Turning dross into gold. Knowledge graphs, with their capacity for surfacing vast hidden networks, can help detect looted art from the ownership history – or provenance – of artworks. The cultural heritage sector and art industry have explored named entity recognition with an event-based approach using CIDOC-CRM. However, Nazi-looted art poses a particular challenge, in part due to the passage of time, and in part due to unreliable data, as attempts to conceal and distort information which began in the Nazi era continue into the digital age. Missing, confusing or badly coded entities, false dates, names, events, places, the mixing of speculation and fact occur with such frequency in Nazi-looted art that it is useful to view errors, not as anomalies to be cleansed from the dataset, but as primary features to be analyzed. This presentation focuses on strategies and methods to quantify, classify, code and exploit this unreliable information in order to detect looted art and the patterns and networks which underly its commercialisation.

This presentation addresses issues of data quality that were identified in 2022 in "Tracking Looted Art with Graphs: a Wikidata case study" (video: https://youtu.be/_U2TDZCGBs8 )