Learning to Characterize Matching Experts
This work aims to improve the accuracy of data integration processes for organizations by identifying trustworthy human matching experts, which is an incremental improvement to existing semi-automatic matching workflows.
This paper addresses the challenge of identifying reliable human experts in data matching tasks, particularly in the context of big data. It introduces a novel framework and features to characterize matching experts, demonstrating that filtering out inexpert matchers can improve matching results.
Matching is a task at the heart of any data integration process, aimed at identifying correspondences among data elements. Matching problems were traditionally solved in a semi-automatic manner, with correspondences being generated by matching algorithms and outcomes subsequently validated by human experts. Human-in-the-loop data integration has been recently challenged by the introduction of big data and recent studies have analyzed obstacles to effective human matching and validation. In this work we characterize human matching experts, those humans whose proposed correspondences can mostly be trusted to be valid. We provide a novel framework for characterizing matching experts that, accompanied with a novel set of features, can be used to identify reliable and valuable human experts. We demonstrate the usefulness of our approach using an extensive empirical evaluation. In particular, we show that our approach can improve matching results by filtering out inexpert matchers.