| Name | pykeen JSON |
| Version |
1.11.0
JSON |
| download |
| home_page | None |
| Summary | A package for training and evaluating multimodal knowledge graph embeddings |
| upload_time | 2024-10-29 16:25:58 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.9 |
| license | MIT License Copyright (c) 2019-2024 PyKEEN Project Team Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. |
| keywords |
snekpack
cookiecutter
knowledge graph embeddings
machine learning
data mining
linked data
|
| VCS |
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| bugtrack_url |
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| requirements |
No requirements were recorded.
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<p align="center">
<img src="docs/source/logo.png" height="150">
</p>
<h1 align="center">
PyKEEN
</h1>
<p align="center">
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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, and more. See the tutorials
on [using your own dataset](https://pykeen.readthedocs.io/en/latest/byo/data.html),
[understanding the evaluation](https://pykeen.readthedocs.io/en/latest/tutorial/understanding_evaluation.html),
and [making novel link predictions](https://pykeen.readthedocs.io/en/latest/tutorial/making_predictions.html).
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``
The full documentation can be found at https://pykeen.readthedocs.io.
## Implementation
Below are the models, datasets, training modes, evaluators, and metrics implemented
in ``pykeen``.
### Datasets
The following 37 datasets are built in to PyKEEN. The citation for each dataset corresponds to either the paper
describing the dataset, the first paper published using the dataset with knowledge graph embedding models,
or the URL for the dataset if neither of the first two are available. If you want to use a custom dataset,
see the [Bring Your Own Dataset](https://pykeen.readthedocs.io/en/latest/byo/data.html) tutorial. If you
have a suggestion for another dataset to include in PyKEEN, please let us know
[here](https://github.com/pykeen/pykeen/issues/new?assignees=cthoyt&labels=New+Dataset&template=dataset-request.md&title=Add+%5BDATASET+NAME%5D).
| Name | Documentation | Citation | Entities | Relations | Triples |
|------------------------------------|---------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------|------------|-------------|-----------|
| Aristo-v4 | [`pykeen.datasets.AristoV4`](https://pykeen.readthedocs.io/en/latest/api/pykeen.datasets.AristoV4.html) | [Chen *et al*., 2021](https://openreview.net/pdf?id=Qa3uS3H7-Le) | 42016 | 1593 | 279425 |
| BioKG | [`pykeen.datasets.BioKG`](https://pykeen.readthedocs.io/en/latest/api/pykeen.datasets.BioKG.html) | [Walsh *et al*., 2019](https://doi.org/10.1145/3340531.3412776) | 105524 | 17 | 2067997 |
| Clinical Knowledge Graph | [`pykeen.datasets.CKG`](https://pykeen.readthedocs.io/en/latest/api/pykeen.datasets.CKG.html) | [Santos *et al*., 2020](https://doi.org/10.1101/2020.05.09.084897) | 7617419 | 11 | 26691525 |
| CN3l Family | [`pykeen.datasets.CN3l`](https://pykeen.readthedocs.io/en/latest/api/pykeen.datasets.CN3l.html) | [Chen *et al*., 2017](https://www.ijcai.org/Proceedings/2017/0209.pdf) | 3206 | 42 | 21777 |
| CoDEx (large) | [`pykeen.datasets.CoDExLarge`](https://pykeen.readthedocs.io/en/latest/api/pykeen.datasets.CoDExLarge.html) | [Safavi *et al*., 2020](https://arxiv.org/abs/2009.07810) | 77951 | 69 | 612437 |
| CoDEx (medium) | [`pykeen.datasets.CoDExMedium`](https://pykeen.readthedocs.io/en/latest/api/pykeen.datasets.CoDExMedium.html) | [Safavi *et al*., 2020](https://arxiv.org/abs/2009.07810) | 17050 | 51 | 206205 |
| CoDEx (small) | [`pykeen.datasets.CoDExSmall`](https://pykeen.readthedocs.io/en/latest/api/pykeen.datasets.CoDExSmall.html) | [Safavi *et al*., 2020](https://arxiv.org/abs/2009.07810) | 2034 | 42 | 36543 |
| ConceptNet | [`pykeen.datasets.ConceptNet`](https://pykeen.readthedocs.io/en/latest/api/pykeen.datasets.ConceptNet.html) | [Speer *et al*., 2017](https://arxiv.org/abs/1612.03975) | 28370083 | 50 | 34074917 |
| Countries | [`pykeen.datasets.Countries`](https://pykeen.readthedocs.io/en/latest/api/pykeen.datasets.Countries.html) | [Bouchard *et al*., 2015](https://www.aaai.org/ocs/index.php/SSS/SSS15/paper/view/10257/10026) | 271 | 2 | 1158 |
| Commonsense Knowledge Graph | [`pykeen.datasets.CSKG`](https://pykeen.readthedocs.io/en/latest/api/pykeen.datasets.CSKG.html) | [Ilievski *et al*., 2020](http://arxiv.org/abs/2012.11490) | 2087833 | 58 | 4598728 |
| DB100K | [`pykeen.datasets.DB100K`](https://pykeen.readthedocs.io/en/latest/api/pykeen.datasets.DB100K.html) | [Ding *et al*., 2018](https://arxiv.org/abs/1805.02408) | 99604 | 470 | 697479 |
| DBpedia50 | [`pykeen.datasets.DBpedia50`](https://pykeen.readthedocs.io/en/latest/api/pykeen.datasets.DBpedia50.html) | [Shi *et al*., 2017](https://arxiv.org/abs/1711.03438) | 24624 | 351 | 34421 |
| Drug Repositioning Knowledge Graph | [`pykeen.datasets.DRKG`](https://pykeen.readthedocs.io/en/latest/api/pykeen.datasets.DRKG.html) | [`gnn4dr/DRKG`](https://github.com/gnn4dr/DRKG) | 97238 | 107 | 5874257 |
| FB15k | [`pykeen.datasets.FB15k`](https://pykeen.readthedocs.io/en/latest/api/pykeen.datasets.FB15k.html) | [Bordes *et al*., 2013](http://papers.nips.cc/paper/5071-translating-embeddings-for-modeling-multi-relational-data.pdf) | 14951 | 1345 | 592213 |
| FB15k-237 | [`pykeen.datasets.FB15k237`](https://pykeen.readthedocs.io/en/latest/api/pykeen.datasets.FB15k237.html) | [Toutanova *et al*., 2015](https://www.aclweb.org/anthology/W15-4007/) | 14505 | 237 | 310079 |
| Global Biotic Interactions | [`pykeen.datasets.Globi`](https://pykeen.readthedocs.io/en/latest/api/pykeen.datasets.Globi.html) | [Poelen *et al*., 2014](https://doi.org/10.1016/j.ecoinf.2014.08.005) | 404207 | 39 | 1966385 |
| Hetionet | [`pykeen.datasets.Hetionet`](https://pykeen.readthedocs.io/en/latest/api/pykeen.datasets.Hetionet.html) | [Himmelstein *et al*., 2017](https://doi.org/10.7554/eLife.26726) | 45158 | 24 | 2250197 |
| Kinships | [`pykeen.datasets.Kinships`](https://pykeen.readthedocs.io/en/latest/api/pykeen.datasets.Kinships.html) | [Kemp *et al*., 2006](https://www.aaai.org/Papers/AAAI/2006/AAAI06-061.pdf) | 104 | 25 | 10686 |
| Nations | [`pykeen.datasets.Nations`](https://pykeen.readthedocs.io/en/latest/api/pykeen.datasets.Nations.html) | [`ZhenfengLei/KGDatasets`](https://github.com/ZhenfengLei/KGDatasets) | 14 | 55 | 1992 |
| NationsL | [`pykeen.datasets.NationsLiteral`](https://pykeen.readthedocs.io/en/latest/api/pykeen.datasets.NationsLiteral.html) | [`pykeen/pykeen`](https://github.com/pykeen/pykeen) | 14 | 55 | 1992 |
| OGB BioKG | [`pykeen.datasets.OGBBioKG`](https://pykeen.readthedocs.io/en/latest/api/pykeen.datasets.OGBBioKG.html) | [Hu *et al*., 2020](https://arxiv.org/abs/2005.00687) | 93773 | 51 | 5088434 |
| OGB WikiKG2 | [`pykeen.datasets.OGBWikiKG2`](https://pykeen.readthedocs.io/en/latest/api/pykeen.datasets.OGBWikiKG2.html) | [Hu *et al*., 2020](https://arxiv.org/abs/2005.00687) | 2500604 | 535 | 17137181 |
| OpenBioLink | [`pykeen.datasets.OpenBioLink`](https://pykeen.readthedocs.io/en/latest/api/pykeen.datasets.OpenBioLink.html) | [Breit *et al*., 2020](https://doi.org/10.1093/bioinformatics/btaa274) | 180992 | 28 | 4563407 |
| OpenBioLink LQ | [`pykeen.datasets.OpenBioLinkLQ`](https://pykeen.readthedocs.io/en/latest/api/pykeen.datasets.OpenBioLinkLQ.html) | [Breit *et al*., 2020](https://doi.org/10.1093/bioinformatics/btaa274) | 480876 | 32 | 27320889 |
| OpenEA Family | [`pykeen.datasets.OpenEA`](https://pykeen.readthedocs.io/en/latest/api/pykeen.datasets.OpenEA.html) | [Sun *et al*., 2020](http://www.vldb.org/pvldb/vol13/p2326-sun.pdf) | 15000 | 248 | 38265 |
| PharMeBINet | [`pykeen.datasets.PharMeBINet`](https://pykeen.readthedocs.io/en/latest/api/pykeen.datasets.PharMeBINet.html) | [Königs *et al*., 2022](https://www.nature.com/articles/s41597-022-01510-3) | 2869407 | 208 | 15883653 |
| PharmKG | [`pykeen.datasets.PharmKG`](https://pykeen.readthedocs.io/en/latest/api/pykeen.datasets.PharmKG.html) | [Zheng *et al*., 2020](https://doi.org/10.1093/bib/bbaa344) | 188296 | 39 | 1093236 |
| PharmKG8k | [`pykeen.datasets.PharmKG8k`](https://pykeen.readthedocs.io/en/latest/api/pykeen.datasets.PharmKG8k.html) | [Zheng *et al*., 2020](https://doi.org/10.1093/bib/bbaa344) | 7247 | 28 | 485787 |
| PrimeKG | [`pykeen.datasets.PrimeKG`](https://pykeen.readthedocs.io/en/latest/api/pykeen.datasets.PrimeKG.html) | [Chandak *et al*., 2022](https://doi.org/10.1101/2022.05.01.489928) | 129375 | 30 | 8100498 |
| Unified Medical Language System | [`pykeen.datasets.UMLS`](https://pykeen.readthedocs.io/en/latest/api/pykeen.datasets.UMLS.html) | [`ZhenfengLei/KGDatasets`](https://github.com/ZhenfengLei/KGDatasets) | 135 | 46 | 6529 |
| WD50K (triples) | [`pykeen.datasets.WD50KT`](https://pykeen.readthedocs.io/en/latest/api/pykeen.datasets.WD50KT.html) | [Galkin *et al*., 2020](https://www.aclweb.org/anthology/2020.emnlp-main.596/) | 40107 | 473 | 232344 |
| Wikidata5M | [`pykeen.datasets.Wikidata5M`](https://pykeen.readthedocs.io/en/latest/api/pykeen.datasets.Wikidata5M.html) | [Wang *et al*., 2019](https://arxiv.org/abs/1911.06136) | 4594149 | 822 | 20624239 |
| WK3l-120k Family | [`pykeen.datasets.WK3l120k`](https://pykeen.readthedocs.io/en/latest/api/pykeen.datasets.WK3l120k.html) | [Chen *et al*., 2017](https://www.ijcai.org/Proceedings/2017/0209.pdf) | 119748 | 3109 | 1375406 |
| WK3l-15k Family | [`pykeen.datasets.WK3l15k`](https://pykeen.readthedocs.io/en/latest/api/pykeen.datasets.WK3l15k.html) | [Chen *et al*., 2017](https://www.ijcai.org/Proceedings/2017/0209.pdf) | 15126 | 1841 | 209041 |
| WordNet-18 | [`pykeen.datasets.WN18`](https://pykeen.readthedocs.io/en/latest/api/pykeen.datasets.WN18.html) | [Bordes *et al*., 2014](https://arxiv.org/abs/1301.3485) | 40943 | 18 | 151442 |
| WordNet-18 (RR) | [`pykeen.datasets.WN18RR`](https://pykeen.readthedocs.io/en/latest/api/pykeen.datasets.WN18RR.html) | [Toutanova *et al*., 2015](https://www.aclweb.org/anthology/W15-4007/) | 40559 | 11 | 92583 |
| YAGO3-10 | [`pykeen.datasets.YAGO310`](https://pykeen.readthedocs.io/en/latest/api/pykeen.datasets.YAGO310.html) | [Mahdisoltani *et al*., 2015](http://service.tsi.telecom-paristech.fr/cgi-bin//valipub_download.cgi?dId=284) | 123143 | 37 | 1089000 |
### Inductive Datasets
The following 5 inductive datasets are built in to PyKEEN.
