Graph Neural Networks for Entity Matching.

Data integration still remains largely rule-driven and lacks universal automation. 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. We evaluate our method on the task of integrating data about business entities and demonstrate that it outperforms standard rule-based systems, as well as other deep-learning approaches that do not leverage any inductive bias towards the use of graph-based representations.