The DOI matches its Crossref record; 43 of the 43 references it cites were found in a registry or an index.
KATARA: A Data Cleaning System Powered by Knowledge Bases and Crowdsourcing
Abstract
Classical approaches to clean data have relied on using integrity constraints, statistics, or machine learning. These approaches are known to be limited in the cleaning accuracy, which can usually be improved by consulting master data and involving experts to resolve ambiguity. The advent of knowledge bases KBs both general-purpose and within enterprises, and crowdsourcing marketplaces are providing yet more opportunities to achieve higher accuracy at a larger scale. We propose KATARA, a knowledge base and crowd powered data cleaning system that, given a table, a KB, and a crowd, interprets table semantics to align it with the KB, identifies correct and incorrect data, and generates top-k possible repairs for incorrect data. Experiments show that KATARA can be applied to various datasets and KBs, and can efficiently annotate data and suggest possible repairs.
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Cites
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Cited by
Data Cleaning: Overview and Emerging Challenges
Chu, X., Ilyas, I. F., Krishnan, S., & Wang, J. (2016) · Proceedings of the 2016 International Conference on Management of Data · doi:10.1145/2882903.2912574 · Cited by 488
Self-citationHoloClean: holistic data repairs with probabilistic inference
Rekatsinas, T., Chu, X., Ilyas, I. F., & Ré, C. (2017) · Proceedings of the VLDB Endowment · doi:10.14778/3137628.3137631 · Cited by 480
Self-citationBenchmark and Survey of Automated Machine Learning Frameworks
Zöller, M.-A., & Huber, M. F. (2021) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.11854 · Cited by 448
Systematic Review of Advanced AI Methods for Improving Healthcare Data Quality in Post COVID-19 Era
Isgut, M., Gloster, L., Choi, K., Venugopalan, J., & Wang, M. D. (2023) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2022.3216531 · Cited by 35
On Studying the Effect of Data Quality on Classification Performances
Jouseau, R., Salva, S., & Samir, C. (2022) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-21753-1_9 · Cited by 5
Metadata
- Authors
- Chu, X., Morcos, J., Ilyas, I. F., Ouzzani, M., Papotti, P., Tang, N., & Ye, Y.
- Venue
- Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data
- Publisher
- ACM
- Published
- 2015-05-27
- Pages
- 1247-1261
- ISSN
- 0730-8078 (ISSN-L)
- OpenAlex
- W1992479406
- Cited by
- 279
- References
- 43
- FWCI
- 58.68
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Chu, X., Morcos, J., Ilyas, I. F., Ouzzani, M., Papotti, P., Tang, N., & Ye, Y. (2015). KATARA: A Data Cleaning System Powered by Knowledge Bases and Crowdsourcing. Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data. https://doi.org/10.1145/2723372.2749431
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