As data privacy increasingly becomes a critical societal concern, federated learning has
been a hot research topic in enabling the collaborative training of machine learning models …

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 …

Federated learning (FL) is a machine learning setting where many clients (eg, mobile
devices or whole organizations) collaboratively train a model under the orchestration of a …

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 …

Consider two data providers, each maintaining private records of different feature sets about
common entities. They aim to learn a linear model jointly in a federated setting, namely, data …

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 …

As we are moving towards the Internet of Things (IoT), the number of sensors deployed
around the world is growing at a rapid pace. Market research has shown a significant growth …

Record linkage is the process of identifying and linking records about the same entities from
one or more databases. Record linkage can be viewed as a classification problem where …

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 …

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 …