| Name | Documentation | Citation |
|-----------------|---------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------|
| ILPC2022 Large | [`pykeen.datasets.ILPC2022Large`](https://pykeen.readthedocs.io/en/latest/api/pykeen.datasets.ILPC2022Large.html) | [Galkin *et al*., 2022](https://arxiv.org/abs/2203.01520) |
| ILPC2022 Small | [`pykeen.datasets.ILPC2022Small`](https://pykeen.readthedocs.io/en/latest/api/pykeen.datasets.ILPC2022Small.html) | [Galkin *et al*., 2022](https://arxiv.org/abs/2203.01520) |
| FB15k-237 | [`pykeen.datasets.InductiveFB15k237`](https://pykeen.readthedocs.io/en/latest/api/pykeen.datasets.InductiveFB15k237.html) | [Teru *et al*., 2020](https://arxiv.org/abs/1911.06962) |
| NELL | [`pykeen.datasets.InductiveNELL`](https://pykeen.readthedocs.io/en/latest/api/pykeen.datasets.InductiveNELL.html) | [Teru *et al*., 2020](https://arxiv.org/abs/1911.06962) |
| WordNet-18 (RR) | [`pykeen.datasets.InductiveWN18RR`](https://pykeen.readthedocs.io/en/latest/api/pykeen.datasets.InductiveWN18RR.html) | [Teru *et al*., 2020](https://arxiv.org/abs/1911.06962) |
### Representations
The following 20 representations are implemented by PyKEEN.
| Name | Reference |
|----------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Backfill | [`pykeen.nn.BackfillRepresentation`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.representation.BackfillRepresentation.html) |
| Text Encoding | [`pykeen.nn.BiomedicalCURIERepresentation`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.representation.BiomedicalCURIERepresentation.html) |
| Combined | [`pykeen.nn.CombinedRepresentation`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.representation.CombinedRepresentation.html) |
| Embedding | [`pykeen.nn.Embedding`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.representation.Embedding.html) |
| Featurized Message Passing | [`pykeen.nn.FeaturizedMessagePassingRepresentation`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.pyg.FeaturizedMessagePassingRepresentation.html) |
| Low Rank Embedding | [`pykeen.nn.LowRankRepresentation`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.representation.LowRankRepresentation.html) |
| NodePiece | [`pykeen.nn.NodePieceRepresentation`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.node_piece.representation.NodePieceRepresentation.html) |
| Partition | [`pykeen.nn.PartitionRepresentation`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.representation.PartitionRepresentation.html) |
| R-GCN | [`pykeen.nn.RGCNRepresentation`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.message_passing.RGCNRepresentation.html) |
| Simple Message Passing | [`pykeen.nn.SimpleMessagePassingRepresentation`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.pyg.SimpleMessagePassingRepresentation.html) |
| CompGCN | [`pykeen.nn.SingleCompGCNRepresentation`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.representation.SingleCompGCNRepresentation.html) |
| Subset Representation | [`pykeen.nn.SubsetRepresentation`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.representation.SubsetRepresentation.html) |
| Tensor-Train | [`pykeen.nn.TensorTrainRepresentation`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.representation.TensorTrainRepresentation.html) |
| Text Encoding | [`pykeen.nn.TextRepresentation`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.representation.TextRepresentation.html) |
| Tokenization | [`pykeen.nn.TokenizationRepresentation`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.node_piece.representation.TokenizationRepresentation.html) |
| Transformed | [`pykeen.nn.TransformedRepresentation`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.representation.TransformedRepresentation.html) |
| Typed Message Passing | [`pykeen.nn.TypedMessagePassingRepresentation`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.pyg.TypedMessagePassingRepresentation.html) |
| Visual | [`pykeen.nn.VisualRepresentation`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.vision.representation.VisualRepresentation.html) |
| Wikidata Text Encoding | [`pykeen.nn.WikidataTextRepresentation`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.representation.WikidataTextRepresentation.html) |
| Wikidata Visual | [`pykeen.nn.WikidataVisualRepresentation`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.vision.representation.WikidataVisualRepresentation.html) |
### Interactions
The following 34 interactions are implemented by PyKEEN.
| Name | Reference | Citation |
|--------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------|
| AutoSF | [`pykeen.nn.AutoSFInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.modules.AutoSFInteraction.html) | [Zhang *et al.*, 2020](https://arxiv.org/abs/1904.11682) |
| BoxE | [`pykeen.nn.BoxEInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.modules.BoxEInteraction.html) | [Abboud *et al.*, 2020](https://arxiv.org/abs/2007.06267) |
| ComplEx | [`pykeen.nn.ComplExInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.modules.ComplExInteraction.html) | [Trouillon *et al.*, 2016](https://arxiv.org/abs/1606.06357) |
| ConvE | [`pykeen.nn.ConvEInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.modules.ConvEInteraction.html) | [Dettmers *et al.*, 2018](https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/17366) |
| ConvKB | [`pykeen.nn.ConvKBInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.modules.ConvKBInteraction.html) | [Nguyen *et al.*, 2018](https://www.aclweb.org/anthology/N18-2053) |
| Canonical Tensor Decomposition | [`pykeen.nn.CPInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.modules.CPInteraction.html) | [Lacroix *et al.*, 2018](https://arxiv.org/abs/1806.07297) |
| CrossE | [`pykeen.nn.CrossEInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.modules.CrossEInteraction.html) | [Zhang *et al.*, 2019](https://arxiv.org/abs/1903.04750) |
| DistMA | [`pykeen.nn.DistMAInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.modules.DistMAInteraction.html) | [Shi *et al.*, 2019](https://www.aclweb.org/anthology/D19-1075.pdf) |
| DistMult | [`pykeen.nn.DistMultInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.modules.DistMultInteraction.html) | [Yang *et al.*, 2014](https://arxiv.org/abs/1412.6575) |
| ER-MLP | [`pykeen.nn.ERMLPInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.modules.ERMLPInteraction.html) | [Dong *et al.*, 2014](https://storage.googleapis.com/pub-tools-public-publication-data/pdf/45634.pdf) |
| ER-MLP (E) | [`pykeen.nn.ERMLPEInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.modules.ERMLPEInteraction.html) | [Sharifzadeh *et al.*, 2019](https://github.com/pykeen/pykeen) |
| HolE | [`pykeen.nn.HolEInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.modules.HolEInteraction.html) | [Nickel *et al.*, 2016](https://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/viewFile/12484/11828) |
| KG2E | [`pykeen.nn.KG2EInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.modules.KG2EInteraction.html) | [He *et al.*, 2015](https://dl.acm.org/doi/10.1145/2806416.2806502) |
| LineaRE | [`pykeen.nn.LineaREInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.modules.LineaREInteraction.html) | [Peng *et al.*, 2020](https://arxiv.org/abs/2004.10037) |
| MultiLinearTucker | [`pykeen.nn.MultiLinearTuckerInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.modules.MultiLinearTuckerInteraction.html) | [Tucker *et al.*, 1966](https://dx.doi.org/10.1007/BF02289464) |
| MuRE | [`pykeen.nn.MuREInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.modules.MuREInteraction.html) | [Balažević *et al.*, 2019](https://arxiv.org/abs/1905.09791) |
| NTN | [`pykeen.nn.NTNInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.modules.NTNInteraction.html) | [Socher *et al.*, 2013](https://proceedings.neurips.cc/paper/2013/file/b337e84de8752b27eda3a12363109e80-Paper.pdf) |
| PairRE | [`pykeen.nn.PairREInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.modules.PairREInteraction.html) | [Chao *et al.*, 2020](http://arxiv.org/abs/2011.03798) |
| ProjE | [`pykeen.nn.ProjEInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.modules.ProjEInteraction.html) | [Shi *et al.*, 2017](https://aaai.org/papers/10677-aaai-31-2017/) |
| QuatE | [`pykeen.nn.QuatEInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.modules.QuatEInteraction.html) | [Zhang *et al.*, 2019](https://arxiv.org/abs/1904.10281) |
| RESCAL | [`pykeen.nn.RESCALInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.modules.RESCALInteraction.html) | [Nickel *et al.*, 2011](https://icml.cc/2011/papers/438_icmlpaper.pdf) |
| RotatE | [`pykeen.nn.RotatEInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.modules.RotatEInteraction.html) | [Sun *et al.*, 2019](https://arxiv.org/abs/1902.10197) |
| Structured Embedding | [`pykeen.nn.SEInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.modules.SEInteraction.html) | [Bordes *et al.*, 2011](https://www.aaai.org/ocs/index.php/AAAI/AAAI11/paper/download/3659/3898) |
| SimplE | [`pykeen.nn.SimplEInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.modules.SimplEInteraction.html) | [Kazemi *et al.*, 2018](https://papers.nips.cc/paper/7682-simple-embedding-for-link-prediction-in-knowledge-graphs) |
| TorusE | [`pykeen.nn.TorusEInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.modules.TorusEInteraction.html) | [Ebisu *et al.*, 2018](https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/16227) |
| TransD | [`pykeen.nn.TransDInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.modules.TransDInteraction.html) | [Ji *et al.*, 2015](http://www.aclweb.org/anthology/P15-1067) |
| TransE | [`pykeen.nn.TransEInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.modules.TransEInteraction.html) | [Bordes *et al.*, 2013](http://papers.nips.cc/paper/5071-translating-embeddings-for-modeling-multi-relational-data.pdf) |
| TransF | [`pykeen.nn.TransFInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.modules.TransFInteraction.html) | [Feng *et al.*, 2016](https://www.aaai.org/ocs/index.php/KR/KR16/paper/view/12887) |
| Transformer | [`pykeen.nn.TransformerInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.modules.TransformerInteraction.html) | [Galkin *et al.*, 2020](https://doi.org/10.18653/v1/2020.emnlp-main.596) |
| TransH | [`pykeen.nn.TransHInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.modules.TransHInteraction.html) | [Wang *et al.*, 2014](https://aaai.org/papers/8870-knowledge-graph-embedding-by-translating-on-hyperplanes/) |
| TransR | [`pykeen.nn.TransRInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.modules.TransRInteraction.html) | [Lin *et al.*, 2015](https://aaai.org/papers/9491-learning-entity-and-relation-embeddings-for-knowledge-graph-completion/) |
| TripleRE | [`pykeen.nn.TripleREInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.modules.TripleREInteraction.html) | [Yu *et al.*, 2021](https://vixra.org/abs/2112.0095) |
| TuckER | [`pykeen.nn.TuckERInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.modules.TuckERInteraction.html) | [Balažević *et al.*, 2019](https://arxiv.org/abs/1901.09590) |
| Unstructured Model | [`pykeen.nn.UMInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.modules.UMInteraction.html) | [Bordes *et al.*, 2014](https://link.springer.com/content/pdf/10.1007%2Fs10994-013-5363-6.pdf) |
### Models
The following 40 models are implemented by PyKEEN.
