Diagnosing and Mitigating Semantic Inconsistencies in Wikidata's Classification Hierarchy

Shixiong Zhao, Hideaki Takeda
11/7/2025
cs.CL

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Abstract

Wikidata is currently the largest open knowledge graph on the web, encompassing over 120 million entities. It integrates data from various domain-specific databases and imports a substantial amount of content from Wikipedia, while also allowing users to freely edit its content. This openness has positioned Wikidata as a central resource in knowledge graph research and has enabled convenient knowledge access for users worldwide. However, its relatively loose editorial policy has also led to a degree of taxonomic inconsistency. Building on prior work, this study proposes and applies a novel validation method to confirm the presence of classification errors, over-generalized subclass links, and redundant connections in specific domains of Wikidata. We further introduce a new evaluation criterion for determining whether such issues warrant correction and develop a system that allows users to inspect the taxonomic relationships of arbitrary Wikidata entities-leveraging the platform's crowdsourced nature to its full potential.

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@article{zhao2025diagnosing,
  title  = {Diagnosing and Mitigating Semantic Inconsistencies in Wikidata's Classification Hierarchy},
  author = {Shixiong Zhao and Hideaki Takeda},
  year   = {2025},
  eprint = {2511.04926},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2511.04926v2}
}

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