Neural Networks for Entity Matching: A Survey

作者：Nils Barlaug, Jon Atle Gulla · 发表于：ACM Transactions on Knowledge Discovery from Data · 年份：2021 · DOI：10.1145/3442200 · 被引用次数：122 · 研究领域：Data Quality and Management、Topic Modeling、Natural Language Processing Techniques

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.