TY - GEN
T1 - Katara
T2 - ACM SIGMOD International Conference on Management of Data, SIGMOD 2015
AU - Chu, Xu
AU - Morcos, John
AU - Ilyas, Ihab F.
AU - Ouzzani, Mourad
AU - Papotti, Paolo
AU - Tang, Nan
AU - Ye, Yin
N1 - Publisher Copyright:
Copyright © 2015 ACM.
PY - 2015/5/27
Y1 - 2015/5/27
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84957586399&partnerID=8YFLogxK
U2 - 10.1145/2723372.2749431
DO - 10.1145/2723372.2749431
M3 - Conference contribution
AN - SCOPUS:84957586399
T3 - Proceedings of the ACM SIGMOD International Conference on Management of Data
SP - 1247
EP - 1261
BT - SIGMOD 2015 - Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data
PB - Association for Computing Machinery
Y2 - 31 May 2015 through 4 June 2015
ER -