Understanding Wikidata Qualifiers: An Analysis and Taxonomy
This paper presents an in-depth analysis of Wikidata qualifiers, focusing on their semantics and actual usage, with the aim of developing a taxonomy that addresses the challenges of selecting appropriate qualifiers, querying the graph, and making logical inferences. The study evaluates qualifier importance based on frequency and diversity, using a modified Shannon entropy index to account for the "long tail" phenomenon. By analyzing a Wikidata dump, the top 300 qualifiers were selected and categorized into a refined taxonomy that includes contextual, epistemic/uncertainty, structural, and additional qualifiers. The taxonomy aims to guide contributors in creating and querying statements, i...
| Authors | Gilles Falquet, Sahar Aljalbout |
|---|---|
| Primary category | cs.AI |
| Published | 12 March 2026 |
| Last updated at source | 12 March 2026 |
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