Details for reproducing the experiments in our d-blink paper
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Updated
Jun 10, 2021 - R
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.
Details for reproducing the experiments in our d-blink paper
ProxCluster is a framework for Incremental Entity Resolution that leverages concepts similar to K-Means for clustering duplicates. This work was developed as the final paper for my Bachelor degree in Computer Science
Fuzzymatching made easy
A Winner-Take-All Hashing-Based Unsupervised Model for Entity Resolution Problems. [B. Sc. Thesis]
List of entity resolution software and resources.
Intent detection and Slot filling
Tool to explain Entity Resolution model predictions
A proof-of-concept entity resolution approach, with Tensorflow, inside a MonetDB.
This project aims to clusterize duplicates from the Music_Brainz dataset using a custom Kmeans implementation.
utilities for working with Entity Resolution models
Mirror of https://bitbucket.org/resteorts/smered
🔎 Finds fuzzy matches between datasets
Entity Resolution and Record Linkage library
Python-based tool to link legal entity datasets when no common ID is available, using name and address information
Scalable record-level matching rules
U.S. Hospital and Hotel Recommendation System based on CMS and Kaggle Datasets
TFIDF / KNN based string matching
Person entity identification and matching using face recognition and machine learning algorithms
Created by Halbert L. Dunn
Released 1946