Business Entity Matching with Siamese Graph Convolutional Networks

TL;DR

This paper introduces a novel deep learning approach using Siamese Graph Convolutional Networks for business entity matching, effectively leveraging relational and contextual data to improve integration accuracy and scalability.

Contribution

It presents a new method combining Siamese and graph neural networks for entity matching, outperforming traditional and existing deep learning techniques.

Findings

Outperforms rule-based systems in accuracy

Demonstrates scalability on large datasets

Leverages relational and contextual information effectively

Abstract

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.