An open source, high scalability toolkit in Java for Entity Resolution.
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Updated
Apr 12, 2024 - Java
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, high scalability toolkit in Java for Entity Resolution.
Entity resolution for Elasticsearch.
MetaSRA: normalized sample-specific metadata for the Sequence Read Archive
PyTorch library for transforming entities like companies, products, etc. into vectors to support scalable Record Linkage / Entity Resolution using Approximate Nearest Neighbors.
An open-source library that leverages Python’s data science ecosystem to build powerful end-to-end Entity Resolution workflows.
CLK hash: hash pii for entity matching
A convenient way to link, deduplicate, aggregate and cluster data(frames) in Python using deep learning
Implementation of the paper "Deep Indexed Active Learning for Matching Heterogeneous Entity Representations"
Code for the paper "CollaborEM: A Self-supervised Entity Matching Framework Using Multi-features Collaboration". TKDE 2021.
Code for the paper "PromptEM: Prompt-tuning for Low-resource Generalized Entity Matching". VLDB 2023.
CERTA - Computing Entity Resolution explanations with TriAngles
Spark Search - high performance advanced search features based on Apache Lucene
Curated list of awesome software and resources for Senzing, The First Real-Time AI for Entity Resolution.
data and code for the paper: Bridging the Gap between Reality and Ideality of Entity Matching: A Revisiting and Benchmark Re-Construction
☕ Multi-source ORM for Javascript Client+Server
Entity Matching specific Explanation tool. Landmark generates reliable and coherent explanations through a perturbation analysis.
Entity Matching Model solves the problem of matching company names between two possibly very large datasets.
Entity matching and record de-duplication project with Amazon Development Centre Scotland
Created by Halbert L. Dunn
Released 1946