An open-source compound AI toolchain for fast and accurate entity matching, powered by LLMs.
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Updated
Jun 7, 2024 - 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.
An open-source compound AI toolchain for fast and accurate entity matching, powered by LLMs.
Fast, accurate and scalable probabilistic data linkage with support for multiple SQL backends
An open-source library that leverages Python’s data science ecosystem to build powerful end-to-end Entity Resolution workflows.
Curated list of awesome software and resources for Senzing, The First Real-Time AI for Entity Resolution.
🔎 Finds fuzzy matches between datasets
🔎 Finds fuzzy matches between CSV files
Link Wikidata items to large catalogs
A powerful and modular toolkit for record linkage and duplicate detection in Python
AdapterEM: Pre-trained Language Model Adaptation for Generalized Entity Matching using Adapter-tuning
An extension for ASReview Lab to preprocess the dataset before importing in ASReview
A maximum-strength name parser for record linkage.
Record linking package that fuzzy matches two Python pandas dataframes using sqlite3 fts4
A Single View application aggregates and reconciles data from multiple sources to create a single view of an entity.
Created by Halbert L. Dunn
Released 1946