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 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 …

Domain-specific knowledge graphs have emerged as a field unto their own, steadily and
perhaps not so slowly. Graphs have been pervasive in AI for a long period of time, dating …

The emergence of various disruptive technologies such as big data, Internet of Things, and
artificial intelligence have instigated our society to generate enormous volumes of data. The …

One of the most important tasks for improving data quality and the reliability of data analytics
results is Entity Resolution (ER). ER aims to identify different descriptions that refer to the …

Instance matching concerns identifying pairs of instances that refer to the same underlying
entity. Current state-of-the-art instance matchers use machine learning methods. Supervised …

Efficiency techniques are an integral part of Entity Resolution, since its infancy. In this
survey, we organized the bulk of works in the field into Blocking, Filtering and hybrid …

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 …