A Knowledge Representation Framework for Managing Leonardo Da Vinci's Mona Lisa: Case Study of the Hidden Painting
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12 septembre 2022
- handle: 10670/1.gwxs9a
- hal: hal-03959052
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Gian Piero Zarri et al., « A Knowledge Representation Framework for Managing Leonardo Da Vinci's Mona Lisa: Case Study of the Hidden Painting », HAL-SHS : histoire de l'art, ID : 10670/1.gwxs9a
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Résumé
This paper explores the use of Artificial Intelligence/Knowledge Representation methods for digitally modeling the cultural heritage items. It fully complies with the concept of “Cultural Heritage Digital Twin”, which is characterized by a “physical” component of the cultural entity, concerning style, dimension, name of the artist, execution time, etc., and by an “immaterial” component representing, among other things, the emotional and intangible messages transmitted by the entity. The “Narrative Knowledge Representation Language” (NKRL) is then been adopted for digitally representing the two components of the twin and its immaterial component in particular, due to its ability to represent in a simple but rigorous and efficient way complex situations and events, behaviors, attitudes, etc. An experiment concerning the “hidden painting” that lies beneath the Mona Lisa (“La Gioconda”) image on the same poplar panel has been then realized, showing that NKRL is able, in fact, to successfully provide a suitable representation of at least some of the intangible elements of the “visual narrative” represented by this still largely undeciphered portrait.