Entity matching with transformer architectures - a step forward in data integration

Brunner, Ursin; Stockinger, Kurt

doi:10.5441/002/edbt.2020.58

Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-19637

Full metadata record

DC Field	Value	Language
dc.contributor.author	Brunner, Ursin	-
dc.contributor.author	Stockinger, Kurt	-
dc.date.accessioned	2020-03-05T12:54:42Z	-
dc.date.available	2020-03-05T12:54:42Z	-
dc.date.issued	2020-03	-
dc.identifier.isbn	978-3-89318-083-7	de_CH
dc.identifier.uri	https://digitalcollection.zhaw.ch/handle/11475/19637	-
dc.description.abstract	Transformer architectures have proven to be very effective and provide state-of-the-art results in many natural language tasks. The attention-based architecture in combination with pre-training on large amounts of text lead to the recent breakthrough and a variety of slightly different implementations. In this paper we analyze how well four of the most recent attention-based transformer architectures (BERT, XLNet, RoBERTa and DistilBERT) perform on the task of entity matching - a crucial part of data integration. Entity matching (EM) is the task of finding data instances that refer to the same real-world entity. It is a challenging task if the data instances consist of long textual data or if the data instances are "dirty" due to misplaced values. To evaluate the capability of transformer architectures and transfer-learning on the task of EM, we empirically compare the four approaches on inherently difficult data sets. We show that transformer architectures outperform classical deep learning methods in EM by an average margin of 27.5%.	de_CH
dc.language.iso	en	de_CH
dc.publisher	OpenProceedings	de_CH
dc.rights	http://creativecommons.org/licenses/by-nc-nd/4.0/	de_CH
dc.subject	Entity matching	de_CH
dc.subject	Data integration	de_CH
dc.subject	Machine learning	de_CH
dc.subject	Neural networks	de_CH
dc.subject	Transformers	de_CH
dc.subject	BERT	de_CH
dc.subject.ddc	006: Spezielle Computerverfahren	de_CH
dc.title	Entity matching with transformer architectures - a step forward in data integration	de_CH
dc.type	Konferenz: Paper	de_CH
dcterms.type	Text	de_CH
zhaw.departement	School of Engineering	de_CH
zhaw.organisationalunit	Institut für Informatik (InIT)	de_CH
dc.identifier.doi	10.5441/002/edbt.2020.58	de_CH
dc.identifier.doi	10.21256/zhaw-19637	-
zhaw.conference.details	23rd International Conference on Extending Database Technology, Copenhagen, 30 March - 2 April 2020	de_CH
zhaw.funding.eu	No	de_CH
zhaw.originated.zhaw	Yes	de_CH
zhaw.pages.end	473	de_CH
zhaw.pages.start	463	de_CH
zhaw.publication.status	publishedVersion	de_CH
zhaw.publication.review	Peer review (Publikation)	de_CH
zhaw.title.proceedings	Proceedings of EDBT 2020	de_CH
zhaw.webfeed	Datalab	de_CH
zhaw.webfeed	Information Engineering	de_CH
zhaw.author.additional	No	de_CH
Appears in collections:	Publikationen School of Engineering

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Brunner, U., & Stockinger, K. (2020). Entity matching with transformer architectures - a step forward in data integration [Conference paper]. Proceedings of EDBT 2020, 463–473. https://doi.org/10.5441/002/edbt.2020.58

Brunner, U. and Stockinger, K. (2020) ‘Entity matching with transformer architectures - a step forward in data integration’, in Proceedings of EDBT 2020. OpenProceedings, pp. 463–473. Available at: https://doi.org/10.5441/002/edbt.2020.58.

U. Brunner and K. Stockinger, “Entity matching with transformer architectures - a step forward in data integration,” in Proceedings of EDBT 2020, Mar. 2020, pp. 463–473. doi: 10.5441/002/edbt.2020.58.

BRUNNER, Ursin und Kurt STOCKINGER, 2020. Entity matching with transformer architectures - a step forward in data integration. In: Proceedings of EDBT 2020. Conference paper. OpenProceedings. März 2020. S. 463–473. ISBN 978-3-89318-083-7

Brunner, Ursin, and Kurt Stockinger. 2020. “Entity Matching with Transformer Architectures - a Step Forward in Data Integration.” Conference paper. In Proceedings of EDBT 2020, 463–73. OpenProceedings. https://doi.org/10.5441/002/edbt.2020.58.

Brunner, Ursin, and Kurt Stockinger. “Entity Matching with Transformer Architectures - a Step Forward in Data Integration.” Proceedings of EDBT 2020, OpenProceedings, 2020, pp. 463–73, https://doi.org/10.5441/002/edbt.2020.58.