2021
DOI: 10.14778/3476249.3476294
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Deep learning for blocking in entity matching

Abstract: Entity matching (EM) finds data instances that refer to the same real-world entity. Most EM solutions perform blocking then matching. Many works have applied deep learning (DL) to matching, but far fewer works have applied DL to blocking. These blocking works are also limited in that they consider only a simple form of DL and some of them require labeled training data. In this paper, we develop the DeepBlocker framework that significantly advances the state of the art in applying DL to blocking for EM. We firs… Show more

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Cited by 50 publications
(33 citation statements)
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“…3) DeepBlocker [59]. It is the most recent method based on deep learning, consistently outperforms all others, e.g., AutoBlock [67] and DeepER [19].…”
Section: Nearest-neighbor (Nn) Methodsmentioning
confidence: 99%
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“…3) DeepBlocker [59]. It is the most recent method based on deep learning, consistently outperforms all others, e.g., AutoBlock [67] and DeepER [19].…”
Section: Nearest-neighbor (Nn) Methodsmentioning
confidence: 99%
“…In both cases, the output consists of the detected duplicate profiles. Following [36,45,59], we exclusively consider Clean-Clean ER in the following, which also fits naturally to the index-query scheme of nearest neighbor search algorithms, as shown in Figure 2.…”
Section: Preliminariesmentioning
confidence: 99%
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