SQuIREL: A Context-Relative Framework for Measuring Entity Richness in Wikidata
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.
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Key points
- 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.
- 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.
Sources (1)
- [1]SQuIREL: A Context-Relative Framework for Measuring Entity Richness in WikidataarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 17, 02:16 PM
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.
Wikidata is a large, heterogeneous knowledge graph and applying data quality metrics requires context-specific approaches.
Extractive summary: sentences quoted from the sources.