Understanding Wikidata Qualifiers: An Analysis and Taxonomy

TL;DR

This paper analyzes Wikidata qualifiers to develop a comprehensive taxonomy that aids in their selection, querying, and logical inference, based on usage frequency and diversity.

Contribution

It introduces a new taxonomy of Wikidata qualifiers derived from empirical analysis, improving understanding and utilization of qualifiers in knowledge graph applications.

Findings

The taxonomy covers the most important qualifiers effectively.

Qualifiers are categorized into contextual, epistemic, structural, and additional types.

The study highlights the importance of qualifier diversity and frequency in taxonomy development.

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.