  With the increase of dirty data, data cleaning turns into a crux of data
analysis. Most of the existing algorithms rely on either qualitative techniques
(e.g., data rules) or quantitative ones (e.g., statistical methods). In this
paper, we present a novel hybrid data cleaning framework on top of Markov logic
networks (MLNs), termed as MLNClean, which is capable of cleaning both
schema-level and instance-level errors. MLNClean mainly consists of two
cleaning stages, namely, first cleaning multiple data versions separately (each
of which corresponds to one data rule), and then deriving the final clean data
based on multiple data versions. Moreover, we propose a series of
techniques/concepts, e.g., the MLN index, the concepts of reliability score and
fusion score, to facilitate the cleaning process. Extensive experimental
results on both real and synthetic datasets demonstrate the superiority of
MLNClean to the state-of-the-art approach in terms of both accuracy and
efficiency.
