Fork of the Freely Extensible Biomedical Record Linkage program
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Updated
Nov 4, 2016 - Python
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.
Fork of the Freely Extensible Biomedical Record Linkage program
My entry to a data analysis / record linkage coding challenge
Tools for improved blocking for historical record linkage
Performs unique entity estimation corresponding to Chen, Shrivastava, Steorts (2018).
🆔 Command line tool for deduplicating CSV files
Learned string similarity for entity names using optimal transport.
A database management system for restaurant inspection records, restaurant-related tweets, and other relevant data.
Lo scopo di questo progetto è quello di confrontare l’efficienza di diversi metodi per il confronto approssimativo di stringhe applicate nell’ambito del record linkage.
A Python package for efficient evaluation based on OASIS (Optimal Asymptotic Sequential Importance Sampling).
Merge Dirty Data with Clean Reference Tables
utilities for working with Entity Resolution models
A maximum-strength name parser for record linkage.
Privacy Preserving Record Linkage Service
Deduplicate data using fuzzy and deterministic matching rules.
An extension for ASReview Lab to preprocess the dataset before importing in ASReview
Tools for EHR patient de-duplication (aka entity resolution)
Entity Resolution and Record Linkage library
🆔 Examples for using the dedupe library
Created by Halbert L. Dunn
Released 1946