The paper studies the application of automated machine learning approaches (AutoML) for addressing the problem of Entity Matching (EM). This would make the existing, highly effective, Machine Learning (ML) and Deep Learning based approaches for EM usable also by non-expert users, who do not have the expertise to train and tune such complex systems. Our experiments show that the direct application of AutoML systems to this scenario does not provide high quality results. To address this issue, we introduce a new component, the EM adapter, to be pipelined with standard AutoML systems, that preprocesses the EM datasets to make them usable by automated approaches. The experimental evaluation shows that our proposal obtains the same effectiveness as the state-of-the-art EM systems, but it does not require any skill on ML to tune it.

Automated Machine Learning for Entity Matching Tasks / Paganelli, Matteo; DEL BUONO, Francesco; Pevarello, Marco; Guerra, Francesco; Vincini, Maurizio. - 2021-:(2021), pp. 325-330. ((Intervento presentato al convegno EDBT tenutosi a Nicosia nel 23-26 March 2021 [10.5441/002/edbt.2021.29].

Automated Machine Learning for Entity Matching Tasks

Matteo Paganelli
;
Francesco Del Buono
;
Francesco Guerra
;
Maurizio Vincini
2021

Abstract

The paper studies the application of automated machine learning approaches (AutoML) for addressing the problem of Entity Matching (EM). This would make the existing, highly effective, Machine Learning (ML) and Deep Learning based approaches for EM usable also by non-expert users, who do not have the expertise to train and tune such complex systems. Our experiments show that the direct application of AutoML systems to this scenario does not provide high quality results. To address this issue, we introduce a new component, the EM adapter, to be pipelined with standard AutoML systems, that preprocesses the EM datasets to make them usable by automated approaches. The experimental evaluation shows that our proposal obtains the same effectiveness as the state-of-the-art EM systems, but it does not require any skill on ML to tune it.
EDBT
Nicosia
23-26 March 2021
2021-
325
330
Paganelli, Matteo; DEL BUONO, Francesco; Pevarello, Marco; Guerra, Francesco; Vincini, Maurizio
Automated Machine Learning for Entity Matching Tasks / Paganelli, Matteo; DEL BUONO, Francesco; Pevarello, Marco; Guerra, Francesco; Vincini, Maurizio. - 2021-:(2021), pp. 325-330. ((Intervento presentato al convegno EDBT tenutosi a Nicosia nel 23-26 March 2021 [10.5441/002/edbt.2021.29].
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11380/1247077
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