Entity matching on the DBLP-ACM dataset
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Updated
Jan 10, 2023 - 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 matching on the DBLP-ACM dataset
Course project for CS839 Spring18 at UW-Madison
Entity matching and record de-duplication project with Amazon Development Centre Scotland
4 stage data science project
Submissions for Data Science: Principles, Algorithms, and Applications (CS839) @ UW-Madison
AdapterEM: Pre-trained Language Model Adaptation for Generalized Entity Matching using Adapter-tuning
Submission Repository for Data Science Class Project
Entity-Match books from goodreads.com and bookdepository.com
Entity Matching specific Explanation tool. Landmark generates reliable and coherent explanations through a perturbation analysis.
Data Augmentation for Entity Matching using Consistency Learning
Libem notebooks.
This repository is a supplement resource for a research article entitled "Deep Learning Untuk Entity Matching Produk Kamera Antar Online Store Menggunakan DeepMatcher"
An exploration of generalizable approaches to unsupervised entity matching for use in linking tabular public energy data sources.
Performed entity matching on Album music data across two different (extracted) tables from metacritic.com and wikipedia.
Code for the paper "Match, Compare, or Select? An Investigation of Large Language Models for Entity Matching"
Created by Halbert L. Dunn
Released 1946