closely align with the input texts, demonstrating its ability to generate high-quality narrative events. Prompt engineering techniques are then employed to enhance the performance of the selected model, leading to further improvements in the quality of the generated texts.

PMID:39650368 | PMC:PMC11623210 | DOI:10.7717/peerj-cs.2242