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🤖 A Python library for learning and evaluating knowledge graph embeddings
| Date | Stars |
|---|---|
| 2026-07-31 | 2019 |
| 2026-08-01 | 2020 |
| 2026-08-06 | 2020 |
Today
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growth rate 0.00%/day
<p align="center">
<img src="docs/source/logo.png" height="150">
</p>
<h1 align="center">
PyKEEN
</h1>
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<p align="center">
<b>PyKEEN</b> (<b>P</b>ython <b>K</b>nowl<b>E</b>dge <b>E</b>mbeddi<b>N</b>gs) is a Python package designed to
train and evaluate knowledge graph embedding models (incorporating multi-modal information).
</p>
<p align="center">
<a href="#installation">Installation</a> •
<a href="#quickstart">Quickstart</a> •
<a href="#datasets">Datasets (37)</a> •
<a href="#inductive-datasets">Inductive Datasets (5)</a> •
<a href="#models">Models (40)</a> •
<a href="#supporters">Support</a> •
<a href="#citation">Citation</a>
</p>
## Installation  
The latest stable version of PyKEEN requires Python 3.9+. It can be downloaded
and installed from [PyPI](https://pypi.org/project/pykeen) with:
```shell
pip install pykeen
```
The latest version of PyKEEN can be installed directly from the
source code on [GitHub](https://github.com/pykeen/pykeen) with:
```shell
pip install git+https://github.com/pykeen/pykeen.git
```
More information about installation (e.g., development mode, Windows installation, Colab, Kaggle, extras)
can be found in the [installation documentation](https://pykeen.readthedocs.io/en/latest/installation.html).
## Quickstart [](https://pykeen.readthedocs.io/en/latest/?badge=latest)
This example shows how to train a model on a dataset and test on another dataset.
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](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.TransE.html#pykeen.models.TransE)
model on the [Nations](https://pykeen.readthedocs.io/en/latest/api/pykeen.datasets.Nations.html#pykeen.datasets.Nations)
dataset. By default, the training loop uses the [stochastic local closed world assumption (sLCWA)](https://pykeen.readthedocs.io/en/latest/reference/training.html#pykeen.training.SLCWATrainingLoop)
training approach and evaluates with [rank-based evaluation](https://pykeen.readthedocs.io/en/latest/reference/evaluation/rank_based.html#pykeen.evaluation.RankBasedEvaluator).
```python
from pykeen.pipeline import pipeline
result = pipeline(
model='TransE',
dataset='nations',
)
```
The results are returned in an instance of the [PipelineResult](https://pykeen.readthedocs.io/en/latest/reference/pipeline.html#pykeen.pipeline.PipelineResult)
dataclass that has attributes for the trained model, the training loop, the evaluation, aExcerpt of 80,528 characters
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