Knowledge graph based analysis and exploration of historical theatre photographs

Historical theatre collections are an important form of cultural heritage and need to be preserved and made accessible to users. Often however, the metadata available for a historical collection are too sparse to create meaningful exploration tools. On the use case of a historical theatre photograph collection, this position paper discusses means of automated recognition of historical images to enhance the variety and depth of the metadata associated to the collection. Moreover, it describes how the results obtained by image recognition can be integrated into an existing Knowledge Graph (KG) and how these generated structured image metadata can support data exploration and automated querying to support human users. The goal of the paper is to explore cultural heritage data curation techniques based on deep learning and KGs to make the data findable, accessible, interoperable and reusable in accordance with the F.A.I.R principles.