Abstract Machine Learning (ML) is integrated into a growing number of systems for various
applications. Because the performance of an ML model is highly dependent on the quality of …

Entity matching, a core data integration problem, is the task of deciding whether two data
tuples refer to the same real-world entity. Recent advances in deep learning methods, using …

While the state-of-the-art performance on entity resolution (ER) has been achieved by deep
learning, its effectiveness depends on large quantities of accurately labeled training data. To …

Deep Learning (DL) techniques now constitute the state-of-the-art for important problems in
areas such as text and image processing, and there have been impactful results that deploy …

Entity resolution (ER), precisely identifying different representations of the same real-world
entities, is critical for data integration. The ER question has been studied for many years …

Entity Resolution (ER), a fundamental task in data cleaning and integration, is critical in
various fields such as healthcare, e-commerce, and social networks. Traditional ER methods …

Entity resolution, accurately identifying various representations of the same real-world
entities, is a crucial part of data integration systems. While existing learning-based models …

Evaluation is a bottleneck in data integration processes: it is performed by domain experts
through manual onerous data inspections. This task is particularly heavy in real business …

Entity matching is a key technique in data quality research, which refers to the identification
of records that refer to the same real-world entity in different data sources. This paper …

Entity Resolution (ER) is a fundamental task in data integration, aiming to identify data
objects across different sources that refer to the same real-world entity. In recent years, deep …