CONFERENCE PAPER: “Where Did It Come From? Deep Learning for Event Extraction in Art Provenance”, 10/06/2022 – 10/07/2022.

From October 67th, 2022, Lab member Fabio participated in the 10th annual “New Directions in Analyzing Text as Data” conference (TADA), which is a leading forum for research on the study of politics, society, and culture through computational analysis of documents. At this year’s conference, Fabio presented a talk titled “Where Did It Come From? Deep Learning for Event Extraction in Art Provenance”. 

In the talk, he explored the possibilities offered by artificial intelligence in the structuring of provenance events, where events are composed of parties, locations, dates, and methods of transfer. In particular, Fabio focused on the possibilities of applying deep learning methods, i.e. tools using artificial intelligence, for event extraction from provenance. From a computer science perspective, the extraction of knowledge from a provenance text relates to an event extraction problem in Natural Language Processing (NLP), an area of research where computer science, linguistics, and, more recently, artificial intelligence intersect. Using a museum dataset of over 11,000 provenances, Fabio showed how it is possible to solve NLP tasks by training artificial intelligence. 

CONFERENCE PAPER: “Modern Migrants – Provenance Studies at Leuphana”, 11/19/2021.

From November 17-19th, 2021, Fabio participated in the Second Culture Community Plenary of the NFDI4 Consortium for Research Data on Material and Immaterial Cultural Heritage. In his talk “Modern Migrants – Provenance Studies at Leuphana”, Fabio presented the project Modern Migrants: Paintings from Europe in US Museums at Leuphana University Lüneburg, which aims to digitally collect, structure and, ultimately, analyze data on modern paintings from Europe in US museum collections, with the view to generating knowledge about art markets, the circulation of artworks, and other networked aspects of art history. Using the provenance of Pablo Picasso’s ‘Harlequin’ (1915) as an example, Fabio’s presentation focused on the challenges of extracting knowledge from provenance texts, as well as the possibilities and limitations of natural language processing.

NEWS STORY: “Complete Clarification – Lynn Rother Teaches the New Subject of Provenance Studies in Lüneburg”, 03/01/2020.

Upon Lynn‘s appointment to the Lichtenberg-Professorship at Lüneburg, the German art magazine Art ran a two-page interview with her in their March issue of 2020. Under the title “Complete Clarification”, Lynn argued:

“In the last two decades in particular, an extraordinarily large amount of provenance data has been generated on individual objects, but it has not yet been analysed across museums and collections. When it comes to thousands of works with tens of thousands of changes in ownership, this may be an almost impossible task for a human being, but not for a computer. This is where methods from data science come in.”

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