Entity matching refers to the task of determining whether two different representations refer
to the same real-world entity. It continues to be a prevalent problem for many organizations …

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 …

Big data is typically characterized with 4V's: Volume, Velocity, Variety and Veracity. When it
comes to big graphs, these challenges become even more staggering. Each and every of …

Entity Resolution (ER) lies at the core of data integration and cleaning and, thus, a bulk of
the research examines ways for improving its effectiveness and time efficiency. The initial …

Can AI help automate human-easy but computer-hard data preparation tasks that burden
data scientists, practitioners, and crowd workers? We answer this question by presenting …

Many recent works on Entity Resolution (ER) leverage Deep Learning techniques involving
language models to improve effectiveness. This is applied to both main steps of ER, ie …

The last few years witnessed significant advances in building automated or semi-automated
data quality, data cleaning and data integration systems powered by machine learning (ML) …

Data quality affects machine learning (ML) model performances, and data scientists spend
considerable amount of time on data cleaning before model training. However, to date, there …

End-to-end AutoML has attracted intensive interests from both academia and industry which
automatically searches for ML pipelines in a space induced by feature engineering …

Entity Resolution (ER) aims to identify and merge records that refer to the same real-world
entity. ER is typically employed as an expensive cleaning step on the entire data before …