How Contentious Terms About People and Cultures are Used in Linked Open Data

TL;DR

This study analyzes the occurrence and marking of contentious terms about people and cultures in linked open data, revealing frequent use of outdated stereotypes and inconsistent marking practices across datasets.

Contribution

It provides a systematic analysis of contentious term usage in LOD and highlights the rarity and inconsistency of explicit markers for such terms.

Findings

Contentious terms frequently appear in descriptive literals.

Marking of contentious terms is rare and inconsistent.

Outdated stereotypes are often propagated through LOD literals.

Abstract

Web resources in linked open data (LOD) are comprehensible to humans through literal textual values attached to them, such as labels, notes, or comments. Word choices in literals may not always be neutral. When outdated and culturally stereotyping terminology is used in literals, they may appear as offensive to users in interfaces and propagate stereotypes to algorithms trained on them. We study how frequently and in which literals contentious terms about people and cultures occur in LOD and whether there are attempts to mark the usage of such terms. For our analysis, we reuse English and Dutch terms from a knowledge graph that provides opinions of experts from the cultural heritage domain about terms' contentiousness. We inspect occurrences of these terms in four widely used datasets: Wikidata, The Getty Art & Architecture Thesaurus, Princeton WordNet, and Open Dutch WordNet. Some terms are ambiguous and contentious only in particular senses. Applying word sense disambiguation, we generate a set of literals relevant to our analysis. We found that outdated, derogatory, stereotyping terms frequently appear in descriptive and labelling literals, such as preferred labels that are usually displayed in interfaces and used for indexing. In some cases, LOD contributors mark contentious terms with words and phrases in literals (implicit markers) or properties linked to resources (explicit markers). However, such marking is rare and non-consistent in all datasets. Our quantitative and qualitative insights could be helpful in developing more systematic approaches to address the propagation of stereotypes via LOD.