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) lies at the core of data integration and cleaning and, thus, a bulk of
the research examines ways for improving its effectiveness and time efficiency. The initial …

Abstract Knowledge graphs holistically integrate information about entities from multiple
sources. A key step in the construction and maintenance of knowledge graphs is 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 …

Current data integration approaches are mostly limited to few data sources, partly due to the
use of binary match approaches between pairs of sources. We thus advocate for the …

Entity resolution identifies semantically equivalent entities, eg, describing the same product
or customer. It is especially challenging for big data applications where large volumes of …

Entity resolution identifies semantically equivalent entities, eg describing the same product
or customer. It is especially challenging for Big Data applications where large volumes of …

Duplicate detection algorithms produce clusters of database records, each cluster
representing a single real-world entity. As most of these algorithms use pairwise …

The data quality improvement of DBpedia has been in the focus of many publications in the
past years with topics covering both knowledge enrichment techniques such as type …

Data integration in the Web of Data is not limited to the pairwise linking of entities but often
requires to cluster entities of different sources, eg, within knowledge graphs. Such entity …