  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.
