A Knowledge Graph of Medieval and Renaissance Geographical Works

Submitted by Valentina Bartalesi on 04/29/2026 - 01:46

Tracking #: 4077-5291

Authors: 
Valentina Bartalesi
Nicolò Pratelli
Emanuele Lenzi

Responsible editor: 
Guest Editors 2025 OD+CH

Submission type: 
Full Paper
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.
Full PDF Version: 
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Previous Version: 
A Knowledge Graph of Medieval and Renaissance Geographical Works
Tags: 
Reviewed
Long-term Stable Link to Resources: 
https://github.com/AIMH-DHgroup/IMAGO/tree/main

Decision/Status: 
Accept

Solicited Reviews:
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Review #1
By Alessandro Mosca submitted on 07/May/2026
Suggestion:
Accept
Review Comment:

The authors have now implemented all the suggestions made in the second round of peer review, and it is evident that they have paid particular attention to the comments regarding the experimental validation of the methodological approach presented. We would like to express our appreciation for the additional notes on the methodological approach and the adopted technological pipeline that the authors have added to the final sections of their manuscript. While we understand that a detailed experimental evaluation (and systematic comparison with other similar solutions) cannot be performed in a few weeks, we would like to assure you that we acknowledge the effort that has gone into this. We therefore consider that the paper, in its current form, has reached a sufficient level of scientific maturity and robustness to be accepted for publication in the Special Issue.