Mining an "Anti-Knowledge Base" from Wikipedia Updates with Applications to Fact Checking and Beyond
Summary: Introduces unsupervised anti-knowledge mining: detecting Wikipedia corrections, estimating correction likelihood via iterative EM over Web-claim frequencies, and extracting ranked erroneous SPO triples. Produces 110K long-tail mistakes at high precision, enabling fact-checking benchmarks and Web-wide error discovery. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Georgios Karagiannis (Cornell University)
- 2. Immanuel Trummer (Cornell University)
- 3. Saehan Jo (Cornell University)
- 4. Shubham Khandelwal (Cornell University)
- 5. Xuezhi Wang (Google)
- 6. Cong Yu (Google)
BibTeX Citation
@article{karagiannis_vldb20,
title = {{Mining an "Anti-Knowledge Base" from Wikipedia Updates with Applications to Fact Checking and Beyond}},
author = {Karagiannis, Georgios and Trummer, Immanuel and Jo, Saehan and Khandelwal, Shubham and Wang, Xuezhi and Yu, Cong},
journal = {PVLDB},
series = {{VLDB} '20},
volume = {13},
number = {4},
pages = {561--574},
doi = {10.14778/3372716.3372727},
url = {https://doi.org/10.14778/3372716.3372727},
year = {2020}
}
Incoming Citations (Sorted by Pagerank)
Showing 5 of 5 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,717 | Demonstrating CEDAR: A System for Cost-Efficient Data-Driven Claim Verification | 2025 | SIGMOD | 5.093636e-05 |
| 10,983 | CEDAR: A System for Cost-Efficient Data-Driven Claim Verification | 2025 | VLDB | 5.093636e-05 |
| 11,663 | To Intervene or Not To Intervene: Cost based Intervention for Combating Fake News | 2021 | SIGMOD | 5.093636e-05 |
| 11,718 | Wikinegata: a Knowledge Base with Interesting Negative Statements | 2021 | VLDB | 5.093636e-05 |
| 11,731 | On the Limits of Machine Knowledge: Completeness, Recall and Negation in Web-scale Knowledge Bases | 2021 | VLDB | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 9 of 9 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 112 | HoloClean: Holistic Data Repairs with Probabilistic Inference | 2017 | VLDB | 0.00032801121 |
| 205 | Snorkel: Rapid Training Data Creation with Weak Supervision | 2018 | VLDB | 0.00025235185 |
| 319 | Declarative Information Extraction Using Datalog with Embedded Extraction Predicates | 2007 | VLDB | 0.00021377065 |
| 2,307 | Resolving Conflicts in Heterogeneous Data by Truth Discovery and Source Reliability Estimation | 2014 | SIGMOD | 8.7750499e-05 |
| 2,428 | ClaimBuster: The First-ever End-to-end Fact-checking System | 2017 | VLDB | 8.5897309e-05 |
| 3,231 | Truth Inference in Crowdsourcing: Is the Problem Solved? | 2017 | VLDB | 7.6191767e-05 |
| 5,077 | Verifying Text Summaries of Relational Data Sets | 2019 | SIGMOD | 6.3730257e-05 |
| 6,787 | Domain-Aware Multi-Truth Discovery from Conflicting Sources | 2018 | VLDB | 5.7738798e-05 |
| 7,136 | Mining Subjective Properties on the Web | 2015 | SIGMOD | 5.6939829e-05 |
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Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 11,718 | Wikinegata: a Knowledge Base with Interesting Negative Statements | 2021 | VLDB |
| 2 | 7,125 | Computational Fact Checking: A Content Management Perspective | 2018 | VLDB |
| 3 | 9,204 | Selecting Data to Clean for Fact Checking: Minimizing Uncertainty vs. Maximizing Surprise | 2019 | VLDB |
| 4 | 9,295 | Combating Fake News: A Data Management and Mining Perspective | 2019 | VLDB |
| 5 | 3,147 | Auto-Detect: Data-Driven Error Detection in Tables | 2018 | SIGMOD |
| 6 | 11,980 | Building Structured Databases of Factual Knowledge from Massive Text Corpora | 2017 | SIGMOD |
| 7 | 9,316 | Automatically Generating Interesting Facts from Wikipedia Tables | 2019 | SIGMOD |
| 8 | 3,495 | Knowledge-Based Trust: Estimating the Trustworthiness of Web Sources | 2015 | VLDB |
| 9 | 3,127 | Toward Computational Fact-Checking | 2014 | VLDB |
| 10 | 7,628 | User Guidance for Efficient Fact Checking | 2019 | VLDB |