Modelling Medieval and Renaissance Geographical Knowledge: The IMAGO Knowledge Graph and Semantic Pipeline

Geographical works from the Middle Ages and Renaissance offer crucial insights into the cultural and intellectual landscapes of their time. However, digital scholarship in this domain remains fragmented, with key historical sources scattered across various physical and digital repositories. The Index Medii Aevi Geographiae Operum (IMAGO), an Italian national research project conducted from 2020 to 2024, addresses this gap by building a semantically enriched, interoperable knowledge graph focused on Latin geographical literature from the 6th to the 15th centuries. By combining expertise in medieval studies, philology, and digital humanities, IMAGO employs Semantic Web technologies and a dedicated ontology extending CIDOC CRM and LRMoo. The project facilitates data integration and reuse by applying Linked Open Data (LOD) principles, thereby enhancing the discoverability and interoperability of cultural heritage data. Beyond the release of the IMAGO knowledge graph, this work contributes a methodological pipeline for semantic modelling, annotation, integration, and publication of data related to medieval and Renaissance geographical works using established Knowledge Representation and Semantic Web standards. The approach is evaluated through a set of scholarly queries. These queries showcase the IMAGO infrastructure’s potential for data retrieval and deeper scholarly analysis. Finally, a user-friendly web application further enables access to the knowledge graph via interactive maps, dynamic tables, and exportable formats.

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Publication Details

Journal
Semantic Web
Published
2026-10-08
DOI
https://doi.org/10.1177/22104968261479443
Primary Topic
Digital Humanities and Scholarship
Type
article
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0.00
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article

Modelling Medieval and Renaissance Geographical Knowledge: The IMAGO Knowledge Graph and Semantic Pipeline

Valentina Bartalesi, Emanuele Lenzi, Nicolò Pratelli
Semantic Web
Digital Humanities and Scholarship
article

Modelling Medieval and Renaissance Geographical Knowledge: The IMAGO Knowledge Graph and Semantic Pipeline

Valentina Bartalesi, Emanuele Lenzi, Nicolò Pratelli
article en

Abstract

Geographical works from the Middle Ages and Renaissance offer crucial insights into the cultural and intellectual landscapes of their time. However, digital scholarship in this domain remains fragmented, with key historical sources scattered across various physical and digital repositories. The Index Medii Aevi Geographiae Operum (IMAGO), an Italian national research project conducted from 2020 to 2024, addresses this gap by building a semantically enriched, interoperable knowledge graph focused on Latin geographical literature from the 6th to the 15th centuries. By combining expertise in medieval studies, philology, and digital humanities, IMAGO employs Semantic Web technologies and a dedicated ontology extending CIDOC CRM and LRMoo. The project facilitates data integration and reuse by applying Linked Open Data (LOD) principles, thereby enhancing the discoverability and interoperability of cultural heritage data. Beyond the release of the IMAGO knowledge graph, this work contributes a methodological pipeline for semantic modelling, annotation, integration, and publication of data related to medieval and Renaissance geographical works using established Knowledge Representation and Semantic Web standards. The approach is evaluated through a set of scholarly queries. These queries showcase the IMAGO infrastructure’s potential for data retrieval and deeper scholarly analysis. Finally, a user-friendly web application further enables access to the knowledge graph via interactive maps, dynamic tables, and exportable formats.

Semantic WebVol. 17(6)
University of Pisa (IT), Istituto di Scienza e Tecnologie dell'Informazione "Alessandro Faedo" (IT)
Openalex Percentile: Top 1%
Digital Humanities and Scholarship
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Modelling Medieval and Renaissance Geographical Knowledge: The IMAGO Knowledge Graph and Semantic Pipeline — Valentina Bartalesi, Emanuele Lenzi, et al. · Semantic Web (2026) | TGRS Research Map | TGRS