One of the most critical tasks for improving data quality and increasing the reliability of data
analytics is Entity Resolution (ER), which aims to identify different descriptions that refer to …

Entity Resolution (ER), a core task of Data Integration, detects different entity profiles that
correspond to the same real-world object. Due to its inherently quadratic complexity, a series …

Entity matching (EM) finds data instances that refer to the same real-world entity. In this
paper we examine applying deep learning (DL) to EM, to understand DL's benefits and …

This is an overview of the end-to-end data cleaning process. Data quality is one of the most
important problems in data management, since dirty data often leads to inaccurate data …

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 …

Entity matching is the problem of identifying which records refer to the same real-world
entity. It has been actively researched for decades, and a variety of different approaches …

Sensitive personal data are created in many application domains, and there is now an
increasing demand to share, integrate, and link such data within and across organisations in …

Entity resolution (ER) is a significant task in data integration, which aims to detect all entity
profiles that correspond to the same real-world entity. Due to its inherently quadratic …

Can one build a knowledge graph (KG) for all products in the world? Knowledge graphs
have firmly established themselves as valuable sources of information for search and …

Entity resolution (ER) refers to the problem of matching records in one or more relations that
refer to the same real-world entity. While supervised machine learning (ML) approaches …