# Siamese Graph Neural Networks for Data Integration

arXiv: https://arxiv.org/abs/2001.06543 · HTML: https://mlsanity.com/paper/2001.06543/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jan 2020.

## Built on by (most cited first, 5 of 5)

- [Graph Neural Networks for Tabular Data Learning: A Survey with Taxonomy and Directions](https://mlsanity.com/paper/2401.02143/) (2024)
- [Business Entity Matching with Siamese Graph Convolutional Networks](https://mlsanity.com/paper/2105.03701/) (2021)
- [Mobile Network Configuration Recommendation using Deep Generative Graph Neural Network](https://mlsanity.com/paper/2406.04779/) (2024)
- [Named Entity Normalization Model Using Edge Weight Updating Neural Network: Assimilation Between Knowledge-Driven Graph and Data-Driven Graph](https://mlsanity.com/paper/2106.07549/) (2021)
- [U-CECE: A Universal Multi-Resolution Framework for Conceptual Counterfactual Explanations](https://mlsanity.com/paper/2604.08295/) (2026)

## Abstract (arXiv)

Data integration has been studied extensively for decades and approached from different angles. However, this domain still remains largely rule-driven and lacks universal automation. Recent development in machine learning and in particular deep learning has opened the way to more general and more efficient solutions to data integration problems. In this work, we propose a general approach to modeling and integrating entities from structured data, such as relational databases, as well as unstructured sources, such as free text from news articles. Our approach is designed to explicitly model and leverage relations between entities, thereby using all available information and preserving as much context as possible. This is achieved by combining siamese and graph neural networks to propagate information between connected entities and support high scalability. We evaluate our method on the task of integrating data about business entities, and we demonstrate that it outperforms standard rule-based systems, as well as other deep learning approaches that do not use graph-based representations.