Neural Networks for Entity Matching: A Survey

Abstract

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.

arXiv commentPublished in ACM Transactions on Knowledge Discovery from Data (TKDD)

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Cited by in corpus (4)

• Construction of Knowledge Graphs: State and Challenges
• Deep Indexed Active Learning for Matching Heterogeneous Entity Representations
• Disambiguation of Company names via Deep Recurrent Networks
• Entity Matching by Pool-based Active Learning