Entity resolution (also known as data matching, data linkage, record linkage, and many other terms) is the task of finding entities in a dataset that refer to the same entity across different data sources (e.g., data files, books, websites, and databases). Entity resolution is necessary when joining different data sets based on entities that may or may not share a common identifier (e.g., database key, URI, National identification number), which may be due to differences in record shape, storage location, or curator style or preference.
LinkOrgs: An R package for linking linking records on organizations using half a billion open-collaborated records from LinkedIn
pyspark-parallelised functions producing graph-theoretical metrics in connected component clusters for use in record-linkage (or other domains)
Blog of the American Statistical Association's Record Linkage Interest Group.
Emulates the methods the US Census Bureau uses to link people across multiple data sources, using open-source software (Splink) and simulated data (from pseudopeople).
MINDFIRL stands for Minimum & Necessary Disclosure for Interactive Record Linkage. It is a software for interactive record linkage.
Discover Probable Duplicates in Plant Genetic Resources Collections
A Flask app to take IDs and resolve them to Wikidata URIs
A short guide to approximate geocoding
Created by Halbert L. Dunn
Released 1946