| Name | Model | Citation |
|--------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------|
| AutoSF | [`pykeen.models.AutoSF`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.unimodal.auto_sf.AutoSF.html) | [Zhang *et al.*, 2020](https://arxiv.org/abs/1904.11682) |
| BoxE | [`pykeen.models.BoxE`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.unimodal.boxe.BoxE.html) | [Abboud *et al.*, 2020](https://arxiv.org/abs/2007.06267) |
| Canonical Tensor Decomposition | [`pykeen.models.CP`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.unimodal.cp.CP.html) | [Lacroix *et al.*, 2018](https://arxiv.org/abs/1806.07297) |
| CompGCN | [`pykeen.models.CompGCN`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.unimodal.compgcn.CompGCN.html) | [Vashishth *et al.*, 2020](https://arxiv.org/pdf/1911.03082) |
| ComplEx | [`pykeen.models.ComplEx`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.unimodal.complex.ComplEx.html) | [Trouillon *et al.*, 2016](https://arxiv.org/abs/1606.06357) |
| ComplEx Literal | [`pykeen.models.ComplExLiteral`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.multimodal.complex_literal.ComplExLiteral.html) | [Kristiadi *et al.*, 2018](https://arxiv.org/abs/1802.00934) |
| ConvE | [`pykeen.models.ConvE`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.unimodal.conv_e.ConvE.html) | [Dettmers *et al.*, 2018](https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/17366) |
| ConvKB | [`pykeen.models.ConvKB`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.unimodal.conv_kb.ConvKB.html) | [Nguyen *et al.*, 2018](https://www.aclweb.org/anthology/N18-2053) |
| CooccurrenceFiltered | [`pykeen.models.CooccurrenceFilteredModel`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.meta.filtered.CooccurrenceFilteredModel.html) | [Berrendorf *et al.*, 2022](https://github.com/pykeen/pykeen/pull/943) |
| CrossE | [`pykeen.models.CrossE`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.unimodal.crosse.CrossE.html) | [Zhang *et al.*, 2019](https://arxiv.org/abs/1903.04750) |
| DistMA | [`pykeen.models.DistMA`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.unimodal.distma.DistMA.html) | [Shi *et al.*, 2019](https://www.aclweb.org/anthology/D19-1075.pdf) |
| DistMult | [`pykeen.models.DistMult`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.unimodal.distmult.DistMult.html) | [Yang *et al.*, 2014](https://arxiv.org/abs/1412.6575) |
| DistMult Literal | [`pykeen.models.DistMultLiteral`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.multimodal.distmult_literal.DistMultLiteral.html) | [Kristiadi *et al.*, 2018](https://arxiv.org/abs/1802.00934) |
| DistMult Literal (Gated) | [`pykeen.models.DistMultLiteralGated`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.multimodal.distmult_literal_gated.DistMultLiteralGated.html) | [Kristiadi *et al.*, 2018](https://arxiv.org/abs/1802.00934) |
| ER-MLP | [`pykeen.models.ERMLP`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.unimodal.ermlp.ERMLP.html) | [Dong *et al.*, 2014](https://dl.acm.org/citation.cfm?id=2623623) |
| ER-MLP (E) | [`pykeen.models.ERMLPE`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.unimodal.ermlpe.ERMLPE.html) | [Sharifzadeh *et al.*, 2019](https://github.com/pykeen/pykeen) |
| Fixed Model | [`pykeen.models.FixedModel`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.mocks.FixedModel.html) | [Berrendorf *et al.*, 2021](https://github.com/pykeen/pykeen/pull/691) |
| HolE | [`pykeen.models.HolE`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.unimodal.hole.HolE.html) | [Nickel *et al.*, 2016](https://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/viewFile/12484/11828) |
| InductiveNodePiece | [`pykeen.models.InductiveNodePiece`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.inductive.inductive_nodepiece.InductiveNodePiece.html) | [Galkin *et al.*, 2021](https://arxiv.org/abs/2106.12144) |
| InductiveNodePieceGNN | [`pykeen.models.InductiveNodePieceGNN`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.inductive.inductive_nodepiece_gnn.InductiveNodePieceGNN.html) | [Galkin *et al.*, 2021](https://arxiv.org/abs/2106.12144) |
| KG2E | [`pykeen.models.KG2E`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.unimodal.kg2e.KG2E.html) | [He *et al.*, 2015](https://dl.acm.org/doi/10.1145/2806416.2806502) |
| MuRE | [`pykeen.models.MuRE`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.unimodal.mure.MuRE.html) | [Balažević *et al.*, 2019](https://arxiv.org/abs/1905.09791) |
| NTN | [`pykeen.models.NTN`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.unimodal.ntn.NTN.html) | [Socher *et al.*, 2013](https://dl.acm.org/doi/10.5555/2999611.2999715) |
| NodePiece | [`pykeen.models.NodePiece`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.unimodal.node_piece.NodePiece.html) | [Galkin *et al.*, 2021](https://arxiv.org/abs/2106.12144) |
| PairRE | [`pykeen.models.PairRE`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.unimodal.pair_re.PairRE.html) | [Chao *et al.*, 2020](http://arxiv.org/abs/2011.03798) |
| ProjE | [`pykeen.models.ProjE`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.unimodal.proj_e.ProjE.html) | [Shi *et al.*, 2017](https://www.aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14279) |
| QuatE | [`pykeen.models.QuatE`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.unimodal.quate.QuatE.html) | [Zhang *et al.*, 2019](https://arxiv.org/abs/1904.10281) |
| R-GCN | [`pykeen.models.RGCN`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.unimodal.rgcn.RGCN.html) | [Schlichtkrull *et al.*, 2018](https://arxiv.org/pdf/1703.06103) |
| RESCAL | [`pykeen.models.RESCAL`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.unimodal.rescal.RESCAL.html) | [Nickel *et al.*, 2011](http://www.cip.ifi.lmu.de/~nickel/data/paper-icml2011.pdf) |
| RotatE | [`pykeen.models.RotatE`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.unimodal.rotate.RotatE.html) | [Sun *et al.*, 2019](https://arxiv.org/abs/1902.10197v1) |
| SimplE | [`pykeen.models.SimplE`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.unimodal.simple.SimplE.html) | [Kazemi *et al.*, 2018](https://papers.nips.cc/paper/7682-simple-embedding-for-link-prediction-in-knowledge-graphs) |
| Structured Embedding | [`pykeen.models.SE`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.unimodal.structured_embedding.SE.html) | [Bordes *et al.*, 2011](https://www.aaai.org/ocs/index.php/AAAI/AAAI11/paper/download/3659/3898) |
| TorusE | [`pykeen.models.TorusE`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.unimodal.toruse.TorusE.html) | [Ebisu *et al.*, 2018](https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/16227) |
| TransD | [`pykeen.models.TransD`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.unimodal.trans_d.TransD.html) | [Ji *et al.*, 2015](http://www.aclweb.org/anthology/P15-1067) |
| TransE | [`pykeen.models.TransE`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.unimodal.trans_e.TransE.html) | [Bordes *et al.*, 2013](http://papers.nips.cc/paper/5071-translating-embeddings-for-modeling-multi-relational-data.pdf) |
| TransF | [`pykeen.models.TransF`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.unimodal.trans_f.TransF.html) | [Feng *et al.*, 2016](https://www.aaai.org/ocs/index.php/KR/KR16/paper/view/12887) |
| TransH | [`pykeen.models.TransH`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.unimodal.trans_h.TransH.html) | [Wang *et al.*, 2014](https://aaai.org/papers/8870-knowledge-graph-embedding-by-translating-on-hyperplanes/) |
| TransR | [`pykeen.models.TransR`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.unimodal.trans_r.TransR.html) | [Lin *et al.*, 2015](http://www.aaai.org/ocs/index.php/AAAI/AAAI15/paper/download/9571/9523/) |
| TuckER | [`pykeen.models.TuckER`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.unimodal.tucker.TuckER.html) | [Balažević *et al.*, 2019](https://arxiv.org/abs/1901.09590) |
| Unstructured Model | [`pykeen.models.UM`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.unimodal.unstructured_model.UM.html) | [Bordes *et al.*, 2014](https://link.springer.com/content/pdf/10.1007%2Fs10994-013-5363-6.pdf) |
### Losses
The following 15 losses are implemented by PyKEEN.
| Name | Reference | Description |
|-----------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------|
| Adversarially weighted binary cross entropy (with logits) | [`pykeen.losses.AdversarialBCEWithLogitsLoss`](https://pykeen.readthedocs.io/en/latest/api/pykeen.losses.AdversarialBCEWithLogitsLoss.html) | An adversarially weighted BCE loss. |
| Binary cross entropy (after sigmoid) | [`pykeen.losses.BCEAfterSigmoidLoss`](https://pykeen.readthedocs.io/en/latest/api/pykeen.losses.BCEAfterSigmoidLoss.html) | The numerically unstable version of explicit Sigmoid + BCE loss. |
| Binary cross entropy (with logits) | [`pykeen.losses.BCEWithLogitsLoss`](https://pykeen.readthedocs.io/en/latest/api/pykeen.losses.BCEWithLogitsLoss.html) | The binary cross entropy loss. |
| Cross entropy | [`pykeen.losses.CrossEntropyLoss`](https://pykeen.readthedocs.io/en/latest/api/pykeen.losses.CrossEntropyLoss.html) | The cross entropy loss that evaluates the cross entropy after softmax output. |
| Double Margin | [`pykeen.losses.DoubleMarginLoss`](https://pykeen.readthedocs.io/en/latest/api/pykeen.losses.DoubleMarginLoss.html) | A limit-based scoring loss, with separate margins for positive and negative elements from [sun2018]_. |
| Focal | [`pykeen.losses.FocalLoss`](https://pykeen.readthedocs.io/en/latest/api/pykeen.losses.FocalLoss.html) | The focal loss proposed by [lin2018]_. |
| InfoNCE loss with additive margin | [`pykeen.losses.InfoNCELoss`](https://pykeen.readthedocs.io/en/latest/api/pykeen.losses.InfoNCELoss.html) | The InfoNCE loss with additive margin proposed by [wang2022]_. |
| Margin ranking | [`pykeen.losses.MarginRankingLoss`](https://pykeen.readthedocs.io/en/latest/api/pykeen.losses.MarginRankingLoss.html) | The pairwise hinge loss (i.e., margin ranking loss). |
| Mean squared error | [`pykeen.losses.MSELoss`](https://pykeen.readthedocs.io/en/latest/api/pykeen.losses.MSELoss.html) | The mean squared error loss. |
| Self-adversarial negative sampling | [`pykeen.losses.NSSALoss`](https://pykeen.readthedocs.io/en/latest/api/pykeen.losses.NSSALoss.html) | The self-adversarial negative sampling loss function proposed by [sun2019]_. |
| Pairwise logistic | [`pykeen.losses.PairwiseLogisticLoss`](https://pykeen.readthedocs.io/en/latest/api/pykeen.losses.PairwiseLogisticLoss.html) | The pairwise logistic loss. |
| Pointwise Hinge | [`pykeen.losses.PointwiseHingeLoss`](https://pykeen.readthedocs.io/en/latest/api/pykeen.losses.PointwiseHingeLoss.html) | The pointwise hinge loss. |
| Soft margin ranking | [`pykeen.losses.SoftMarginRankingLoss`](https://pykeen.readthedocs.io/en/latest/api/pykeen.losses.SoftMarginRankingLoss.html) | The soft pairwise hinge loss (i.e., soft margin ranking loss). |
| Softplus | [`pykeen.losses.SoftplusLoss`](https://pykeen.readthedocs.io/en/latest/api/pykeen.losses.SoftplusLoss.html) | The pointwise logistic loss (i.e., softplus loss). |
| Soft Pointwise Hinge | [`pykeen.losses.SoftPointwiseHingeLoss`](https://pykeen.readthedocs.io/en/latest/api/pykeen.losses.SoftPointwiseHingeLoss.html) | The soft pointwise hinge loss. |
### Regularizers
The following 6 regularizers are implemented by PyKEEN.
| Name | Reference | Description |
|---------------|-------------------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------|
| combined | [`pykeen.regularizers.CombinedRegularizer`](https://pykeen.readthedocs.io/en/latest/api/pykeen.regularizers.CombinedRegularizer.html) | A convex combination of regularizers. |
| lp | [`pykeen.regularizers.LpRegularizer`](https://pykeen.readthedocs.io/en/latest/api/pykeen.regularizers.LpRegularizer.html) | A simple L_p norm based regularizer. |
| no | [`pykeen.regularizers.NoRegularizer`](https://pykeen.readthedocs.io/en/latest/api/pykeen.regularizers.NoRegularizer.html) | A regularizer which does not perform any regularization. |
| normlimit | [`pykeen.regularizers.NormLimitRegularizer`](https://pykeen.readthedocs.io/en/latest/api/pykeen.regularizers.NormLimitRegularizer.html) | A regularizer which formulates a soft constraint on a maximum norm. |
| orthogonality | [`pykeen.regularizers.OrthogonalityRegularizer`](https://pykeen.readthedocs.io/en/latest/api/pykeen.regularizers.OrthogonalityRegularizer.html) | A regularizer for the soft orthogonality constraints from [wang2014]_. |
| powersum | [`pykeen.regularizers.PowerSumRegularizer`](https://pykeen.readthedocs.io/en/latest/api/pykeen.regularizers.PowerSumRegularizer.html) | A simple x^p based regularizer. |
### Training Loops
The following 3 training loops are implemented in PyKEEN.
| Name | Reference | Description |
|---------------|----------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------|
| lcwa | [`pykeen.training.LCWATrainingLoop`](https://pykeen.readthedocs.io/en/latest/reference/training.html#pykeen.training.LCWATrainingLoop) | A training loop that is based upon the local closed world assumption (LCWA). |
| slcwa | [`pykeen.training.SLCWATrainingLoop`](https://pykeen.readthedocs.io/en/latest/reference/training.html#pykeen.training.SLCWATrainingLoop) | A training loop that uses the stochastic local closed world assumption training approach. |
| symmetriclcwa | [`pykeen.training.SymmetricLCWATrainingLoop`](https://pykeen.readthedocs.io/en/latest/reference/training.html#pykeen.training.SymmetricLCWATrainingLoop) | A "symmetric" LCWA scoring heads *and* tails at once. |
### Negative Samplers
The following 3 negative samplers are implemented in PyKEEN.
| Name | Reference | Description |
|-------------|---------------------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------|
| basic | [`pykeen.sampling.BasicNegativeSampler`](https://pykeen.readthedocs.io/en/latest/api/pykeen.sampling.BasicNegativeSampler.html) | A basic negative sampler. |
| bernoulli | [`pykeen.sampling.BernoulliNegativeSampler`](https://pykeen.readthedocs.io/en/latest/api/pykeen.sampling.BernoulliNegativeSampler.html) | An implementation of the Bernoulli negative sampling approach proposed by [wang2014]_. |
| pseudotyped | [`pykeen.sampling.PseudoTypedNegativeSampler`](https://pykeen.readthedocs.io/en/latest/api/pykeen.sampling.PseudoTypedNegativeSampler.html) | A sampler that accounts for which entities co-occur with a relation. |
### Stoppers
The following 2 stoppers are implemented in PyKEEN.
| Name | Reference | Description |
|--------|--------------------------------------------------------------------------------------------------------------------------------|-------------------------------|
| early | [`pykeen.stoppers.EarlyStopper`](https://pykeen.readthedocs.io/en/latest/reference/stoppers.html#pykeen.stoppers.EarlyStopper) | A harness for early stopping. |
| nop | [`pykeen.stoppers.NopStopper`](https://pykeen.readthedocs.io/en/latest/reference/stoppers.html#pykeen.stoppers.NopStopper) | A stopper that does nothing. |
### Evaluators
The following 5 evaluators are implemented in PyKEEN.
| Name | Reference | Description |
|------------------|-----------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------|
| classification | [`pykeen.evaluation.ClassificationEvaluator`](https://pykeen.readthedocs.io/en/latest/api/pykeen.evaluation.ClassificationEvaluator.html) | An evaluator that uses a classification metrics. |
| macrorankbased | [`pykeen.evaluation.MacroRankBasedEvaluator`](https://pykeen.readthedocs.io/en/latest/api/pykeen.evaluation.MacroRankBasedEvaluator.html) | Macro-average rank-based evaluation. |
| ogb | [`pykeen.evaluation.OGBEvaluator`](https://pykeen.readthedocs.io/en/latest/api/pykeen.evaluation.OGBEvaluator.html) | A sampled, rank-based evaluator that applies a custom OGB evaluation. |
| rankbased | [`pykeen.evaluation.RankBasedEvaluator`](https://pykeen.readthedocs.io/en/latest/api/pykeen.evaluation.RankBasedEvaluator.html) | A rank-based evaluator for KGE models. |
| sampledrankbased | [`pykeen.evaluation.SampledRankBasedEvaluator`](https://pykeen.readthedocs.io/en/latest/api/pykeen.evaluation.SampledRankBasedEvaluator.html) | A rank-based evaluator using sampled negatives instead of all negatives. |
### Metrics
The following 44 metrics are implemented in PyKEEN.
