Understanding Wikidata Qualifiers: An Analysis and Taxonomy

Abstract

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.

Figures (7)

• Figure 1: Frequency as a function of the frequency rank for the Wikidata qualifiers
• Figure 2: Frequency/diversity plot of the Wikidata qualifiers where the diversity is defined as the number of distinct properties qualified by a qualifier
• Figure 3: Frequency and $^{1}\!D$ proportional diversity of the Wikidata qualifiers
• Figure 4: Qualifier taxonomy
• Figure 5: Number of qualifiers in each category for the top 50 qualifiers