xEM: Explainable Entity Matching in Customer 360
This addresses the need for explainability in entity matching for users in data integration and customer management, but it appears incremental as it builds on existing matching engines and neural networks without claiming major breakthroughs.
The paper tackles the problem of explaining entity matching in Customer 360, which involves determining if multiple records represent the same real-world entity, and presents an Explainable Entity Matching (xEM) system as a demonstration.
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.