This article presents the WRITE data model and dataset, a comprehensive collection of Chinese contemporary calligraphic data, utilizing Linked Open Data (LOD) principles. Calligraphy plays a pivotal role in Chinese culture, reflecting national identity and cultural transformations. The objective of this study is to enhance understanding and provide new tools for exploring Chinese contemporary calligraphy through LOD. The WRITE data model comprises artistic, linguistic, and socio-political-economic aspects. The WRITE data model, developed collaboratively with domain specialists, represents four collections: Contemporary Visual Art, Performance, Graffiti, and Decorative and Applied Arts. Metadata describing the artworks is structured by reusing and extending the Wikidata model. Complex relations are established between artworks and contextual elements, (e.g. people, exhibition history, organizations, and literary works). The artistic and linguistic metadata recorded over the ‘calli-writing units’ provide insights into shared and diverging characteristics with traditional calligraphy. Traditional and contemporary calligraphy practices are compared, highlighting how contemporary calligraphy challenges traditional rules. Two case studies demonstrate the formalization of specific items in the WRITE collection, showcasing the study of graffiti art’s socio-political meaning in China and the multidimensional nature of musicalligraphy performance. The WRITE dataset and data model contribute to advancing knowledge and understanding of Chinese contemporary calligraphy, offering valuable resources for artistic analysis and interdisciplinary research.

Modelling Chinese contemporary calligraphy: the WRITE data model / Pasqual, Valentina; Lučić, Katarina; Bisceglia, Marta Rosa; Merenda, Martina; Iezzi, Adriana; Tomasi, Francesca. - In: DIGITAL SCHOLARSHIP IN THE HUMANITIES. - ISSN 2055-7671. - ELETTRONICO. - 00:(2024), pp. 1-9. [10.1093/llc/fqae006]

Modelling Chinese contemporary calligraphy: the WRITE data model

Pasqual, Valentina;Bisceglia, Marta Rosa;Merenda, Martina;Iezzi, Adriana;Tomasi, Francesca
2024

Abstract

This article presents the WRITE data model and dataset, a comprehensive collection of Chinese contemporary calligraphic data, utilizing Linked Open Data (LOD) principles. Calligraphy plays a pivotal role in Chinese culture, reflecting national identity and cultural transformations. The objective of this study is to enhance understanding and provide new tools for exploring Chinese contemporary calligraphy through LOD. The WRITE data model comprises artistic, linguistic, and socio-political-economic aspects. The WRITE data model, developed collaboratively with domain specialists, represents four collections: Contemporary Visual Art, Performance, Graffiti, and Decorative and Applied Arts. Metadata describing the artworks is structured by reusing and extending the Wikidata model. Complex relations are established between artworks and contextual elements, (e.g. people, exhibition history, organizations, and literary works). The artistic and linguistic metadata recorded over the ‘calli-writing units’ provide insights into shared and diverging characteristics with traditional calligraphy. Traditional and contemporary calligraphy practices are compared, highlighting how contemporary calligraphy challenges traditional rules. Two case studies demonstrate the formalization of specific items in the WRITE collection, showcasing the study of graffiti art’s socio-political meaning in China and the multidimensional nature of musicalligraphy performance. The WRITE dataset and data model contribute to advancing knowledge and understanding of Chinese contemporary calligraphy, offering valuable resources for artistic analysis and interdisciplinary research.
2024
Modelling Chinese contemporary calligraphy: the WRITE data model / Pasqual, Valentina; Lučić, Katarina; Bisceglia, Marta Rosa; Merenda, Martina; Iezzi, Adriana; Tomasi, Francesca. - In: DIGITAL SCHOLARSHIP IN THE HUMANITIES. - ISSN 2055-7671. - ELETTRONICO. - 00:(2024), pp. 1-9. [10.1093/llc/fqae006]
Pasqual, Valentina; Lučić, Katarina; Bisceglia, Marta Rosa; Merenda, Martina; Iezzi, Adriana; Tomasi, Francesca
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/966933
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