The Curator’s Machine

Clustering of Museum Collection Data through Annotation of Hidden Connection Patterns between Artworks

Abstract

The digitization in art museums promises extended access to the objects of the collection both for scientific purposes and for an interested public, and this preferably online—independent of location and at any time. Here it is not enough to simply limit the search in databases to narrowly defined keywords. Rather, specific interfaces and visualizations should allow the user to explore the digital inventory as well as to ‘stroll’ through the online collection. Artificial intelligence can support the systematic and structured processing of the mass of data in the museum. Machine learning can reveal connections and links between artworks, which previously became accessible to the curator only incompletely or with difficulty. The text presents a first prototype on the basis of which the research project “Training the Archive” intends to investigate the machine-aided, explorative (re)discovery of connections within the museum's collection.