# Business Entity Matching with Siamese Graph Convolutional Networks

arXiv: https://arxiv.org/abs/2105.03701 · HTML: https://mlsanity.com/paper/2105.03701/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 19 references that are in the map.
- First posted on arXiv 8 May 2021.

## Builds on (most cited first, 11 of 11)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [Hierarchical Graph Representation Learning with Differentiable Pooling](https://mlsanity.com/paper/1806.08804/) (2018)
- [Hyperbolic Graph Convolutional Neural Networks](https://mlsanity.com/paper/1910.12933/) (2019)
- [ANN-Benchmarks: A Benchmarking Tool for Approximate Nearest Neighbor Algorithms](https://mlsanity.com/paper/1807.05614/) (2018)
- [LinkNBed: Multi-Graph Representation Learning with Entity Linkage](https://mlsanity.com/paper/1807.08447/) (2018)
- [How to Train Triplet Networks with 100K Identities?](https://mlsanity.com/paper/1709.02940/) (2017)
- [Siamese Graph Neural Networks for Data Integration](https://mlsanity.com/paper/2001.06543/) (2020)
- [Matching Entities Across Different Knowledge Graphs with Graph Embeddings](https://mlsanity.com/paper/1903.06607/) (2019)
- [Fast Record Linkage for Company Entities](https://mlsanity.com/paper/1907.08667/) (2019)

## Built on by (most cited first, 4 of 4)

- [Siamese Attribute-missing Graph Auto-encoder](https://mlsanity.com/paper/2112.04842/) (2021)
- [SIMBA UQ: Similarity-Based Aggregation for Uncertainty Quantification in Large Language Models](https://mlsanity.com/paper/2510.13836/) (2025)
- [Short-Form Video Recommendations with Multimodal Embeddings: Addressing Cold-Start and Bias Challenges](https://mlsanity.com/paper/2507.19346/) (2025)
- [VCG: A Multimodal Retrieval Framework for E-Commerce Video Feeds under Extreme Cold-Start Conditions](https://mlsanity.com/paper/2606.19627/) (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 developments in machine learning and in particular deep learning have opened the way to more general and efficient solutions to data-integration tasks. In this paper, we demonstrate an approach that allows modeling and integrating entities by leveraging their relations and contextual information. This is achieved by combining siamese and graph neural networks to effectively propagate information between connected entities and support high scalability. We evaluated our approach on the task of integrating data about business entities, demonstrating that it outperforms both traditional rule-based systems and other deep learning approaches.