Katara: A data cleaning system powered by knowledge bases and crowdsourcing

Xu Chu, John Morcos, Ihab F. Ilyas, Mourad Ouzzani, Paolo Papotti, Nan Tang, Yin Ye

Research output: Chapter in Book/Report/Conference proceedingConference contribution

110 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationSIGMOD 2015 - Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data
PublisherAssociation for Computing Machinery
Pages1247-1261
Number of pages15
Volume2015-May
ISBN (Electronic)9781450327589
DOIs
StatePublished - May 27 2015
Externally publishedYes
EventACM SIGMOD International Conference on Management of Data, SIGMOD 2015 - Melbourne, Australia
Duration: May 31 2015Jun 4 2015

Other

OtherACM SIGMOD International Conference on Management of Data, SIGMOD 2015
CountryAustralia
CityMelbourne
Period5/31/156/4/15

ASJC Scopus subject areas

  • Software
  • Information Systems

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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. In SIGMOD 2015 - Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data (Vol. 2015-May, pp. 1247-1261). Association for Computing Machinery. https://doi.org/10.1145/2723372.2749431