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 (McMaster University)
BibTeX Citation
@inproceedings{zheng_sigmod21,
title = {{Contextual Data Cleaning with Ontology FDs}},
author = {Zheng, Zheng},
series = {{SIGMOD} '21},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3448016.3450583},
url = {https://dl.acm.org/doi/10.1145/3448016.3450583},
year = {2021}
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Outgoing Citations (Sorted by Pagerank)
Showing 10 of 10 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 190 | A Cost-Based Model and Effective Heuristic for Repairing Constraints by Value Modification | 2005 | SIGMOD | 0.00025812169 |
| 199 | CrowdER: Crowdsourcing Entity Resolution | 2012 | VLDB | 0.0002551523 |
| 527 | Improving Data Quality: Consistency and Accuracy | 2007 | VLDB | 0.00016859013 |
| 717 | Guided Data Repair | 2011 | VLDB | 0.00014531658 |
| 988 | Towards Certain Fixes with Editing Rules and Master Data | 2010 | VLDB | 0.00012643318 |
| 1,099 | KATARA: A Data Cleaning System Powered by Knowledge Bases and Crowdsourcing | 2015 | SIGMOD | 0.00012008783 |
| 1,347 | Baran: Effective Error Correction via a Unified Context Representation and Transfer Learning | 2020 | VLDB | 0.00010947547 |
| 2,056 | Combining Quantitative and Logical Data Cleaning | 2016 | VLDB | 9.10022e-05 |
| 3,432 | UGuide – User-Guided Discovery of FD-Detectable Errors | 2017 | SIGMOD | 7.288198e-05 |
| 9,420 | A Data Quality Metric (DQM): How to Estimate the Number of Undetected Errors in Data Sets | 2017 | VLDB | 5.1672232e-05 |
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