A data ecosystem offers an alliance-driven infrastructure that enables the interaction of different stakeholders and the resolution of interoperability issues among shared data. Despite years of research in data governance and management, trustability is still affected by the absence of transparent and traceable data-driven pipelines. Data integration is the main facilitator of such data-driven pipelines and matching is a task at the heart of any data integration process, aimed at identifying correspondences among data elements. Matching problems were traditionally performed 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 talk, we focus on the tension between human and machine matching. We propose a novel data ecosystem architecture that relies on both human knowledge and machine learning and offer a concrete algorithmic solution for effective data integration within this architecture. In particular, we shall present the limitations of human matching and offer a method for learning to characterize reliable and valuable matching experts.
04/04/2023