| Name | Interval | Direction | Description | Type |
|------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------|-------------|-------------------------------------------------------------------------|----------------|
| [Accuracy](https://en.wikipedia.org/wiki/Evaluation_of_binary_classifiers#Single_metrics) | $[0, 1]$ | 📈 | The ratio of the number of correct classifications to the total number. | Classification |
| [Area Under The Receiver Operating Characteristic Curve](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_auc_score.html) | $[0, 1]$ | 📈 | The area under the receiver operating characteristic curve. | Classification |
| [Average Precision Score](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.average_precision_score.html) | $[0, 1]$ | 📈 | The average precision across different thresholds. | Classification |
| [Balanced Accuracy Score](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.balanced_accuracy_score.html) | $[0, 1]$ | 📈 | The average of recall obtained on each class. | Classification |
| [Diagnostic Odds Ratio](https://en.wikipedia.org/wiki/Diagnostic_odds_ratio) | $[0, ∞)$ | 📈 | The ratio of positive and negative likelihood ratio. | Classification |
| [F1 Score](https://en.wikipedia.org/wiki/F1_score) | $[0, 1]$ | 📈 | The harmonic mean of precision and recall. | Classification |
| [False Discovery Rate](https://en.wikipedia.org/wiki/False_discovery_rate) | $[0, 1]$ | 📉 | The proportion of predicted negatives which are true positive. | Classification |
| [False Negative Rate](https://en.wikipedia.org/wiki/Type_I_and_type_II_errors#False_positive_and_false_negative_rates) | $[0, 1]$ | 📉 | The probability that a truly positive triple is predicted negative. | Classification |
| [False Omission Rate](https://en.wikipedia.org/wiki/Positive_and_negative_predictive_values) | $[0, 1]$ | 📉 | The proportion of predicted positives which are true negative. | Classification |
| [False Positive Rate](https://en.wikipedia.org/wiki/False_positive_rate) | $[0, 1]$ | 📉 | The probability that a truly negative triple is predicted positive. | Classification |
| [Fowlkes Mallows Index](https://en.wikipedia.org/wiki/Fowlkes%E2%80%93Mallows_index) | $[0, 1]$ | 📈 | The Fowlkes Mallows index. | Classification |
| [Informedness](https://en.wikipedia.org/wiki/Informedness) | $[-1, 1]$ | 📈 | The informedness metric. | Classification |
| [Matthews Correlation Coefficient](https://en.wikipedia.org/wiki/Phi_coefficient) | $[-1, 1]$ | 📈 | The Matthews Correlation Coefficient (MCC). | Classification |
| [Negative Likelihood Ratio](https://en.wikipedia.org/wiki/Negative_likelihood_ratio) | $[0, ∞)$ | 📉 | The ratio of false positive rate to true positive rate. | Classification |
| [Negative Predictive Value](https://en.wikipedia.org/wiki/Negative_predictive_value) | $[0, 1]$ | 📈 | The proportion of predicted negatives which are true negatives. | Classification |
| [Number of Scores](https://pykeen.readthedocs.io/en/stable/reference/evaluation.html) | $[0, ∞)$ | 📈 | The number of scores. | Classification |
| [Positive Likelihood Ratio](https://en.wikipedia.org/wiki/Positive_likelihood_ratio) | $[0, ∞)$ | 📈 | The ratio of true positive rate to false positive rate. | Classification |
| [Positive Predictive Value](https://en.wikipedia.org/wiki/Positive_predictive_value) | $[0, 1]$ | 📈 | The proportion of predicted positives which are true positive. | Classification |
| [Prevalence Threshold](https://en.wikipedia.org/wiki/Prevalence_threshold) | $[0, ∞)$ | 📉 | The prevalence threshold. | Classification |
| [Threat Score](https://en.wikipedia.org/wiki/Sensitivity_and_specificity) | $[0, 1]$ | 📈 | The harmonic mean of precision and recall. | Classification |
| [True Negative Rate](https://en.wikipedia.org/wiki/Specificity_(tests)) | $[0, 1]$ | 📈 | The probability that a truly false triple is predicted negative. | Classification |
| [True Positive Rate](https://en.wikipedia.org/wiki/Sensitivity_(test)) | $[0, 1]$ | 📈 | The probability that a truly positive triple is predicted positive. | Classification |
| [Adjusted Arithmetic Mean Rank (AAMR)](https://arxiv.org/abs/2002.06914) | $[0, 2)$ | 📉 | The mean over all ranks divided by its expected value. | Ranking |
| [Adjusted Arithmetic Mean Rank Index (AAMRI)](https://arxiv.org/abs/2002.06914) | $[-1, 1]$ | 📈 | The re-indexed adjusted mean rank (AAMR) | Ranking |
| [Adjusted Geometric Mean Rank Index (AGMRI)](https://arxiv.org/abs/2002.06914) | $(\frac{-E[f]}{1-E[f]}, 1]$ | 📈 | The re-indexed adjusted geometric mean rank (AGMRI) | Ranking |
| [Adjusted Hits at K](https://arxiv.org/abs/2203.07544) | $(\frac{-E[f]}{1-E[f]}, 1]$ | 📈 | The re-indexed adjusted hits at K | Ranking |
| [Adjusted Inverse Harmonic Mean Rank](https://arxiv.org/abs/2203.07544) | $(\frac{-E[f]}{1-E[f]}, 1]$ | 📈 | The re-indexed adjusted MRR | Ranking |
| [Geometric Mean Rank (GMR)](https://arxiv.org/abs/2203.07544) | $[1, ∞)$ | 📉 | The geometric mean over all ranks. | Ranking |
| [Harmonic Mean Rank (HMR)](https://arxiv.org/abs/2203.07544) | $[1, ∞)$ | 📉 | The harmonic mean over all ranks. | Ranking |
| [Hits @ K](https://pykeen.readthedocs.io/en/stable/tutorial/understanding_evaluation.html#hits-k) | $[0, 1]$ | 📈 | The relative frequency of ranks not larger than a given k. | Ranking |
| [Inverse Arithmetic Mean Rank (IAMR)](https://arxiv.org/abs/2203.07544) | $(0, 1]$ | 📈 | The inverse of the arithmetic mean over all ranks. | Ranking |
| [Inverse Geometric Mean Rank (IGMR)](https://arxiv.org/abs/2203.07544) | $(0, 1]$ | 📈 | The inverse of the geometric mean over all ranks. | Ranking |
| [Inverse Median Rank](https://arxiv.org/abs/2203.07544) | $(0, 1]$ | 📈 | The inverse of the median over all ranks. | Ranking |
| [Mean Rank (MR)](https://pykeen.readthedocs.io/en/stable/tutorial/understanding_evaluation.html#mean-rank) | $[1, ∞)$ | 📉 | The arithmetic mean over all ranks. | Ranking |
| [Mean Reciprocal Rank (MRR)](https://en.wikipedia.org/wiki/Mean_reciprocal_rank) | $(0, 1]$ | 📈 | The inverse of the harmonic mean over all ranks. | Ranking |
| [Median Rank](https://arxiv.org/abs/2203.07544) | $[1, ∞)$ | 📉 | The median over all ranks. | Ranking |
| [z-Geometric Mean Rank (zGMR)](https://arxiv.org/abs/2203.07544) | $(-∞, ∞)$ | 📈 | The z-scored geometric mean rank | Ranking |
| [z-Hits at K](https://arxiv.org/abs/2203.07544) | $(-∞, ∞)$ | 📈 | The z-scored hits at K | Ranking |
| [z-Mean Rank (zMR)](https://arxiv.org/abs/2203.07544) | $(-∞, ∞)$ | 📈 | The z-scored mean rank | Ranking |
| [z-Mean Reciprocal Rank (zMRR)](https://arxiv.org/abs/2203.07544) | $(-∞, ∞)$ | 📈 | The z-scored mean reciprocal rank | Ranking |
### Trackers
The following 8 trackers are implemented in PyKEEN.
| Name | Reference | Description |
|-------------|-----------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------|
| console | [`pykeen.trackers.ConsoleResultTracker`](https://pykeen.readthedocs.io/en/latest/api/pykeen.trackers.ConsoleResultTracker.html) | A class that directly prints to console. |
| csv | [`pykeen.trackers.CSVResultTracker`](https://pykeen.readthedocs.io/en/latest/api/pykeen.trackers.CSVResultTracker.html) | Tracking results to a CSV file. |
| json | [`pykeen.trackers.JSONResultTracker`](https://pykeen.readthedocs.io/en/latest/api/pykeen.trackers.JSONResultTracker.html) | Tracking results to a JSON lines file. |
| mlflow | [`pykeen.trackers.MLFlowResultTracker`](https://pykeen.readthedocs.io/en/latest/api/pykeen.trackers.MLFlowResultTracker.html) | A tracker for MLflow. |
| neptune | [`pykeen.trackers.NeptuneResultTracker`](https://pykeen.readthedocs.io/en/latest/api/pykeen.trackers.NeptuneResultTracker.html) | A tracker for Neptune.ai. |
| python | [`pykeen.trackers.PythonResultTracker`](https://pykeen.readthedocs.io/en/latest/api/pykeen.trackers.PythonResultTracker.html) | A tracker which stores everything in Python dictionaries. |
| tensorboard | [`pykeen.trackers.TensorBoardResultTracker`](https://pykeen.readthedocs.io/en/latest/api/pykeen.trackers.TensorBoardResultTracker.html) | A tracker for TensorBoard. |
| wandb | [`pykeen.trackers.WANDBResultTracker`](https://pykeen.readthedocs.io/en/latest/api/pykeen.trackers.WANDBResultTracker.html) | A tracker for Weights and Biases. |
## Experimentation
### Reproduction
PyKEEN includes a set of curated experimental settings for reproducing past landmark
experiments. They can be accessed and run like:
```shell
pykeen experiments reproduce tucker balazevic2019 fb15k
```
Where the three arguments are the model name, the reference, and the dataset.
The output directory can be optionally set with `-d`.
### Ablation
PyKEEN includes the ability to specify ablation studies using the
hyper-parameter optimization module. They can be run like:
```shell
pykeen experiments ablation ~/path/to/config.json
```
### Large-scale Reproducibility and Benchmarking Study
We used PyKEEN to perform a large-scale reproducibility and benchmarking study which are described in
[our article](https://doi.org/10.1109/TPAMI.2021.3124805):
```bibtex
@article{ali2020benchmarking,
author={Ali, Mehdi and Berrendorf, Max and Hoyt, Charles Tapley and Vermue, Laurent and Galkin, Mikhail and Sharifzadeh, Sahand and Fischer, Asja and Tresp, Volker and Lehmann, Jens},
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
title={Bringing Light Into the Dark: A Large-scale Evaluation of Knowledge Graph Embedding Models under a Unified Framework},
year={2021},
pages={1-1},
doi={10.1109/TPAMI.2021.3124805}}
}
```
We have made all code, experimental configurations, results, and analyses that lead to our interpretations available
at https://github.com/pykeen/benchmarking.
## Contributing
Contributions, whether filing an issue, making a pull request, or forking, are appreciated.
See [CONTRIBUTING.md](/CONTRIBUTING.md) for more information on getting involved.
If you have questions, please use the GitHub discussions feature at
https://github.com/pykeen/pykeen/discussions/new.
