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.

• 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:

pyJedAI has been tested in Windows and Linux OS.

Basic requirements:

• Python version greater or equal to 3.8.
• For Windows, Microsoft Visual C++ 14.0 is required. Download it from Microsoft Official site.

Install the latest version of pyjedai:

pip install pyjedai

More on PyPI.

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 .

Available at Docker Hub, or clone this repo and:

docker build -f Dockerfile

         


           

See the full list of dependencies and all versions used, in this file.

Status

Statistics & Info

GitHub Discussions is the discussion forum for general questions and discussions and our recommended starting point. Please report any bugs that you find here.

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.

• Konstantinos Nikoletos, Research Associate at University of Athens, Greece
• Jakub Maciejewski, Research Associate at University of Athens, Greece
• George Papadakis, Senior Researcher at University of Athens, Greece
• Ekaterini Ioannou, Assistant Professor at Tilburg University, The Netherlands
• Manolis Koubarakis, Professor at University of Athens, Greece

This is a research project by the AI-Team of the Department of Informatics and Telecommunications at the University of Athens.

Released under the Apache-2.0 license (see LICENSE.txt).

Copyright © 2024 AI-Team, University of Athens

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This project is being funded in the context of STELAR that is an HORIZON-Europe project.