dc.contributor.author | Barlaug, Nils | |
dc.contributor.author | Gulla, Jon Atle | |
dc.date.accessioned | 2021-05-18T11:55:46Z | |
dc.date.available | 2021-05-18T11:55:46Z | |
dc.date.created | 2021-05-05T19:40:42Z | |
dc.date.issued | 2021 | |
dc.identifier.issn | 1556-4681 | |
dc.identifier.uri | https://hdl.handle.net/11250/2755473 | |
dc.description.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. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | Association for Computing Machinery (ACM) | en_US |
dc.title | Neural Networks for Entity Matching: A Survey | en_US |
dc.type | Peer reviewed | en_US |
dc.type | Journal article | en_US |
dc.description.version | acceptedVersion | en_US |
dc.source.journal | ACM Transactions on Knowledge Discovery from Data | en_US |
dc.identifier.doi | https://doi.org/10.1145/3442200 | |
dc.identifier.cristin | 1908304 | |
dc.description.localcode | © ACM, 2021. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published here http://dx.doi.org/https://doi.org/10.1145/3442200 | en_US |
cristin.ispublished | true | |
cristin.fulltext | postprint | |
cristin.qualitycode | 1 | |