Neural Networks for Entity Matching: A Survey
It addresses the problem of entity matching for researchers and practitioners by summarizing recent deep learning approaches, but it is incremental as it is a survey rather than new research.
This survey examines how neural networks have been applied to entity matching, identifying the steps in the process targeted by existing work and providing an overview of techniques used at each step.
Entity matching is the problem of identifying which records refer to the same real-world entity. It has been actively researched for decades, and a variety of different approaches have been developed. Even today, it remains a challenging problem, and there is still generous room for improvement. In recent years we have seen new methods based upon deep learning techniques for natural language processing emerge. In this survey, we present how neural networks have been used for entity matching. Specifically, we identify which steps of the entity matching process existing work have targeted using neural networks, and provide an overview of the different techniques used at each step. We also discuss contributions from deep learning in entity matching compared to traditional methods, and propose a taxonomy of deep neural networks for entity matching.