Data Cleaning is a long standing problem, which is growing in importance with the mass of
uncurated web data. State of the art approaches for handling inconsistent data are systems
that learn and use conditional functional dependencies (CFDs) to rectify data. These
methods learn data patterns--CFDs--from a clean sample of the data and use them to rectify
the dirty/inconsistent data. While getting a clean training sample is feasible in enterprise
data scenarios, it is infeasible in web databases where there is no separate curated data …

Data Cleaning is a long standing problem, which is growing in importance with the mass of
uncurated web data. State of the art approaches for handling inconsistent data are systems
that learn and use conditional functional dependencies (CFDs) to rectify data. These
methods learn data patterns–CFDs–from a clean sample of the data and use them to rectify
the dirty/inconsistent data. While getting a clean training sample is feasible in enterprise
data scenarios, it is infeasible in web databases where there is no separate curated data …