The recognition of past memory evidence and identity through a critical reconstruction is an essential development driver in the field of industrial heritage. The complex network of relationships established in a site between humans, factories, cities, landscapes, and daily life needs the support of digital tools and technologies to preserve material and immaterial knowledge and to manage intervention and valorization activities. In order to handle such a complex network, it is crucial to promote a deeper understanding and a better knowledge-based structure for general problems and specifically the technological production elements during the artefact design phase. Within the usual digital tools, there is a fragmentation of representations that does not allow a complete comprehension of the heritage knowledge description and semantic relations. In this context, the use of ontological models for data and knowledge definition, for inconsistency reductions and for enriching data possibility sources through external information certainly represent the main features to obtain a heterogeneous data model capable of fully expressing the objects’ values. Therefore, the proposed framework shows how to exploit digital technologies to make data available in an open and standards-compliant format to provide the correct interpretative background through correlations with other concepts, information and knowledge. The expected outcome is to improve both the computable knowledge representation and the reasoning automatization system by ontologies to obtain a deeper comprehension, new value recognition and better management to exploit archaeological and industrial assets.
Tecnologia e conoscenza digitali per il patrimonio archeologico e industriale / Cui, CASSIA DE LIAN; Simeone, Davide; Cursi, Stefano; Bortoletto, Simone; Fioravanti, Antonio; Curra', Edoardo. - (2023), pp. 1447-1463. (Intervento presentato al convegno Colloqui.AT.e 2023 tenutosi a Bari).
Tecnologia e conoscenza digitali per il patrimonio archeologico e industriale
Cui Cassia De Lian
;Simeone Davide;Cursi Stefano;Bortoletto Simone;Fioravanti Antonio;Curra Edoardo
2023
Abstract
The recognition of past memory evidence and identity through a critical reconstruction is an essential development driver in the field of industrial heritage. The complex network of relationships established in a site between humans, factories, cities, landscapes, and daily life needs the support of digital tools and technologies to preserve material and immaterial knowledge and to manage intervention and valorization activities. In order to handle such a complex network, it is crucial to promote a deeper understanding and a better knowledge-based structure for general problems and specifically the technological production elements during the artefact design phase. Within the usual digital tools, there is a fragmentation of representations that does not allow a complete comprehension of the heritage knowledge description and semantic relations. In this context, the use of ontological models for data and knowledge definition, for inconsistency reductions and for enriching data possibility sources through external information certainly represent the main features to obtain a heterogeneous data model capable of fully expressing the objects’ values. Therefore, the proposed framework shows how to exploit digital technologies to make data available in an open and standards-compliant format to provide the correct interpretative background through correlations with other concepts, information and knowledge. The expected outcome is to improve both the computable knowledge representation and the reasoning automatization system by ontologies to obtain a deeper comprehension, new value recognition and better management to exploit archaeological and industrial assets.File | Dimensione | Formato | |
---|---|---|---|
Cui_Tecnologia-e-conoscenza-digitali_2023.pdf
solo gestori archivio
Note: copertina, frontespizio, sommario, contributo e retro di copertina
Tipologia:
Versione editoriale (versione pubblicata con il layout dell'editore)
Licenza:
Tutti i diritti riservati (All rights reserved)
Dimensione
2.7 MB
Formato
Adobe PDF
|
2.7 MB | Adobe PDF | Contatta l'autore |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.