Entity Matching is the task of deciding whether two entity descriptions refer to the same real-
world entity. Entity Matching is a central step in most data integration pipelines and an …

Entity resolution (ER) is the process of identifying records that refer to the same entities
within one or across multiple databases. Numerous techniques have been developed to …

The difficulty of an entity matching task depends on a combination of multiple factors such as
the amount of corner-case pairs, the fraction of entities in the test set that have not been …

Generative AI has made significant strides, yet concerns about the accuracy and reliability of
its outputs continue to grow. Such inaccuracies can have serious consequences such as …

Entity Matching (EM), which aims to identify whether two entity records from two relational
tables refer to the same real-world entity, is one of the fundamental problems in data …

Data matching-which decides whether two data elements (eg, string, tuple, column, or
knowledge graph entity) are the" same"(aka a match)-is a key concept in data integration …

In this paper, we introduce AttendEM, a framework for entity matching (EM), ie, pairwise
identification of duplicates across databases. Eschewing the prevalent focus on text …

Cardinality Estimation (CE) is a fundamental but critical problem in DBMS query
optimization, while deep learning techniques have made significant breakthroughs in the …

Entity resolution (ER) is a core data integration problem that identifies pairs of data instances
referring to the same real-world entities, and the state-of-the-art results of ER are achieved …

Data preparation is crucial in achieving optimized results for machine learning (ML).
However, having a good data preparation pipeline is highly non-trivial for ML practitioners …