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Artificial Intelligence and Ontologies for the Management of Heritage Digital Twins Data

Author

Listed:
  • Achille Felicetti

    (VAST LAB, PIN, Piazza dell’Università 1, 59100 Prato, Italy)

  • Franco Niccolucci

    (VAST LAB, PIN, Piazza dell’Università 1, 59100 Prato, Italy)

Abstract

This study builds upon the Reactive Heritage Digital Twin paradigm established in prior research, exploring the role of artificial intelligence in expanding and enhancing its capabilities. After providing an overview of the ontological model underlying the RHDT paradigm, this paper investigates the application of AI to improve data analysis and predictive capabilities of Heritage Digital Twins in synergy with the previously defined RHDTO semantic model. The structured nature of ontologies is highlighted as essential for enabling AIs to operate transparently, minimising hallucinations and other errors that are characteristic challenges of these technologies. New classes and properties within RHDTO are introduced to represent the AI-enhanced functions. Finally, some case studies are provided to illustrate how integrating AI within the RHDT framework can contribute to enriching the understanding of cultural information through interconnected data and facilitate real-time monitoring and preservation of cultural objects.

Suggested Citation

  • Achille Felicetti & Franco Niccolucci, 2024. "Artificial Intelligence and Ontologies for the Management of Heritage Digital Twins Data," Data, MDPI, vol. 10(1), pages 1-24, December.
    • Handle: RePEc:gam:jdataj:v:10:y:2024:i:1:p:1-:d:1553733
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    References listed on IDEAS

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    1. Franco Niccolucci & Achille Felicetti & Sorin Hermon, 2022. "Populating the Data Space for Cultural Heritage with Heritage Digital Twins," Data, MDPI, vol. 7(8), pages 1-28, July.
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