Search papers, labs, and topics across Lattice.
This paper analyzes Wikidata qualifiers to develop a taxonomy that improves their selection, querying, and logical inference. The study evaluates qualifier importance using frequency, diversity, and a modified Shannon entropy index to address the "long tail" phenomenon. The resulting taxonomy categorizes the top 300 qualifiers into contextual, epistemic/uncertainty, structural, and additional types, demonstrating its effectiveness in structuring and understanding qualifiers.
A new taxonomy of Wikidata qualifiers clarifies their semantics and usage, offering a structured approach to knowledge graph design and querying.
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.