## Acknowledgements
### Supporters
This project has been supported by several organizations (in alphabetical order):
- [Bayer](https://www.bayer.com/)
- [CoronaWhy](https://www.coronawhy.org/)
- [Enveda Biosciences](https://www.envedabio.com/)
- [Fraunhofer Institute for Algorithms and Scientific Computing](https://www.scai.fraunhofer.de)
- [Fraunhofer Institute for Intelligent Analysis and Information Systems](https://www.iais.fraunhofer.de)
- [Fraunhofer Center for Machine Learning](https://www.cit.fraunhofer.de/de/zentren/maschinelles-lernen.html)
- [Harvard Program in Therapeutic Science - Laboratory of Systems Pharmacology](https://hits.harvard.edu/the-program/laboratory-of-systems-pharmacology/)
- [Ludwig-Maximilians-Universität München](https://www.en.uni-muenchen.de/index.html)
- [Munich Center for Machine Learning (MCML)](https://mcml.ai/)
- [Siemens](https://new.siemens.com/global/en.html)
- [Smart Data Analytics Research Group (University of Bonn & Fraunhofer IAIS)](https://sda.tech)
- [Technical University of Denmark - DTU Compute - Section for Cognitive Systems](https://www.compute.dtu.dk/english/research/research-sections/cogsys)
- [Technical University of Denmark - DTU Compute - Section for Statistics and Data Analysis](https://www.compute.dtu.dk/english/research/research-sections/stat)
- [University of Bonn](https://www.uni-bonn.de/)
### Funding
The development of PyKEEN has been funded by the following grants:
| Funding Body | Program | Grant |
|----------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------|-----------------|
| DARPA | [Young Faculty Award (PI: Benjamin Gyori)](https://indralab.github.io/#projects) | W911NF2010255 |
| DARPA | [Automating Scientific Knowledge Extraction (ASKE)](https://www.darpa.mil/program/automating-scientific-knowledge-extraction) | HR00111990009 |
| German Federal Ministry of Education and Research (BMBF) | [Maschinelles Lernen mit Wissensgraphen (MLWin)](https://mlwin.de) | 01IS18050D |
| German Federal Ministry of Education and Research (BMBF) | [Munich Center for Machine Learning (MCML)](https://mcml.ai) | 01IS18036A |
| Innovation Fund Denmark (Innovationsfonden) | [Danish Center for Big Data Analytics driven Innovation (DABAI)](https://dabai.dk) | Grand Solutions |
### Logo
The PyKEEN logo was designed by [Carina Steinborn](https://www.xing.com/profile/Carina_Steinborn2)
## Citation
If you have found PyKEEN useful in your work, please consider citing
[our article](http://jmlr.org/papers/v22/20-825.html):
```bibtex
@article{ali2021pykeen,
author = {Ali, Mehdi and Berrendorf, Max and Hoyt, Charles Tapley and Vermue, Laurent and Sharifzadeh, Sahand and Tresp, Volker and Lehmann, Jens},
journal = {Journal of Machine Learning Research},
number = {82},
pages = {1--6},
title = {{PyKEEN 1.0: A Python Library for Training and Evaluating Knowledge Graph Embeddings}},
url = {http://jmlr.org/papers/v22/20-825.html},
volume = {22},
year = {2021}
}
```
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"description": "<p align=\"center\">\n <img src=\"docs/source/logo.png\" height=\"150\">\n</p>\n\n<h1 align=\"center\">\n PyKEEN\n</h1>\n\n<p align=\"center\">\n <a href=\"https://github.com/pykeen/pykeen/actions/workflows/common.yml\">\n <img src=\"https://github.com/pykeen/pykeen/actions/workflows/common.yml/badge.svg\"\n alt=\"GitHub Actions\">\n </a>\n\n <a href='https://opensource.org/licenses/MIT'>\n <img src='https://img.shields.io/badge/License-MIT-blue.svg' alt='License'/>\n </a>\n\n <a href=\"https://zenodo.org/badge/latestdoi/242672435\">\n <img src=\"https://zenodo.org/badge/242672435.svg\" alt=\"DOI\">\n </a>\n\n <a href=\"https://optuna.org\">\n <img src=\"https://img.shields.io/badge/Optuna-integrated-blue\" alt=\"Optuna integrated\" height=\"20\">\n </a>\n\n <a href=\"https://pytorchlightning.ai\">\n <img src=\"https://img.shields.io/badge/-Lightning-792ee5?logo=pytorchlightning&logoColor=white\" alt=\"PyTorch Lightning\">\n </a>\n\n <a href=\"https://github.com/astral-sh/ruff\">\n <img src=\"https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json\" alt=\"Ruff\" style=\"max-width:100%;\">\n </a>\n\n <a href=\".github/CODE_OF_CONDUCT.md\">\n <img src=\"https://img.shields.io/badge/Contributor%20Covenant-2.1-4baaaa.svg\" alt=\"Contributor Covenant\">\n </a>\n</p>\n\n<p align=\"center\">\n <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\n train and evaluate knowledge graph embedding models (incorporating multi-modal information).\n</p>\n\n<p align=\"center\">\n <a href=\"#installation\">Installation</a> \u2022\n <a href=\"#quickstart\">Quickstart</a> \u2022\n <a href=\"#datasets\">Datasets (37)</a> \u2022\n <a href=\"#inductive-datasets\">Inductive Datasets (5)</a> \u2022\n <a href=\"#models\">Models (40)</a> \u2022\n <a href=\"#supporters\">Support</a> \u2022\n <a href=\"#citation\">Citation</a>\n</p>\n\n## Installation  \n\nThe latest stable version of PyKEEN requires Python 3.9+. It can be downloaded\nand installed from [PyPI](https://pypi.org/project/pykeen) with:\n\n```shell\npip install pykeen\n```\n\nThe latest version of PyKEEN can be installed directly from the\nsource code on [GitHub](https://github.com/pykeen/pykeen) with:\n\n```shell\npip install git+https://github.com/pykeen/pykeen.git\n```\n\nMore information about installation (e.g., development mode, Windows installation, Colab, Kaggle, extras)\ncan be found in the [installation documentation](https://pykeen.readthedocs.io/en/latest/installation.html).\n\n## Quickstart [](https://pykeen.readthedocs.io/en/latest/?badge=latest)\n\nThis example shows how to train a model on a dataset and test on another dataset.\n\nThe fastest way to get up and running is to use the pipeline function. It\nprovides a high-level entry into the extensible functionality of this package.\nThe following example shows how to train and evaluate the [TransE](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.TransE.html#pykeen.models.TransE)\nmodel on the [Nations](https://pykeen.readthedocs.io/en/latest/api/pykeen.datasets.Nations.html#pykeen.datasets.Nations)\ndataset. 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)\ntraining approach and evaluates with [rank-based evaluation](https://pykeen.readthedocs.io/en/latest/reference/evaluation/rank_based.html#pykeen.evaluation.RankBasedEvaluator).\n\n```python\nfrom pykeen.pipeline import pipeline\n\nresult = pipeline(\n model='TransE',\n dataset='nations',\n)\n```\n\nThe results are returned in an instance of the [PipelineResult](https://pykeen.readthedocs.io/en/latest/reference/pipeline.html#pykeen.pipeline.PipelineResult)\ndataclass that has attributes for the trained model, the training loop, the evaluation, and more. See the tutorials\non [using your own dataset](https://pykeen.readthedocs.io/en/latest/byo/data.html),\n[understanding the evaluation](https://pykeen.readthedocs.io/en/latest/tutorial/understanding_evaluation.html),\nand [making novel link predictions](https://pykeen.readthedocs.io/en/latest/tutorial/making_predictions.html).\n\nPyKEEN is extensible such that:\n\n- Each model has the same API, so anything from ``pykeen.models`` can be dropped in\n- Each training loop has the same API, so ``pykeen.training.LCWATrainingLoop`` can be dropped in\n- Triples factories can be generated by the user with ``from pykeen.triples.TriplesFactory``\n\nThe full documentation can be found at https://pykeen.readthedocs.io.\n\n## Implementation\n\nBelow are the models, datasets, training modes, evaluators, and metrics implemented\nin ``pykeen``.\n\n### Datasets \n\nThe following 37 datasets are built in to PyKEEN. The citation for each dataset corresponds to either the paper\ndescribing the dataset, the first paper published using the dataset with knowledge graph embedding models,\nor the URL for the dataset if neither of the first two are available. If you want to use a custom dataset,\nsee the [Bring Your Own Dataset](https://pykeen.readthedocs.io/en/latest/byo/data.html) tutorial. If you\nhave a suggestion for another dataset to include in PyKEEN, please let us know\n[here](https://github.com/pykeen/pykeen/issues/new?assignees=cthoyt&labels=New+Dataset&template=dataset-request.md&title=Add+%5BDATASET+NAME%5D).\n\n| Name | Documentation | Citation | Entities | Relations | Triples |\n|------------------------------------|---------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------|------------|-------------|-----------|\n| Aristo-v4 | [`pykeen.datasets.AristoV4`](https://pykeen.readthedocs.io/en/latest/api/pykeen.datasets.AristoV4.html) | [Chen *et al*., 2021](https://openreview.net/pdf?id=Qa3uS3H7-Le) | 42016 | 1593 | 279425 |\n| BioKG | [`pykeen.datasets.BioKG`](https://pykeen.readthedocs.io/en/latest/api/pykeen.datasets.BioKG.html) | [Walsh *et al*., 2019](https://doi.org/10.1145/3340531.3412776) | 105524 | 17 | 2067997 |\n| Clinical Knowledge Graph | [`pykeen.datasets.CKG`](https://pykeen.readthedocs.io/en/latest/api/pykeen.datasets.CKG.html) | [Santos *et al*., 2020](https://doi.org/10.1101/2020.05.09.084897) | 7617419 | 11 | 26691525 |\n| CN3l Family | [`pykeen.datasets.CN3l`](https://pykeen.readthedocs.io/en/latest/api/pykeen.datasets.CN3l.html) | [Chen *et al*., 2017](https://www.ijcai.org/Proceedings/2017/0209.pdf) | 3206 | 42 | 21777 |\n| CoDEx (large) | [`pykeen.datasets.CoDExLarge`](https://pykeen.readthedocs.io/en/latest/api/pykeen.datasets.CoDExLarge.html) | [Safavi *et al*., 2020](https://arxiv.org/abs/2009.07810) | 77951 | 69 | 612437 |\n| CoDEx (medium) | [`pykeen.datasets.CoDExMedium`](https://pykeen.readthedocs.io/en/latest/api/pykeen.datasets.CoDExMedium.html) | [Safavi *et al*., 2020](https://arxiv.org/abs/2009.07810) | 17050 | 51 | 206205 |\n| CoDEx (small) | [`pykeen.datasets.CoDExSmall`](https://pykeen.readthedocs.io/en/latest/api/pykeen.datasets.CoDExSmall.html) | [Safavi *et al*., 2020](https://arxiv.org/abs/2009.07810) | 2034 | 42 | 36543 |\n| ConceptNet | [`pykeen.datasets.ConceptNet`](https://pykeen.readthedocs.io/en/latest/api/pykeen.datasets.ConceptNet.html) | [Speer *et al*., 2017](https://arxiv.org/abs/1612.03975) | 28370083 | 50 | 34074917 |\n| Countries | [`pykeen.datasets.Countries`](https://pykeen.readthedocs.io/en/latest/api/pykeen.datasets.Countries.html) | [Bouchard *et al*., 2015](https://www.aaai.org/ocs/index.php/SSS/SSS15/paper/view/10257/10026) | 271 | 2 | 1158 |\n| Commonsense Knowledge Graph | [`pykeen.datasets.CSKG`](https://pykeen.readthedocs.io/en/latest/api/pykeen.datasets.CSKG.html) | [Ilievski *et al*., 2020](http://arxiv.org/abs/2012.11490) | 2087833 | 58 | 4598728 |\n| DB100K | [`pykeen.datasets.DB100K`](https://pykeen.readthedocs.io/en/latest/api/pykeen.datasets.DB100K.html) | [Ding *et al*., 2018](https://arxiv.org/abs/1805.02408) | 99604 | 470 | 697479 |\n| DBpedia50 | [`pykeen.datasets.DBpedia50`](https://pykeen.readthedocs.io/en/latest/api/pykeen.datasets.DBpedia50.html) | [Shi *et al*., 2017](https://arxiv.org/abs/1711.03438) | 24624 | 351 | 34421 |\n| Drug Repositioning Knowledge Graph | [`pykeen.datasets.DRKG`](https://pykeen.readthedocs.io/en/latest/api/pykeen.datasets.DRKG.html) | [`gnn4dr/DRKG`](https://github.com/gnn4dr/DRKG) | 97238 | 107 | 5874257 |\n| FB15k | [`pykeen.datasets.FB15k`](https://pykeen.readthedocs.io/en/latest/api/pykeen.datasets.FB15k.html) | [Bordes *et al*., 2013](http://papers.nips.cc/paper/5071-translating-embeddings-for-modeling-multi-relational-data.pdf) | 14951 | 1345 | 592213 |\n| FB15k-237 | [`pykeen.datasets.FB15k237`](https://pykeen.readthedocs.io/en/latest/api/pykeen.datasets.FB15k237.html) | [Toutanova *et al*., 2015](https://www.aclweb.org/anthology/W15-4007/) | 14505 | 237 | 310079 |\n| Global Biotic Interactions | [`pykeen.datasets.Globi`](https://pykeen.readthedocs.io/en/latest/api/pykeen.datasets.Globi.html) | [Poelen *et al*., 2014](https://doi.org/10.1016/j.ecoinf.2014.08.005) | 404207 | 39 | 1966385 |\n| Hetionet | [`pykeen.datasets.Hetionet`](https://pykeen.readthedocs.io/en/latest/api/pykeen.datasets.Hetionet.html) | [Himmelstein *et al*., 2017](https://doi.org/10.7554/eLife.26726) | 45158 | 24 | 2250197 |\n| Kinships | [`pykeen.datasets.Kinships`](https://pykeen.readthedocs.io/en/latest/api/pykeen.datasets.Kinships.html) | [Kemp *et al*., 2006](https://www.aaai.org/Papers/AAAI/2006/AAAI06-061.pdf) | 104 | 25 | 10686 |\n| Nations | [`pykeen.datasets.Nations`](https://pykeen.readthedocs.io/en/latest/api/pykeen.datasets.Nations.html) | [`ZhenfengLei/KGDatasets`](https://github.com/ZhenfengLei/KGDatasets) | 14 | 55 | 1992 |\n| NationsL | [`pykeen.datasets.NationsLiteral`](https://pykeen.readthedocs.io/en/latest/api/pykeen.datasets.NationsLiteral.html) | [`pykeen/pykeen`](https://github.com/pykeen/pykeen) | 14 | 55 | 1992 |\n| OGB BioKG | [`pykeen.datasets.OGBBioKG`](https://pykeen.readthedocs.io/en/latest/api/pykeen.datasets.OGBBioKG.html) | [Hu *et al*., 2020](https://arxiv.org/abs/2005.00687) | 93773 | 51 | 5088434 |\n| OGB WikiKG2 | [`pykeen.datasets.OGBWikiKG2`](https://pykeen.readthedocs.io/en/latest/api/pykeen.datasets.OGBWikiKG2.html) | [Hu *et al*., 2020](https://arxiv.org/abs/2005.00687) | 2500604 | 535 | 17137181 |\n| OpenBioLink | [`pykeen.datasets.OpenBioLink`](https://pykeen.readthedocs.io/en/latest/api/pykeen.datasets.OpenBioLink.html) | [Breit *et al*., 2020](https://doi.org/10.1093/bioinformatics/btaa274) | 180992 | 28 | 4563407 |\n| OpenBioLink LQ | [`pykeen.datasets.OpenBioLinkLQ`](https://pykeen.readthedocs.io/en/latest/api/pykeen.datasets.OpenBioLinkLQ.html) | [Breit *et al*., 2020](https://doi.org/10.1093/bioinformatics/btaa274) | 480876 | 32 | 27320889 |\n| OpenEA Family | [`pykeen.datasets.OpenEA`](https://pykeen.readthedocs.io/en/latest/api/pykeen.datasets.OpenEA.html) | [Sun *et al*., 2020](http://www.vldb.org/pvldb/vol13/p2326-sun.pdf) | 15000 | 248 | 38265 |\n| PharMeBINet | [`pykeen.datasets.PharMeBINet`](https://pykeen.readthedocs.io/en/latest/api/pykeen.datasets.PharMeBINet.html) | [K\u00f6nigs *et al*., 2022](https://www.nature.com/articles/s41597-022-01510-3) | 2869407 | 208 | 15883653 |\n| PharmKG | [`pykeen.datasets.PharmKG`](https://pykeen.readthedocs.io/en/latest/api/pykeen.datasets.PharmKG.html) | [Zheng *et al*., 2020](https://doi.org/10.1093/bib/bbaa344) | 188296 | 39 | 1093236 |\n| PharmKG8k | [`pykeen.datasets.PharmKG8k`](https://pykeen.readthedocs.io/en/latest/api/pykeen.datasets.PharmKG8k.html) | [Zheng *et al*., 2020](https://doi.org/10.1093/bib/bbaa344) | 7247 | 28 | 485787 |\n| PrimeKG | [`pykeen.datasets.PrimeKG`](https://pykeen.readthedocs.io/en/latest/api/pykeen.datasets.PrimeKG.html) | [Chandak *et al*., 2022](https://doi.org/10.1101/2022.05.01.489928) | 129375 | 30 | 8100498 |\n| Unified Medical Language System | [`pykeen.datasets.UMLS`](https://pykeen.readthedocs.io/en/latest/api/pykeen.datasets.UMLS.html) | [`ZhenfengLei/KGDatasets`](https://github.com/ZhenfengLei/KGDatasets) | 135 | 46 | 6529 |\n| WD50K (triples) | [`pykeen.datasets.WD50KT`](https://pykeen.readthedocs.io/en/latest/api/pykeen.datasets.WD50KT.html) | [Galkin *et al*., 2020](https://www.aclweb.org/anthology/2020.emnlp-main.596/) | 40107 | 473 | 232344 |\n| Wikidata5M | [`pykeen.datasets.Wikidata5M`](https://pykeen.readthedocs.io/en/latest/api/pykeen.datasets.Wikidata5M.html) | [Wang *et al*., 2019](https://arxiv.org/abs/1911.06136) | 4594149 | 822 | 20624239 |\n| WK3l-120k Family | [`pykeen.datasets.WK3l120k`](https://pykeen.readthedocs.io/en/latest/api/pykeen.datasets.WK3l120k.html) | [Chen *et al*., 2017](https://www.ijcai.org/Proceedings/2017/0209.pdf) | 119748 | 3109 | 1375406 |\n| WK3l-15k Family | [`pykeen.datasets.WK3l15k`](https://pykeen.readthedocs.io/en/latest/api/pykeen.datasets.WK3l15k.html) | [Chen *et al*., 2017](https://www.ijcai.org/Proceedings/2017/0209.pdf) | 15126 | 1841 | 209041 |\n| WordNet-18 | [`pykeen.datasets.WN18`](https://pykeen.readthedocs.io/en/latest/api/pykeen.datasets.WN18.html) | [Bordes *et al*., 2014](https://arxiv.org/abs/1301.3485) | 40943 | 18 | 151442 |\n| WordNet-18 (RR) | [`pykeen.datasets.WN18RR`](https://pykeen.readthedocs.io/en/latest/api/pykeen.datasets.WN18RR.html) | [Toutanova *et al*., 2015](https://www.aclweb.org/anthology/W15-4007/) | 40559 | 11 | 92583 |\n| YAGO3-10 | [`pykeen.datasets.YAGO310`](https://pykeen.readthedocs.io/en/latest/api/pykeen.datasets.YAGO310.html) | [Mahdisoltani *et al*., 2015](http://service.tsi.telecom-paristech.fr/cgi-bin//valipub_download.cgi?dId=284) | 123143 | 37 | 1089000 |\n\n### Inductive Datasets\n\nThe following 5 inductive datasets are built in to PyKEEN.\n\n| Name | Documentation | Citation |\n|-----------------|---------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------|\n| ILPC2022 Large | [`pykeen.datasets.ILPC2022Large`](https://pykeen.readthedocs.io/en/latest/api/pykeen.datasets.ILPC2022Large.html) | [Galkin *et al*., 2022](https://arxiv.org/abs/2203.01520) |\n| ILPC2022 Small | [`pykeen.datasets.ILPC2022Small`](https://pykeen.readthedocs.io/en/latest/api/pykeen.datasets.ILPC2022Small.html) | [Galkin *et al*., 2022](https://arxiv.org/abs/2203.01520) |\n| FB15k-237 | [`pykeen.datasets.InductiveFB15k237`](https://pykeen.readthedocs.io/en/latest/api/pykeen.datasets.InductiveFB15k237.html) | [Teru *et al*., 2020](https://arxiv.org/abs/1911.06962) |\n| NELL | [`pykeen.datasets.InductiveNELL`](https://pykeen.readthedocs.io/en/latest/api/pykeen.datasets.InductiveNELL.html) | [Teru *et al*., 2020](https://arxiv.org/abs/1911.06962) |\n| WordNet-18 (RR) | [`pykeen.datasets.InductiveWN18RR`](https://pykeen.readthedocs.io/en/latest/api/pykeen.datasets.InductiveWN18RR.html) | [Teru *et al*., 2020](https://arxiv.org/abs/1911.06962) |\n\n### Representations\n\nThe following 20 representations are implemented by PyKEEN.\n\n| Name | Reference |\n|----------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| Backfill | [`pykeen.nn.BackfillRepresentation`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.representation.BackfillRepresentation.html) |\n| Text Encoding | [`pykeen.nn.BiomedicalCURIERepresentation`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.representation.BiomedicalCURIERepresentation.html) |\n| Combined | [`pykeen.nn.CombinedRepresentation`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.representation.CombinedRepresentation.html) |\n| Embedding | [`pykeen.nn.Embedding`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.representation.Embedding.html) |\n| Featurized Message Passing | [`pykeen.nn.FeaturizedMessagePassingRepresentation`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.pyg.FeaturizedMessagePassingRepresentation.html) |\n| Low Rank Embedding | [`pykeen.nn.LowRankRepresentation`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.representation.LowRankRepresentation.html) |\n| NodePiece | [`pykeen.nn.NodePieceRepresentation`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.node_piece.representation.NodePieceRepresentation.html) |\n| Partition | [`pykeen.nn.PartitionRepresentation`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.representation.PartitionRepresentation.html) |\n| R-GCN | [`pykeen.nn.RGCNRepresentation`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.message_passing.RGCNRepresentation.html) |\n| Simple Message Passing | [`pykeen.nn.SimpleMessagePassingRepresentation`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.pyg.SimpleMessagePassingRepresentation.html) |\n| CompGCN | [`pykeen.nn.SingleCompGCNRepresentation`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.representation.SingleCompGCNRepresentation.html) |\n| Subset Representation | [`pykeen.nn.SubsetRepresentation`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.representation.SubsetRepresentation.html) |\n| Tensor-Train | [`pykeen.nn.TensorTrainRepresentation`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.representation.TensorTrainRepresentation.html) |\n| Text Encoding | [`pykeen.nn.TextRepresentation`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.representation.TextRepresentation.html) |\n| Tokenization | [`pykeen.nn.TokenizationRepresentation`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.node_piece.representation.TokenizationRepresentation.html) |\n| Transformed | [`pykeen.nn.TransformedRepresentation`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.representation.TransformedRepresentation.html) |\n| Typed Message Passing | [`pykeen.nn.TypedMessagePassingRepresentation`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.pyg.TypedMessagePassingRepresentation.html) |\n| Visual | [`pykeen.nn.VisualRepresentation`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.vision.representation.VisualRepresentation.html) |\n| Wikidata Text Encoding | [`pykeen.nn.WikidataTextRepresentation`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.representation.WikidataTextRepresentation.html) |\n| Wikidata Visual | [`pykeen.nn.WikidataVisualRepresentation`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.vision.representation.WikidataVisualRepresentation.html) |\n\n### Interactions\n\nThe following 34 interactions are implemented by PyKEEN.\n\n| Name | Reference | Citation |\n|--------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------|\n| AutoSF | [`pykeen.nn.AutoSFInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.modules.AutoSFInteraction.html) | [Zhang *et al.*, 2020](https://arxiv.org/abs/1904.11682) |\n| BoxE | [`pykeen.nn.BoxEInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.modules.BoxEInteraction.html) | [Abboud *et al.*, 2020](https://arxiv.org/abs/2007.06267) |\n| ComplEx | [`pykeen.nn.ComplExInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.modules.ComplExInteraction.html) | [Trouillon *et al.*, 2016](https://arxiv.org/abs/1606.06357) |\n| ConvE | [`pykeen.nn.ConvEInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.modules.ConvEInteraction.html) | [Dettmers *et al.*, 2018](https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/17366) |\n| ConvKB | [`pykeen.nn.ConvKBInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.modules.ConvKBInteraction.html) | [Nguyen *et al.*, 2018](https://www.aclweb.org/anthology/N18-2053) |\n| Canonical Tensor Decomposition | [`pykeen.nn.CPInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.modules.CPInteraction.html) | [Lacroix *et al.*, 2018](https://arxiv.org/abs/1806.07297) |\n| CrossE | [`pykeen.nn.CrossEInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.modules.CrossEInteraction.html) | [Zhang *et al.*, 2019](https://arxiv.org/abs/1903.04750) |\n| DistMA | [`pykeen.nn.DistMAInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.modules.DistMAInteraction.html) | [Shi *et al.*, 2019](https://www.aclweb.org/anthology/D19-1075.pdf) |\n| DistMult | [`pykeen.nn.DistMultInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.modules.DistMultInteraction.html) | [Yang *et al.*, 2014](https://arxiv.org/abs/1412.6575) |\n| ER-MLP | [`pykeen.nn.ERMLPInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.modules.ERMLPInteraction.html) | [Dong *et al.*, 2014](https://storage.googleapis.com/pub-tools-public-publication-data/pdf/45634.pdf) |\n| ER-MLP (E) | [`pykeen.nn.ERMLPEInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.modules.ERMLPEInteraction.html) | [Sharifzadeh *et al.*, 2019](https://github.com/pykeen/pykeen) |\n| HolE | [`pykeen.nn.HolEInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.modules.HolEInteraction.html) | [Nickel *et al.*, 2016](https://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/viewFile/12484/11828) |\n| KG2E | [`pykeen.nn.KG2EInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.modules.KG2EInteraction.html) | [He *et al.*, 2015](https://dl.acm.org/doi/10.1145/2806416.2806502) |\n| LineaRE | [`pykeen.nn.LineaREInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.modules.LineaREInteraction.html) | [Peng *et al.*, 2020](https://arxiv.org/abs/2004.10037) |\n| MultiLinearTucker | [`pykeen.nn.MultiLinearTuckerInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.modules.MultiLinearTuckerInteraction.html) | [Tucker *et al.*, 1966](https://dx.doi.org/10.1007/BF02289464) |\n| MuRE | [`pykeen.nn.MuREInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.modules.MuREInteraction.html) | [Bala\u017eevi\u0107 *et al.*, 2019](https://arxiv.org/abs/1905.09791) |\n| NTN | [`pykeen.nn.NTNInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.modules.NTNInteraction.html) | [Socher *et al.*, 2013](https://proceedings.neurips.cc/paper/2013/file/b337e84de8752b27eda3a12363109e80-Paper.pdf) |\n| PairRE | [`pykeen.nn.PairREInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.modules.PairREInteraction.html) | [Chao *et al.*, 2020](http://arxiv.org/abs/2011.03798) |\n| ProjE | [`pykeen.nn.ProjEInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.modules.ProjEInteraction.html) | [Shi *et al.*, 2017](https://aaai.org/papers/10677-aaai-31-2017/) |\n| QuatE | [`pykeen.nn.QuatEInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.modules.QuatEInteraction.html) | [Zhang *et al.*, 2019](https://arxiv.org/abs/1904.10281) |\n| RESCAL | [`pykeen.nn.RESCALInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.modules.RESCALInteraction.html) | [Nickel *et al.*, 2011](https://icml.cc/2011/papers/438_icmlpaper.pdf) |\n| RotatE | [`pykeen.nn.RotatEInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.modules.RotatEInteraction.html) | [Sun *et al.*, 2019](https://arxiv.org/abs/1902.10197) |\n| Structured Embedding | [`pykeen.nn.SEInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.modules.SEInteraction.html) | [Bordes *et al.*, 2011](https://www.aaai.org/ocs/index.php/AAAI/AAAI11/paper/download/3659/3898) |\n| SimplE | [`pykeen.nn.SimplEInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.modules.SimplEInteraction.html) | [Kazemi *et al.