SystemER: A Human-in-the-loop System for Explainable Entity Resolution

Entity Resolution (ER) is the task of identifying different representations of the same real-world object. To achieve scalability and the desired level of quality, the typical ER pipeline includes multiple steps that may involve low-level coding and extensive human labor. We present SystemER, a tool for learning explainable ER models that reduces the human labor all throughout the stages of the ER pipeline. SystemER achieves explainability by learning rules that not only perform a given ER task but are human-comprehensible; this provides transparency into the learning process, and further enables verification and customization of the learned model by the domain experts. By leveraging a human in the loop and active learning, SystemER also ensures that a small number of labeled examples is sufficient to learn high-quality ER models. SystemER is a full-fledged tool that includes an easy to use interface, support for both flat files and semistructured data, and scale-out capabilities by distributing computation via Apache Spark. PVLDB Reference Format: Kun Qian, Lucian Popa, and Prithviraj Sen. SystemER: A Humanin-the-loop System for Explainable Entity Resolution. PVLDB, 12(12): 1794-1797, 2019. DOI: https://doi.org/10.14778/3352063.3352068