powerful end-to-end Entity Resolution workflows.
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.
- 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 the latest version of pyjedai [requires python >= 3.7]:
pip install pyjedai
More on PyPI.
Find last release source code in GitHub.
|Clean-Clean Entity Resolution.||CleanCleanER.ipynb|
|Dirty Entity Resolution.||DirtyER.ipynb|
|Fine-Tuning using Optuna.||Optuna.ipynb|
|User-Friendly Approach. WorkFlow module.||WorkFlow.ipynb|
|Raw data to pandas DataFrame.||Readers.ipynb|
See the full list of dependencies and all versions used, in this file.
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.
Team & Authors
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.
Released under the Apache-2.0 license (see LICENSE.txt).
Copyright © 2022 AI-Team, University of Athens
This project is being funded in the context of STELAR, an HORIZON-Europe project.