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.
Abstract (translated)
本文对维基数据的限定符进行了深入分析，重点关注其语义和实际应用，并旨在开发一种分类体系来应对选择适当限定符、查询图谱以及进行逻辑推理方面的挑战。研究基于频率和多样性的考量评估了限定符的重要性，并采用了一种经过修改的香农熵指标以适应“长尾”现象的影响。通过对维基数据的备份文件进行分析，选出了排名前300位的限定符，并将其分类为上下文、知识/不确定性、结构以及其他类型的限定符。该分类体系旨在指导贡献者创建和查询陈述语句，改善限定符推荐系统，并提升知识图谱的设计方法论。研究结果表明，这种分类体系能够有效地涵盖最重要的限定符，并提供一种有条理的方法来理解和使用维基数据中的限定符。
URL
https://arxiv.org/abs/2603.11767