*, 2018](https://papers.nips.cc/paper/7682-simple-embedding-for-link-prediction-in-knowledge-graphs) |\n| TorusE | [`pykeen.nn.TorusEInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.modules.TorusEInteraction.html) | [Ebisu *et al.*, 2018](https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/16227) |\n| TransD | [`pykeen.nn.TransDInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.modules.TransDInteraction.html) | [Ji *et al.*, 2015](http://www.aclweb.org/anthology/P15-1067) |\n| TransE | [`pykeen.nn.TransEInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.modules.TransEInteraction.html) | [Bordes *et al.*, 2013](http://papers.nips.cc/paper/5071-translating-embeddings-for-modeling-multi-relational-data.pdf) |\n| TransF | [`pykeen.nn.TransFInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.modules.TransFInteraction.html) | [Feng *et al.*, 2016](https://www.aaai.org/ocs/index.php/KR/KR16/paper/view/12887) |\n| Transformer | [`pykeen.nn.TransformerInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.modules.TransformerInteraction.html) | [Galkin *et al.*, 2020](https://doi.org/10.18653/v1/2020.emnlp-main.596) |\n| TransH | [`pykeen.nn.TransHInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.modules.TransHInteraction.html) | [Wang *et al.*, 2014](https://aaai.org/papers/8870-knowledge-graph-embedding-by-translating-on-hyperplanes/) |\n| TransR | [`pykeen.nn.TransRInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.modules.TransRInteraction.html) | [Lin *et al.*, 2015](https://aaai.org/papers/9491-learning-entity-and-relation-embeddings-for-knowledge-graph-completion/) |\n| TripleRE | [`pykeen.nn.TripleREInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.modules.TripleREInteraction.html) | [Yu *et al.*, 2021](https://vixra.org/abs/2112.0095) |\n| TuckER | [`pykeen.nn.TuckERInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.modules.TuckERInteraction.html) | [Bala\u017eevi\u0107 *et al.*, 2019](https://arxiv.org/abs/1901.09590) |\n| Unstructured Model | [`pykeen.nn.UMInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.modules.UMInteraction.html) | [Bordes *et al.*, 2014](https://link.springer.com/content/pdf/10.1007%2Fs10994-013-5363-6.pdf) |\n\n### Models\n\nThe following 40 models are implemented by PyKEEN.\n\n| Name | Model | Citation |\n|--------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------|\n| AutoSF | [`pykeen.models.AutoSF`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.unimodal.auto_sf.AutoSF.html) | [Zhang *et al.*, 2020](https://arxiv.org/abs/1904.11682) |\n| BoxE | [`pykeen.models.BoxE`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.unimodal.boxe.BoxE.html) | [Abboud *et al.*, 2020](https://arxiv.org/abs/2007.06267) |\n| Canonical Tensor Decomposition | [`pykeen.models.CP`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.unimodal.cp.CP.html) | [Lacroix *et al.*, 2018](https://arxiv.org/abs/1806.07297) |\n| CompGCN | [`pykeen.models.CompGCN`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.unimodal.compgcn.CompGCN.html) | [Vashishth *et al.*, 2020](https://arxiv.org/pdf/1911.03082) |\n| ComplEx | [`pykeen.models.ComplEx`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.unimodal.complex.ComplEx.html) | [Trouillon *et al.*, 2016](https://arxiv.org/abs/1606.06357) |\n| ComplEx Literal | [`pykeen.models.ComplExLiteral`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.multimodal.complex_literal.ComplExLiteral.html) | [Kristiadi *et al.*, 2018](https://arxiv.org/abs/1802.00934) |\n| ConvE | [`pykeen.models.ConvE`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.unimodal.conv_e.ConvE.html) | [Dettmers *et al.*, 2018](https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/17366) |\n| ConvKB | [`pykeen.models.ConvKB`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.unimodal.conv_kb.ConvKB.html) | [Nguyen *et al.*, 2018](https://www.aclweb.org/anthology/N18-2053) |\n| CooccurrenceFiltered | [`pykeen.models.CooccurrenceFilteredModel`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.meta.filtered.CooccurrenceFilteredModel.html) | [Berrendorf *et al.*, 2022](https://github.com/pykeen/pykeen/pull/943) |\n| CrossE | [`pykeen.models.CrossE`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.unimodal.crosse.CrossE.html) | [Zhang *et al.*, 2019](https://arxiv.org/abs/1903.04750) |\n| DistMA | [`pykeen.models.DistMA`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.unimodal.distma.DistMA.html) | [Shi *et al.*, 2019](https://www.aclweb.org/anthology/D19-1075.pdf) |\n| DistMult | [`pykeen.models.DistMult`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.unimodal.distmult.DistMult.html) | [Yang *et al.*, 2014](https://arxiv.org/abs/1412.6575) |\n| DistMult Literal | [`pykeen.models.DistMultLiteral`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.multimodal.distmult_literal.DistMultLiteral.html) | [Kristiadi *et al.*, 2018](https://arxiv.org/abs/1802.00934) |\n| DistMult Literal (Gated) | [`pykeen.models.DistMultLiteralGated`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.multimodal.distmult_literal_gated.DistMultLiteralGated.html) | [Kristiadi *et al.*, 2018](https://arxiv.org/abs/1802.00934) |\n| ER-MLP | [`pykeen.models.ERMLP`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.unimodal.ermlp.ERMLP.html) | [Dong *et al.*, 2014](https://dl.acm.org/citation.cfm?id=2623623) |\n| ER-MLP (E) | [`pykeen.models.ERMLPE`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.unimodal.ermlpe.ERMLPE.html) | [Sharifzadeh *et al.*, 2019](https://github.com/pykeen/pykeen) |\n| Fixed Model | [`pykeen.models.FixedModel`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.mocks.FixedModel.html) | [Berrendorf *et al.*, 2021](https://github.com/pykeen/pykeen/pull/691) |\n| HolE | [`pykeen.models.HolE`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.unimodal.hole.HolE.html) | [Nickel *et al.*, 2016](https://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/viewFile/12484/11828) |\n| InductiveNodePiece | [`pykeen.models.InductiveNodePiece`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.inductive.inductive_nodepiece.InductiveNodePiece.html) | [Galkin *et al.*, 2021](https://arxiv.org/abs/2106.12144) |\n| InductiveNodePieceGNN | [`pykeen.models.InductiveNodePieceGNN`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.inductive.inductive_nodepiece_gnn.InductiveNodePieceGNN.html) | [Galkin *et al.*, 2021](https://arxiv.org/abs/2106.12144) |\n| KG2E | [`pykeen.models.KG2E`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.unimodal.kg2e.KG2E.html) | [He *et al.*, 2015](https://dl.acm.org/doi/10.1145/2806416.2806502) |\n| MuRE | [`pykeen.models.MuRE`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.unimodal.mure.MuRE.html) | [Bala\u017eevi\u0107 *et al.*, 2019](https://arxiv.org/abs/1905.09791) |\n| NTN | [`pykeen.models.NTN`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.unimodal.ntn.NTN.html) | [Socher *et al.*, 2013](https://dl.acm.org/doi/10.5555/2999611.2999715) |\n| NodePiece | [`pykeen.models.NodePiece`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.unimodal.node_piece.NodePiece.html) | [Galkin *et al.*, 2021](https://arxiv.org/abs/2106.12144) |\n| PairRE | [`pykeen.models.PairRE`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.unimodal.pair_re.PairRE.html) | [Chao *et al.*, 2020](http://arxiv.org/abs/2011.03798) |\n| ProjE | [`pykeen.models.ProjE`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.unimodal.proj_e.ProjE.html) | [Shi *et al.*, 2017](https://www.aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14279) |\n| QuatE | [`pykeen.models.QuatE`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.unimodal.quate.QuatE.html) | [Zhang *et al.*, 2019](https://arxiv.org/abs/1904.10281) |\n| R-GCN | [`pykeen.models.RGCN`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.unimodal.rgcn.RGCN.html) | [Schlichtkrull *et al.*, 2018](https://arxiv.org/pdf/1703.06103) |\n| RESCAL | [`pykeen.models.RESCAL`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.unimodal.rescal.RESCAL.html) | [Nickel *et al.*, 2011](http://www.cip.ifi.lmu.de/~nickel/data/paper-icml2011.pdf) |\n| RotatE | [`pykeen.models.RotatE`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.unimodal.rotate.RotatE.html) | [Sun *et al.*, 2019](https://arxiv.org/abs/1902.10197v1) |\n| SimplE | [`pykeen.models.SimplE`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.unimodal.simple.SimplE.html) | [Kazemi *et al.*, 2018](https://papers.nips.cc/paper/7682-simple-embedding-for-link-prediction-in-knowledge-graphs) |\n| Structured Embedding | [`pykeen.models.SE`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.unimodal.structured_embedding.SE.html) | [Bordes *et al.*, 2011](https://www.aaai.org/ocs/index.php/AAAI/AAAI11/paper/download/3659/3898) |\n| TorusE | [`pykeen.models.TorusE`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.unimodal.toruse.TorusE.html) | [Ebisu *et al.*, 2018](https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/16227) |\n| TransD | [`pykeen.models.TransD`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.unimodal.trans_d.TransD.html) | [Ji *et al.*, 2015](http://www.aclweb.org/anthology/P15-1067) |\n| TransE | [`pykeen.models.TransE`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.unimodal.trans_e.TransE.html) | [Bordes *et al.*, 2013](http://papers.nips.cc/paper/5071-translating-embeddings-for-modeling-multi-relational-data.pdf) |\n| TransF | [`pykeen.models.TransF`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.unimodal.trans_f.TransF.html) | [Feng *et al.*, 2016](https://www.aaai.org/ocs/index.php/KR/KR16/paper/view/12887) |\n| TransH | [`pykeen.models.TransH`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.unimodal.trans_h.TransH.html) | [Wang *et al.*, 2014](https://aaai.org/papers/8870-knowledge-graph-embedding-by-translating-on-hyperplanes/) |\n| TransR | [`pykeen.models.TransR`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.unimodal.trans_r.TransR.html) | [Lin *et al.*, 2015](http://www.aaai.org/ocs/index.php/AAAI/AAAI15/paper/download/9571/9523/) |\n| TuckER | [`pykeen.models.TuckER`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.unimodal.tucker.TuckER.html) | [Bala\u017eevi\u0107 *et al.*, 2019](https://arxiv.org/abs/1901.09590) |\n| Unstructured Model | [`pykeen.models.UM`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.unimodal.unstructured_model.UM.html) | [Bordes *et al.*, 2014](https://link.springer.com/content/pdf/10.1007%2Fs10994-013-5363-6.pdf) |\n\n### Losses\n\nThe following 15 losses are implemented by PyKEEN.\n\n| Name | Reference | Description |\n|-----------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------|\n| Adversarially weighted binary cross entropy (with logits) | [`pykeen.losses.AdversarialBCEWithLogitsLoss`](https://pykeen.readthedocs.io/en/latest/api/pykeen.losses.AdversarialBCEWithLogitsLoss.html) | An adversarially weighted BCE loss. |\n| Binary cross entropy (after sigmoid) | [`pykeen.losses.BCEAfterSigmoidLoss`](https://pykeen.readthedocs.io/en/latest/api/pykeen.losses.BCEAfterSigmoidLoss.html) | The numerically unstable version of explicit Sigmoid + BCE loss. |\n| Binary cross entropy (with logits) | [`pykeen.losses.BCEWithLogitsLoss`](https://pykeen.readthedocs.io/en/latest/api/pykeen.losses.BCEWithLogitsLoss.html) | The binary cross entropy loss. |\n| Cross entropy | [`pykeen.losses.CrossEntropyLoss`](https://pykeen.readthedocs.io/en/latest/api/pykeen.losses.CrossEntropyLoss.html) | The cross entropy loss that evaluates the cross entropy after softmax output. |\n| Double Margin | [`pykeen.losses.DoubleMarginLoss`](https://pykeen.readthedocs.io/en/latest/api/pykeen.losses.DoubleMarginLoss.html) | A limit-based scoring loss, with separate margins for positive and negative elements from [sun2018]_. |\n| Focal | [`pykeen.losses.FocalLoss`](https://pykeen.readthedocs.io/en/latest/api/pykeen.losses.FocalLoss.html) | The focal loss proposed by [lin2018]_. |\n| InfoNCE loss with additive margin | [`pykeen.losses.InfoNCELoss`](https://pykeen.readthedocs.io/en/latest/api/pykeen.losses.InfoNCELoss.html) | The InfoNCE loss with additive margin proposed by [wang2022]_. |\n| Margin ranking | [`pykeen.losses.MarginRankingLoss`](https://pykeen.readthedocs.io/en/latest/api/pykeen.losses.MarginRankingLoss.html) | The pairwise hinge loss (i.e., margin ranking loss). |\n| Mean squared error | [`pykeen.losses.MSELoss`](https://pykeen.readthedocs.io/en/latest/api/pykeen.losses.MSELoss.html) | The mean squared error loss. |\n| Self-adversarial negative sampling | [`pykeen.losses.NSSALoss`](https://pykeen.readthedocs.io/en/latest/api/pykeen.losses.NSSALoss.html) | The self-adversarial negative sampling loss function proposed by [sun2019]_. |\n| Pairwise logistic | [`pykeen.losses.PairwiseLogisticLoss`](https://pykeen.readthedocs.io/en/latest/api/pykeen.losses.PairwiseLogisticLoss.html) | The pairwise logistic loss. |\n| Pointwise Hinge | [`pykeen.losses.PointwiseHingeLoss`](https://pykeen.readthedocs.io/en/latest/api/pykeen.losses.PointwiseHingeLoss.html) | The pointwise hinge loss. |\n| Soft margin ranking | [`pykeen.losses.SoftMarginRankingLoss`](https://pykeen.readthedocs.io/en/latest/api/pykeen.losses.SoftMarginRankingLoss.html) | The soft pairwise hinge loss (i.e., soft margin ranking loss). |\n| Softplus | [`pykeen.losses.SoftplusLoss`](https://pykeen.readthedocs.io/en/latest/api/pykeen.losses.SoftplusLoss.html) | The pointwise logistic loss (i.e., softplus loss). |\n| Soft Pointwise Hinge | [`pykeen.losses.SoftPointwiseHingeLoss`](https://pykeen.readthedocs.io/en/latest/api/pykeen.losses.SoftPointwiseHingeLoss.html) | The soft pointwise hinge loss. |\n\n### Regularizers\n\nThe following 6 regularizers are implemented by PyKEEN.