论文标题
XEM：客户360中的可解释实体匹配
xEM: Explainable Entity Matching in Customer 360
论文作者
论文摘要
客户360中的实体匹配是确定多个记录是否代表同一现实世界实体的任务。实体通常是图表中表示为属性节点的人，组织，位置和事件，尽管它们也可以作为关系数据中的记录表示。尽管存在此任务的概率匹配引擎和人工神经网络模型，但解释实体匹配受到了较少的关注。在此演示中，我们介绍了可解释的实体匹配（XEM）系统，并讨论其实施中的不同AI/ML注意事项。
Entity matching in Customer 360 is the task of determining if multiple records represent the same real world entity. Entities are typically people, organizations, locations, and events represented as attributed nodes in a graph, though they can also be represented as records in relational data. While probabilistic matching engines and artificial neural network models exist for this task, explaining entity matching has received less attention. In this demo, we present our Explainable Entity Matching (xEM) system and discuss the different AI/ML considerations that went into its implementation.
