======== TorchKGE

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TorchKGE: Knowledge Graph embedding in Python and Pytorch.

TorchKGE is a Python module for knowledge graph (KG) embedding relying solely on Pytorch. This package provides researchers and engineers with a clean and efficient API to design and test new models. It features a KG data structure, simple model interfaces and modules for negative sampling and model evaluation. Its main strength is a highly efficient evaluation module for the link prediction task, a central application of KG embedding. It has been observed <https://torchkge.readthedocs.io/en/latest/reference/evaluation.html>_ to be up to five times faster than AmpliGraph <https://docs.ampligraph.org/>_ and twenty-four times faster than OpenKE <https://github.com/thunlp/OpenKE>_. Various KG embedding models are also already implemented. Special attention has been paid to code efficiency and simplicity, documentation and API consistency. It is distributed using PyPI under BSD license.

Citations

If you find this code useful in your research, please consider citing our paper <https://arxiv.org/abs/2009.02963>_ (presented at IWKG-KDD <https://suitclub.ischool.utexas.edu/IWKG_KDD2020/index.html>_ 2020):

.. code::

@inproceedings{arm2020torchkge,
    title={TorchKGE: Knowledge Graph Embedding in Python and PyTorch},
    author={Armand Boschin},
    year={2020},
    month={Aug},
    booktitle={International Workshop on Knowledge Graph: Mining Knowledge Graph for Deep Insights},
}

• Free software: BSD license
• Documentation: https://torchkge.readthedocs.io.