xEM: Explainable Entity Matching in Customer 360
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 13 of its 20 references that are in the map. First posted on arXiv 1 Dec 2022.
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References · 13
- 2019GNNExplainer: Generating Explanations for Graph Neural Networkscited by 737
- 2020Parameterized Explainer for Graph Neural Networkcited by 426
- 2020XGNN: Towards Model-Level Explanations of Graph Neural Networkscited by 211
- 2020PGM-Explainer: Probabilistic Graphical Model Explanations for Graph Neural Networkscited by 211
- 2019Position-aware Graph Neural Networkscited by 203
- 2020Interpreting Graph Neural Networks for NLP With Differentiable Edge Maskingcited by 153
- 2021Similar Cases Recommendation using Legal Knowledge Graphscited by 9
- 2020Link Prediction using Graph Neural Networks for Master Data Managementcited by 8
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- 2020Data Augmentation for Personal Knowledge Base Populationcited by 3
- 2021Towards Automated Evaluation of Explanations in Graph Neural Networkscited by 2
- 2021LEMON: Explainable Entity Matchingcited by 2
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