Understanding Wikidata Qualifiers: An Analysis and Taxonomy
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866908881832116224 |
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| author | Falquet, Gilles Aljalbout, Sahar |
| author_facet | Falquet, Gilles Aljalbout, Sahar |
| contents | 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, improve qualifier recommendation systems, and enhance knowledge graph design methodologies. The results show that the taxonomy effectively covers the most important qualifiers and provides a structured approach to understanding and utilizing qualifiers in Wikidata. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_11767 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Understanding Wikidata Qualifiers: An Analysis and Taxonomy Falquet, Gilles Aljalbout, Sahar Artificial Intelligence 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, improve qualifier recommendation systems, and enhance knowledge graph design methodologies. The results show that the taxonomy effectively covers the most important qualifiers and provides a structured approach to understanding and utilizing qualifiers in Wikidata. |
| title | Understanding Wikidata Qualifiers: An Analysis and Taxonomy |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2603.11767 |