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 language | English (US) |
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Title of host publication | SIGMOD 2015 - Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data |
Publisher | Association for Computing Machinery |
Pages | 1247-1261 |
Number of pages | 15 |
Volume | 2015-May |
ISBN (Electronic) | 9781450327589 |
DOIs | |
State | Published - May 27 2015 |
Externally published | Yes |
Event | ACM SIGMOD International Conference on Management of Data, SIGMOD 2015 - Melbourne, Australia Duration: May 31 2015 → Jun 4 2015 |
Other
Other | ACM SIGMOD International Conference on Management of Data, SIGMOD 2015 |
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Country/Territory | Australia |
City | Melbourne |
Period | 5/31/15 → 6/4/15 |
ASJC Scopus subject areas
- Software
- Information Systems