Understanding Wikidata Qualifiers: An Analysis and Taxonomy
This provides a structured approach for Wikidata contributors and knowledge graph designers to improve qualifier usage and systems, though it is incremental as it builds on existing Wikidata analysis.
This paper analyzed Wikidata qualifiers to develop a taxonomy addressing challenges in selection, querying, and inference, using frequency and diversity metrics to identify and categorize the top 300 qualifiers into contextual, epistemic/uncertainty, structural, and additional types.
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.