Wikidata entity richness metric reveals biases in art and writer profiles
SQuIREL: A Context-Relative Framework for Measuring Entity Richness in Wikidata
Artificial IntelligenceDigital Libraries
Summary
Measuring how detailed or 'rich' entries are in Wikidata can be tricky because each item is unique. The authors created SQuIREL, a way to measure richness by looking at different features of each entry, like how specific its meaning is and how connected it is to other data. They tested it on groups of paintings and writers, finding differences that suggest some groups are less well represented. This shows how their method can spot hidden inequalities in big knowledge databases.
What this means in practice
- •For knowledge base editors: Identify which Wikidata entries need more detail to reduce bias and improve data quality.
- •For cultural heritage data teams: Assess structural differences in art and writer records to support more balanced digital collections.
Authors
Michelle Nwachukwu, Albert Meroño-Peñuela, Martim Brandão
Abstract
Wikidata is a large, heterogeneous knowledge graph and applying data quality metrics requires context-specific approaches. The "richness" quality metric is currently under-researched and typically restricted to the ontology level, which fails to capture entity-specific structural depth. To address this gap, we introduce SQuIREL, a method that measures richness directly at the entity level using six dimensions: Semantic Specificity, Qualifier Density, Internal Connectivity, Reference Provenance, External Network Connectivity, and Linguistic Accessibility. These dimensions are kept disaggregated to preserve actionability for targeted editorial intervention. We apply this metric to two case studies of oil paintings (Brazilian vs. French) and writers (grouped by citizenship and gender) to demonstrate how it surfaces representational inequalities that may indicate systemic bias. Our metric successfully distinguishes between highly tailored and basic entity profiles, revealing nuances that raw counts and existing aggregate scores obscure. Specifically, it identifies structural imbalances, such as 48% of Brazilian paintings clustering as low-richness stubs against 0% in the French cohort, as well as intersectional patterns, including contrasting gender gaps between UK and Nigerian writers.