# xEM: Explainable Entity Matching in Customer 360

arXiv: https://arxiv.org/abs/2212.00342 · HTML: https://mlsanity.com/paper/2212.00342/

## 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.

## Builds on (most cited first, 13 of 13)

- [GNNExplainer: Generating Explanations for Graph Neural Networks](https://mlsanity.com/paper/1903.03894/) (2019)
- [Parameterized Explainer for Graph Neural Network](https://mlsanity.com/paper/2011.04573/) (2020)
- [XGNN: Towards Model-Level Explanations of Graph Neural Networks](https://mlsanity.com/paper/2006.02587/) (2020)
- [PGM-Explainer: Probabilistic Graphical Model Explanations for Graph Neural Networks](https://mlsanity.com/paper/2010.05788/) (2020)
- [Position-aware Graph Neural Networks](https://mlsanity.com/paper/1906.04817/) (2019)
- [Interpreting Graph Neural Networks for NLP With Differentiable Edge Masking](https://mlsanity.com/paper/2010.00577/) (2020)
- [Similar Cases Recommendation using Legal Knowledge Graphs](https://mlsanity.com/paper/2107.04771/) (2021)
- [Link Prediction using Graph Neural Networks for Master Data Management](https://mlsanity.com/paper/2003.04732/) (2020)
- [Explainable Link Prediction for Privacy-Preserving Contact Tracing](https://mlsanity.com/paper/2012.05516/) (2020)
- [Data Augmentation for Personal Knowledge Base Population](https://mlsanity.com/paper/2002.10943/) (2020)
- [Towards Automated Evaluation of Explanations in Graph Neural Networks](https://mlsanity.com/paper/2106.11864/) (2021)
- [LEMON: Explainable Entity Matching](https://mlsanity.com/paper/2110.00516/) (2021)
- [Reimagining GNN Explanations with ideas from Tabular Data](https://mlsanity.com/paper/2106.12665/) (2021)

## 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.