xEM: Explainable Entity Matching     



xEM: Explainable Entity Matching - overview

Entity matching is the task of determining if multiple records represent the same real world entity. Entities in Customer 360 applications 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.

Video: https://www.youtube.com/watch?v=wWcXHmCzwu0

Code: https://us-south.git.cloud.ibm.com/anu-kg/explainable-entity-matching

Blog post: https://www.linkedin.com/pulse/explainable-ai-match-360-balaji-ganesan/

Team: Sukriti Jaitly (CMU), Deepa Mariam George, Balaji Ganesan, Muhammad Ameen, Srinivas Pusapati.