A database management system for restaurant inspection records, restaurant-related tweets, and other relevant data.
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Updated
Jan 8, 2021 - 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.
A database management system for restaurant inspection records, restaurant-related tweets, and other relevant data.
Record linkage - simple, flexible, efficient.
My entry to a data analysis / record linkage coding challenge
Range of computer science applications using Python.
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.
🕸️ Little helper for handling entity clusters
Interpretable metadata for the results of NHS England record linkage
Python library for the generation and mutation of realistic personal identification data at scale
Example scripts for generating data with Gecko
utilities for working with Entity Resolution models
An extension for ASReview Lab to preprocess the dataset before importing in ASReview
a Python library for scalable entity resolution, using active learning to learn blocking configurations, generate comparison pairs, then clasify matches
Tools for improved blocking for historical record linkage
A Python package designed to allow health, biomedical and other researchers to clean (standardise) and deduplicate or link data sets of all sizes faster, with less effort and with improved quality.
🧱 blocking methods for entity resolution
Fast, accurate, open-source geocoding in Python
CERTA - Computing Entity Resolution explanations with TriAngles
PySpark implementation of the Open Privacy Preserving Record Linkage (OPPRL) specification.
Created by Halbert L. Dunn
Released 1946