An open-source library that builds powerful end-to-end Entity Resolution workflows.
Project description
powerful end-to-end Entity Resolution workflows.
Overview
pyJedAI is a python framework, aiming to offer experts and novice users, robust and fast solutions for multiple types of Entity Resolution problems. It is builded using state-of-the-art python frameworks. pyJedAI constitutes the sole open-source Link Discovery tool that is capable of exploiting the latest breakthroughs in Deep Learning and NLP techniques, which are publicly available through the Python data science ecosystem. This applies to both blocking and matching, thus ensuring high time efficiency, high scalability as well as high effectiveness, without requiring any labelled instances from the user.
Key-Features
- Input data-type independent. Both structured and semi-structured data can be processed.
- Various implemented algorithms.
- Easy-to-use.
- Utilizes some of the famous and cutting-edge machine learning packages.
- Offers supervised and un-supervised ML techniques.
Open demos are available in:
Google Colab Hands-on demo:
Install
PyPI
Install the latest version of pyjedai [requires python >= 3.8]:
pip install pyjedai
More on PyPI.
Git
Set up locally:
git clone https://github.com/AI-team-UoA/pyJedAI.git
go to the root directory with cd pyJedAI
and type:
pip install .
Dependencies
See the full list of dependencies and all versions used, in this file.
Status
Statistics & Info
Bugs, Discussions & News
GitHub Discussions is the discussion forum for general questions and discussions and our recommended starting point. Please report any bugs that you find here.
Java - Wed Application
For Java users checkout the initial JedAI. There you can find Java based code and a Web Application for interactive creation of ER workflows.
JedAI constitutes an open source, high scalability toolkit that offers out-of-the-box solutions for any data integration task, e.g., Record Linkage, Entity Resolution and Link Discovery. At its core lies a set of domain-independent, state-of-the-art techniques that apply to both RDF and relational data.
Team & Authors
- Konstantinos Nikoletos
- Jakub Maciejewski
- George Papadakis
- Manolis Koubarakis
Research and development is made under the supervision of Pr. Manolis Koubarakis. This is a research project by the AI-Team of the Department of Informatics and Telecommunications at the University of Athens.
License
Released under the Apache-2.0 license (see LICENSE.txt).
Copyright © 2023 AI-Team, University of Athens
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.