  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.
