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Wednesday October 9, 2024 10:55am - 11:05am EDT
The provenance of an artwork documents its ownership and socio-economic custody changes. Provenance records usually contain information on auction houses, collectors, dealers, galleries, and other art market participants, which is often relevant to the history of several artworks in different museum collections (Rother, Koss, Mariani 2022). With the extensive production of increasingly detailed provenance texts in museums in recent decades, creating provenance linked open data (PLOD) thus offers new avenues for collaborative research within and across institutions. PLOD makes it possible to break down the data silos in which provenance data is stored in most museums. It elevates provenance from a difficult-to-access specialist resource to a widely available and contributable resource for historical research of different kinds.
The creation of PLOD in museums necessitates the transformation of large quantities of unstructured textual information to become fully machine-readable. By addressing natural language processing challenges, AI can assist with the laborious and resource-intensive work of structuring provenance data (Rother, Mariani, Koss 2024). However, the research and documentation complexities of provenance records require human intervention. This lightning talk showcases the application of artificial intelligence combined with a human-in-the-loop approach to create and unlock the potential of provenance linked open data.
Speakers
LR

Lynn Rother

Leuphana Universität Lüneburg
FM

Fabio Mariani

Leuphana Universität Lüneburg
avatar for Max Koss

Max Koss

Research Associate, Leuphana Universität Lüneburg
Wednesday October 9, 2024 10:55am - 11:05am EDT
Zoom

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