A Hybrid Data Cleaning Framework Using Markov Logic Networks

Abstract

With the increase of dirty data, data cleaning turns into a crux of data analysis. The accuracy limitation of the existing integrity constraints-based cleaning approaches results from insufficient rules. In this paper, we present a novel hybrid data cleaning framework on top of Markov logic networks (MLNs), termed as${\sf MLNClean}$, which is capable of learning instantiated rules to supplement the insufficient integrity constraints.${\sf MLNClean}$consists of two steps, i.e.,pre-processingandtwo-stage data cleaning. In the pre-processing step,${\sf MLNClean}$first infers a set of probable instantiated rules according to MLNs and then builds a two-layer MLN index structure to generate multiple data versions and facilitate the cleaning process. In the two-stage data cleaning step,${\sf MLNClean}$first presents a concept ofreliability scoreto clean errors within each data version separately, and afterward eliminates the conflict values among different data version using a novel concept offusion score. Considerable experimental results on both real and synthetic scenarios demonstrate the effectiveness of${\sf MLNClean}$in practice.

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