Knowledge graph (KG) refinement refers to the process of filling in missing information,
removing redundancies, and resolving inconsistencies in KGs. With the growing popularity …

Whether the goal is to estimate the number of people that live in a congressional district, to
estimate the number of individuals that have died in an armed conflict, or to disambiguate …

A significant portion of the evolution of Linked Data datasets lies in updating the links to
other datasets. An important challenge when aiming to update these links automatically …

Record linkage is a process of identifying records that refer to the same real-world entity.
Many existing approaches to record linkage apply supervised machine learning techniques …

The tedious grunt work involved in data preparation (prep) before ML reduces ML user
productivity. It is also a roadblock to industrial-scale cloud AutoML workflows that build ML …

We study the problem of discovering joinable datasets at scale. We approach the problem
from a learning perspective relying on profiles. These are succinct representations that …

This article presents an unsupervised system that performs instance matching between
entities in schema-free Resource Description Framework (RDF) files. Rather than relying on …

Entity Resolution (ER) is the problem of determining when two entities refer to the same
underlying entity. The problem has been studied for over 50 years, and most recently, has …

Establishing identity links across RDF datasets is a central and challenging task on the way
to realising the Data Web project. It is well-known that data supplied by different sources can …

Textual bridge inspection reports are important data sources for supporting data-driven
bridge deterioration prediction and maintenance decision making. Information extraction …