Hybrid Construction of Knowledge Graph and Deep Learning Experiments for Notre-Dame De Paris’ Data - Archive ouverte HAL
Chapitre D'ouvrage Année : 2024

Hybrid Construction of Knowledge Graph and Deep Learning Experiments for Notre-Dame De Paris’ Data

Résumé

After the fire that destroyed part of the cathedral Notre-Dame de Paris, a working group specialized in digital data coordinated a scientific project that would allow the management of all the digital data produced by scientific research activities along with restoration operations. The ERC advanced grant “nDame Heritage” project combines digital humanities with computer science and artificial intelligence to create a collaborative knowledge system that analyzes multiple views from different experts on the same cultural heritage objects. In this work, we designed a hybrid artificial intelligence workflow based on both knowledge graphs and deep learning models for semantic segmentation of 2D images. We show that this hybrid approach can help experts to process, integrate, and enrich Notre-Dame’s data.
Fichier non déposé

Dates et versions

hal-04692089 , version 1 (09-09-2024)

Identifiants

Citer

Kévin Réby, Anaïs Guillem, Livio De Luca. Hybrid Construction of Knowledge Graph and Deep Learning Experiments for Notre-Dame De Paris’ Data. Andrea Giordano; Michele Russo; Roberta Spallone. Advances in Representation: New AI- and XR-Driven Transdisciplinarity, Springer, pp.467-482, 2024, Digital Innovations in Architecture, Engineering and Construction, 978-3-031-62962-4 (hardcover) ; 978-3-031-62963-1 (ebook) ; 978-3-031-62965-5 (softcover). ⟨10.1007/978-3-031-62963-1_28⟩. ⟨hal-04692089⟩
84 Consultations
0 Téléchargements

Altmetric

Partager

More