Entity-Match books from goodreads.com and bookdepository.com
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Updated
Mar 24, 2018 - 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.
Entity-Match books from goodreads.com and bookdepository.com
Implementation of the paper "Deep Indexed Active Learning for Matching Heterogeneous Entity Representations"
utilities for working with Entity Resolution models
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.
Entity matching on the DBLP-ACM dataset
AdapterEM: Pre-trained Language Model Adaptation for Generalized Entity Matching using Adapter-tuning
CLK hash: hash pii for entity matching
Code and data for the paper "Bridging the Gap between Reality and Ideality of Entity Matching: A Revisiting and Benchmark Re-Construction"
MetaSRA: normalized sample-specific metadata for the Sequence Read Archive
JOBSKAPE: A Framework for Generating Synthetic Job Postings to Enhance Skill Matching
Fair Entity Matching: A Fairness Suite for Auditing Entity Matching Approaches
CERTA - Computing Entity Resolution explanations with TriAngles
A convenient way to link, deduplicate, aggregate and cluster data(frames) in Python using deep learning
Libem sample datasets.
Reproducibility experiments for Generalized Supervised Meta-blocking
Entity Matching Model solves the problem of matching company names between two possibly very large datasets.
Code for the paper "Match, Compare, or Select? An Investigation of Large Language Models for Entity Matching"
Created by Halbert L. Dunn
Released 1946