Cultural heritage collections available as linked open data (LOD) may contain harmful stereotypes about people and cultures, for example, in outdated textual descriptions of objects. Galleries, libraries, archives, and museums (GLAM) have suggested various approaches to tackle potentially problematic content in digital collections. However, the domain expertise and discussions about words and phrases used in LOD-collections are scattered across different resources and detached from the collections themselves. In this paper, we capture domain expertise about English and Dutch contentious heritage terminology in a knowledge graph. Contentious terms in the resulting graph are then linked to entities from other LOD-resources used in the cultural domain and beyond, including Wikidata and WordNet. We make our design decisions explicit and report on the linking process. The developed knowledge graph makes expert knowledge interoperable, so it can be reused by the cultural heritage community and other LOD-developers to contribute to a more inclusive representation of cultural heritage on the Web.
Recent initiatives by cultural heritage institutions in addressing outdated and offensive language used in their collections demonstrate the need for further understanding into when terms are problematic or contentious. This paper presents an annotated dataset of 2,715 unique samples of terms in context, drawn from a historical newspaper archive, collating 21,800 annotations of contentiousness from expert and crowd workers.We describe the contents of the corpus by analysing inter-rater agreement and differences between experts and crowd workers. In addition, we demonstrate the potential of the corpus for automated detection of contentiousness. We show that a simple classifier applied to the embedding representation of a target word provides a better than baseline performance in predicting contentiousness. We find that the term itself and the context play a role in whether a term is considered contentious. CCS CONCEPTS• Information systems → Digital libraries and archives; • Computing methodologies → Knowledge representation and reasoning.
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