Contextual Data Cleaning with Ontology FDs
Summary: Introduces Ontology Functional Dependencies (OFDs) to encode semantic relations from an ontology, enabling context-sensitive data quality beyond syntactic FDs. Senses disambiguate interpretation (e.g., jaguar as animal vs vehicle) and handle evolving data/ontologies with conflicts among data, OFDs, and knowledge. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Zheng Zheng
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Outgoing Citations (Sorted by Pagerank)
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 263 | CrowdER: Crowdsourcing Entity Resolution | 2012 | VLDB | 0.00029862413 |
| 265 | A Cost-Based Model and Effective Heuristic for Repairing Constraints by Value Modification | 2005 | SIGMOD | 0.00029763412 |
| 623 | Improving Data Quality: Consistency and Accuracy | 2007 | VLDB | 0.00018996374 |
| 833 | Guided Data Repair | 2011 | VLDB | 0.00016138432 |
| 1,159 | Towards Certain Fixes with Editing Rules and Master Data | 2010 | VLDB | 0.00013592813 |
| 1,546 | KATARA: A Data Cleaning System Powered by Knowledge Bases and Crowdsourcing | 2015 | SIGMOD | 0.00011446851 |
| 1,894 | Baran: Effective Error Correction via a Unified Context Representation and Transfer Learning | 2020 | VLDB | 0.0001018378 |
| 2,460 | Combining Quantitative and Logical Data Cleaning | 2016 | VLDB | 8.7617484e-05 |
| 3,976 | UGuide – User-Guided Discovery of FD-Detectable Errors | 2017 | SIGMOD | 6.5736462e-05 |
| 9,056 | A Data Quality Metric (DQM): How to Estimate the Number of Undetected Errors in Data Sets | 2017 | VLDB | 4.4039656e-05 |
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