Mock data that is used for unit testing of the Rosette API bindings
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Updated
Jan 13, 2016
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.
Mock data that is used for unit testing of the Rosette API bindings
Fork of the Freely Extensible Biomedical Record Linkage program
Parallel Blocking in MapReduce
Mirror of https://bitbucket.org/resteorts/smered
Matching algorithm for movies in Amazon and Rotten Tomatoes datasets
My entry to a data analysis / record linkage coding challenge
Implementing instance matching algorithm on GeoLink repository.
Java client for entity-fishing
Person entity identification and matching using face recognition and machine learning algorithms
Performs unique entity estimation corresponding to Chen, Shrivastava, Steorts (2018).
A general purpose deduplication framework
Learning String Alignments for Entity Aliases
An Anaconda3 environment with relevant python libraries to support various linked data OpenRefine reconciliation scripts
A Flask app to take IDs and resolve them to Wikidata URIs
Pre-processing script for data from the Survey of Household Income and Wealth
Intent detection and Slot filling
Created by Halbert L. Dunn
Released 1946