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 …

Equipping machines with comprehensive knowledge of the world's entities and their
relationships has been a longstanding goal of AI. Over the last decade, large-scale …

Entity resolution (ER) is a key data integration problem. Despite the efforts in 70+ years in all
aspects of ER, there is still a high demand for democratizing ER-humans are heavily …

Deep learning based techniques have been recently used with promising results for data
integration problems. Some methods directly use pre-trained embeddings that were trained …

Entity matching (EM) identifies data instances that refer to the same real-world entity, such
as (David Smith, UWMadison) and (DM Smith, UWM). This problem has been a long …

Entity alignment (EA) finds equivalent entities that are located in different knowledge graphs
(KGs), which is an essential step to enhance the quality of KGs, and hence of significance to …

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 …

There is now more data to analyze than ever before. As data volume and variety have
increased, so have the ties between machine learning and data integration become …

Entity alignment (EA) identifies entities that refer to the same real-world object but locate in
different knowledge graphs (KGs), and has been harnessed for KG construction and …

Deep Learning revolutionizes almost all fields of computer science including data
management. However, the demand for high-quality training data is slowing down deep …