Introducing VISU: Vagueness, Incompleteness, Subjectivity, and Uncertainty in Art Provenance Data

Research output: Contributions to collected editions/worksArticle in conference proceedingsResearchpeer-review

Authors

The acronym VISU refers to Vagueness, Incompleteness, Subjectivity, and Uncertainty found in provenance records, which document the history of ownership and socio-economic custody changes of an object. VISU information represents the intellectual effort of researchers and its limits in reconstructing historical events from archival sources. Although provenance has mainly been used in the past to assess an object’s artistic and economic value, it has recently become crucial information from an ethical and legal viewpoint. In light of this, there is a growing interest in structuring provenance information in a machine-readable format and making this data openly accessible to anyone, e.g., by publishing provenance data as linked open data. However, with the impetus to publish provenance linked open data, we risk losing or simplifying VISU information. After describing VISU information and analysing current community standards, this article illustrates how to represent such information in publishing provenance linked open data.

Original languageEnglish
Title of host publicationComputational Methods in the Humanities 2022 : Proceedings of the Workshop on Computational Methods in the Humanities 2022 Lausanne, Switzerland, June 9–10, 2022.
EditorsYannick Rochat, Coline Metrailler, Michael Piotrowski
Number of pages22
Volume3602
Place of PublicationAachen
PublisherSun Site Central Europe (RWTH Aachen University)
Publication date2023
Pages63-84
Publication statusPublished - 2023
Event2nd Workshop on Computational Methods in the Humanities - COMHUM 2022 - Universität Lausanne (UNIL), Lausanne, Switzerland
Duration: 09.06.202210.06.2022
Conference number: 2
https://wp.unil.ch/llist/en/event/comhum2022/

Bibliographical note

Funding Information:
The author would like to thank the three anonymous reviewers for their constructive feedback. I extend my gratitude to Marilena Daquino for valuable input and to Max Koss, Lynn Rother, and Liza Weber for their efforts in editing the article.

Publisher Copyright:
© 2023 CEUR-WS. All rights reserved.

    Research areas

  • CIDOC CRM, Linked Art, Linked Open Data, Nanopublication, Provenance
  • Science of art