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Mining an "Anti-Knowledge Base" from Wikipedia Updates with Applications to Fact Checking and Beyond

Summary: Unsupervised mining builds an anti-knowledge base of factual mistakes from Wikipedia updates, focusing on long-tail errors for fact-checking benchmarks. A multi-step pipeline—heuristics, cross-web corroboration, EM inference, and SVO extraction—produces 110k+ ranked mistakes with 85% precision in the top 1%, enabling web-wide error discovery and analysis. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
12256
Venue
VLDB
Year
2020
Pagerank
5.5207515e-05
Overall Rank
5,412 | 62.36%
DOI
10.14778/3372716.3372727

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