PyKEEN (Python KnowlEdge EmbeddiNgs) is a Python package designed to train and evaluate knowledge graph embedding models (incorporating multi-modal information). It is part of the KEEN Universe.

Installation • Quickstart • Datasets • Models • Support

The development version of PyKEEN can be downloaded and installed from PyPI on Python 3.7+ with:

$ pip install pykeen

The development version of PyKEEN can be downloaded and installed from GitHub on Python 3.7+ with:

$ git clone https://github.com/pykeen/pykeeen.git pykeen
$ cd pykeen
$ pip install -e .
$ # Install pre-commit
$ pip install pre-commit
$ pre-commit install

Contributions, whether filing an issue, making a pull request, or forking, are appreciated. See CONTRIBUTING.md for more information on getting involved.

This example shows how to train a model on a data set and test on another data set.

The fastest way to get up and running is to use the pipeline function. It provides a high-level entry into the extensible functionality of this package. The following example shows how to train and evaluate the TransE model on the Nations dataset. By default, the training loop uses the stochastic local closed world assumption (sLCWA) training approach and evaluates with rank-based evaluation.

from pykeen.pipeline import pipeline
result = pipeline(
    model='TransE',
    dataset='nations',
)

The results are returned in a dataclass that has attributes for the trained model, the training loop, and the evaluation.

PyKEEN is extensible such that:

• Each model has the same API, so anything from pykeen.models can be dropped in
• Each training loop has the same API, so pykeen.training.LCWATrainingLoop can be dropped in
• Triples factories can be generated by the user with from pykeen.triples.TriplesFactory

Below are the models, data sets, training modes, evaluators, and metrics implemented in pykeen.

PyKEEN includes a set of curated experimental settings for reproducing past landmark experiments. They can be accessed and run like:

pykeen experiments reproduce tucker balazevic2019 fb15k

Where the three arguments are the model name, the reference, and the data set. The output directory can be optionally set with -d.

PyKEEN includes the ability to specify ablation studies using the hyper-parameter optimization module. They can be run like:

pykeen experiments ablation ~/path/to/config.json

This project has been supported by several organizations (in alphabetical order):

• Bayer
• Enveda Therapeutics
• Fraunhofer Institute for Algorithms and Scientific Computing
• Fraunhofer Institute for Intelligent Analysis and Information Systems
• Fraunhofer Center for Machine Learning
• Ludwig-Maximilians-Universität München
• Munich Center for Machine Learning (MCML)
• Smart Data Analytics Research Group (University of Bonn & Fraunhofer IAIS)
• Technical University of Denmark - DTU Compute - Section for Cognitive Systems
• Technical University of Denmark - DTU Compute - Section for Statistics and Data Analysis
• University of Bonn

The PyKEEN logo was designed by Carina Steinborn.