xEM: Explainable Entity Matching in Customer 360

• URL: http://arxiv.org/abs/2212.00342v1
• Date: Thu, 1 Dec 2022 08:01:01 GMT
• Title: xEM: Explainable Entity Matching in Customer 360
• Authors: Sukriti Jaitly, Deepa Mariam George, Balaji Ganesan, Muhammad Ameen, Srinivas Pusapati
• Abstract summary: We present our Explainable Entity Matching (xEM) system. In this demo, we discuss the different AI/ML considerations that went into its implementation.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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