\n\n| Name | Reference | Description |\n|---------------|-------------------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------|\n| combined | [`pykeen.regularizers.CombinedRegularizer`](https://pykeen.readthedocs.io/en/latest/api/pykeen.regularizers.CombinedRegularizer.html) | A convex combination of regularizers. |\n| lp | [`pykeen.regularizers.LpRegularizer`](https://pykeen.readthedocs.io/en/latest/api/pykeen.regularizers.LpRegularizer.html) | A simple L_p norm based regularizer. |\n| no | [`pykeen.regularizers.NoRegularizer`](https://pykeen.readthedocs.io/en/latest/api/pykeen.regularizers.NoRegularizer.html) | A regularizer which does not perform any regularization. |\n| normlimit | [`pykeen.regularizers.NormLimitRegularizer`](https://pykeen.readthedocs.io/en/latest/api/pykeen.regularizers.NormLimitRegularizer.html) | A regularizer which formulates a soft constraint on a maximum norm. |\n| orthogonality | [`pykeen.regularizers.OrthogonalityRegularizer`](https://pykeen.readthedocs.io/en/latest/api/pykeen.regularizers.OrthogonalityRegularizer.html) | A regularizer for the soft orthogonality constraints from [wang2014]_. |\n| powersum | [`pykeen.regularizers.PowerSumRegularizer`](https://pykeen.readthedocs.io/en/latest/api/pykeen.regularizers.PowerSumRegularizer.html) | A simple x^p based regularizer. |\n\n### Training Loops\n\nThe following 3 training loops are implemented in PyKEEN.\n\n| Name | Reference | Description |\n|---------------|----------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------|\n| lcwa | [`pykeen.training.LCWATrainingLoop`](https://pykeen.readthedocs.io/en/latest/reference/training.html#pykeen.training.LCWATrainingLoop) | A training loop that is based upon the local closed world assumption (LCWA). |\n| slcwa | [`pykeen.training.SLCWATrainingLoop`](https://pykeen.readthedocs.io/en/latest/reference/training.html#pykeen.training.SLCWATrainingLoop) | A training loop that uses the stochastic local closed world assumption training approach. |\n| symmetriclcwa | [`pykeen.training.SymmetricLCWATrainingLoop`](https://pykeen.readthedocs.io/en/latest/reference/training.html#pykeen.training.SymmetricLCWATrainingLoop) | A "symmetric" LCWA scoring heads *and* tails at once. |\n\n### Negative Samplers\n\nThe following 3 negative samplers are implemented in PyKEEN.\n\n| Name | Reference | Description |\n|-------------|---------------------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------|\n| basic | [`pykeen.sampling.BasicNegativeSampler`](https://pykeen.readthedocs.io/en/latest/api/pykeen.sampling.BasicNegativeSampler.html) | A basic negative sampler. |\n| bernoulli | [`pykeen.sampling.BernoulliNegativeSampler`](https://pykeen.readthedocs.io/en/latest/api/pykeen.sampling.BernoulliNegativeSampler.html) | An implementation of the Bernoulli negative sampling approach proposed by [wang2014]_. |\n| pseudotyped | [`pykeen.sampling.PseudoTypedNegativeSampler`](https://pykeen.readthedocs.io/en/latest/api/pykeen.sampling.PseudoTypedNegativeSampler.html) | A sampler that accounts for which entities co-occur with a relation. |\n\n### Stoppers\n\nThe following 2 stoppers are implemented in PyKEEN.\n\n| Name | Reference | Description |\n|--------|--------------------------------------------------------------------------------------------------------------------------------|-------------------------------|\n| early | [`pykeen.stoppers.EarlyStopper`](https://pykeen.readthedocs.io/en/latest/reference/stoppers.html#pykeen.stoppers.EarlyStopper) | A harness for early stopping. |\n| nop | [`pykeen.stoppers.NopStopper`](https://pykeen.readthedocs.io/en/latest/reference/stoppers.html#pykeen.stoppers.NopStopper) | A stopper that does nothing. |\n\n### Evaluators\n\nThe following 5 evaluators are implemented in PyKEEN.\n\n| Name | Reference | Description |\n|------------------|-----------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------|\n| classification | [`pykeen.evaluation.ClassificationEvaluator`](https://pykeen.readthedocs.io/en/latest/api/pykeen.evaluation.ClassificationEvaluator.html) | An evaluator that uses a classification metrics. |\n| macrorankbased | [`pykeen.evaluation.MacroRankBasedEvaluator`](https://pykeen.readthedocs.io/en/latest/api/pykeen.evaluation.MacroRankBasedEvaluator.html) | Macro-average rank-based evaluation. |\n| ogb | [`pykeen.evaluation.OGBEvaluator`](https://pykeen.readthedocs.io/en/latest/api/pykeen.evaluation.OGBEvaluator.html) | A sampled, rank-based evaluator that applies a custom OGB evaluation. |\n| rankbased | [`pykeen.evaluation.RankBasedEvaluator`](https://pykeen.readthedocs.io/en/latest/api/pykeen.evaluation.RankBasedEvaluator.html) | A rank-based evaluator for KGE models. |\n| sampledrankbased | [`pykeen.evaluation.SampledRankBasedEvaluator`](https://pykeen.readthedocs.io/en/latest/api/pykeen.evaluation.SampledRankBasedEvaluator.html) | A rank-based evaluator using sampled negatives instead of all negatives. |\n\n### Metrics\n\nThe following 44 metrics are implemented in PyKEEN.\n\n| Name | Interval | Direction | Description | Type |\n|------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------|-------------|-------------------------------------------------------------------------|----------------|\n| [Accuracy](https://en.wikipedia.org/wiki/Evaluation_of_binary_classifiers#Single_metrics) | $[0, 1]$ | \ud83d\udcc8 | The ratio of the number of correct classifications to the total number. | Classification |\n| [Area Under The Receiver Operating Characteristic Curve](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_auc_score.html) | $[0, 1]$ | \ud83d\udcc8 | The area under the receiver operating characteristic curve. | Classification |\n| [Average Precision Score](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.average_precision_score.html) | $[0, 1]$ | \ud83d\udcc8 | The average precision across different thresholds. | Classification |\n| [Balanced Accuracy Score](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.balanced_accuracy_score.html) | $[0, 1]$ | \ud83d\udcc8 | The average of recall obtained on each class. | Classification |\n| [Diagnostic Odds Ratio](https://en.wikipedia.org/wiki/Diagnostic_odds_ratio) | $[0, \u221e)$ | \ud83d\udcc8 | The ratio of positive and negative likelihood ratio. | Classification |\n| [F1 Score](https://en.wikipedia.org/wiki/F1_score) | $[0, 1]$ | \ud83d\udcc8 | The harmonic mean of precision and recall. | Classification |\n| [False Discovery Rate](https://en.wikipedia.org/wiki/False_discovery_rate) | $[0, 1]$ | \ud83d\udcc9 | The proportion of predicted negatives which are true positive. | Classification |\n| [False Negative Rate](https://en.wikipedia.org/wiki/Type_I_and_type_II_errors#False_positive_and_false_negative_rates) | $[0, 1]$ | \ud83d\udcc9 | The probability that a truly positive triple is predicted negative. | Classification |\n| [False Omission Rate](https://en.wikipedia.org/wiki/Positive_and_negative_predictive_values) | $[0, 1]$ | \ud83d\udcc9 | The proportion of predicted positives which are true negative. | Classification |\n| [False Positive Rate](https://en.wikipedia.org/wiki/False_positive_rate) | $[0, 1]$ | \ud83d\udcc9 | The probability that a truly negative triple is predicted positive. | Classification |\n| [Fowlkes Mallows Index](https://en.wikipedia.org/wiki/Fowlkes%E2%80%93Mallows_index) | $[0, 1]$ | \ud83d\udcc8 | The Fowlkes Mallows index. | Classification |\n| [Informedness](https://en.wikipedia.org/wiki/Informedness) | $[-1, 1]$ | \ud83d\udcc8 | The informedness metric. | Classification |\n| [Matthews Correlation Coefficient](https://en.wikipedia.org/wiki/Phi_coefficient) | $[-1, 1]$ | \ud83d\udcc8 | The Matthews Correlation Coefficient (MCC). | Classification |\n| [Negative Likelihood Ratio](https://en.wikipedia.org/wiki/Negative_likelihood_ratio) | $[0, \u221e)$ | \ud83d\udcc9 | The ratio of false positive rate to true positive rate. | Classification |\n| [Negative Predictive Value](https://en.wikipedia.org/wiki/Negative_predictive_value) | $[0, 1]$ | \ud83d\udcc8 | The proportion of predicted negatives which are true negatives. | Classification |\n| [Number of Scores](https://pykeen.readthedocs.io/en/stable/reference/evaluation.html) | $[0, \u221e)$ | \ud83d\udcc8 | The number of scores. | Classification |\n| [Positive Likelihood Ratio](https://en.wikipedia.org/wiki/Positive_likelihood_ratio) | $[0, \u221e)$ | \ud83d\udcc8 | The ratio of true positive rate to false positive rate. | Classification |\n| [Positive Predictive Value](https://en.wikipedia.org/wiki/Positive_predictive_value) | $[0, 1]$ | \ud83d\udcc8 | The proportion of predicted positives which are true positive. | Classification |\n| [Prevalence Threshold](https://en.wikipedia.org/wiki/Prevalence_threshold) | $[0, \u221e)$ | \ud83d\udcc9 | The prevalence threshold. | Classification |\n| [Threat Score](https://en.wikipedia.org/wiki/Sensitivity_and_specificity) | $[0, 1]$ | \ud83d\udcc8 | The harmonic mean of precision and recall. | Classification |\n| [True Negative Rate](https://en.wikipedia.org/wiki/Specificity_(tests)) | $[0, 1]$ | \ud83d\udcc8 | The probability that a truly false triple is predicted negative. | Classification |\n| [True Positive Rate](https://en.wikipedia.org/wiki/Sensitivity_(test)) | $[0, 1]$ | \ud83d\udcc8 | The probability that a truly positive triple is predicted positive. | Classification |\n| [Adjusted Arithmetic Mean Rank (AAMR)](https://arxiv.org/abs/2002.06914) | $[0, 2)$ | \ud83d\udcc9 | The mean over all ranks divided by its expected value. | Ranking |\n| [Adjusted Arithmetic Mean Rank Index (AAMRI)](https://arxiv.org/abs/2002.06914) | $[-1, 1]$ | \ud83d\udcc8 | The re-indexed adjusted mean rank (AAMR) | Ranking |\n| [Adjusted Geometric Mean Rank Index (AGMRI)](https://arxiv.org/abs/2002.06914) | $(\\frac{-E[f]}{1-E[f]}, 1]$ | \ud83d\udcc8 | The re-indexed adjusted geometric mean rank (AGMRI) | Ranking |\n| [Adjusted Hits at K](https://arxiv.org/abs/2203.07544) | $(\\frac{-E[f]}{1-E[f]}, 1]$ | \ud83d\udcc8 | The re-indexed adjusted hits at K | Ranking |\n| [Adjusted Inverse Harmonic Mean Rank](https://arxiv.org/abs/2203.07544) | $(\\frac{-E[f]}{1-E[f]}, 1]$ | \ud83d\udcc8 | The re-indexed adjusted MRR | Ranking |\n| [Geometric Mean Rank (GMR)](https://arxiv.org/abs/2203.07544) | $[1, \u221e)$ | \ud83d\udcc9 | The geometric mean over all ranks. | Ranking |\n| [Harmonic Mean Rank (HMR)](https://arxiv.org/abs/2203.07544) | $[1, \u221e)$ | \ud83d\udcc9 | The harmonic mean over all ranks. | Ranking |\n| [Hits @ K](https://pykeen.readthedocs.io/en/stable/tutorial/understanding_evaluation.html#hits-k) | $[0, 1]$ | \ud83d\udcc8 | The relative frequency of ranks not larger than a given k. | Ranking |\n| [Inverse Arithmetic Mean Rank (IAMR)](https://arxiv.org/abs/2203.07544) | $(0, 1]$ | \ud83d\udcc8 | The inverse of the arithmetic mean over all ranks. | Ranking |\n| [Inverse Geometric Mean Rank (IGMR)](https://arxiv.org/abs/2203.07544) | $(0, 1]$ | \ud83d\udcc8 | The inverse of the geometric mean over all ranks. | Ranking |\n| [Inverse Median Rank](https://arxiv.org/abs/2203.07544) | $(0, 1]$ | \ud83d\udcc8 | The inverse of the median over all ranks. | Ranking |\n| [Mean Rank (MR)](https://pykeen.readthedocs.io/en/stable/tutorial/understanding_evaluation.html#mean-rank) | $[1, \u221e)$ | \ud83d\udcc9 | The arithmetic mean over all ranks. | Ranking |\n| [Mean Reciprocal Rank (MRR)](https://en.wikipedia.org/wiki/Mean_reciprocal_rank) | $(0, 1]$ | \ud83d\udcc8 | The inverse of the harmonic mean over all ranks. | Ranking |\n| [Median Rank](https://arxiv.org/abs/2203.07544) | $[1, \u221e)$ | \ud83d\udcc9 | The median over all ranks. | Ranking |\n| [z-Geometric Mean Rank (zGMR)](https://arxiv.org/abs/2203.07544) | $(-\u221e, \u221e)$ | \ud83d\udcc8 | The z-scored geometric mean rank | Ranking |\n| [z-Hits at K](https://arxiv.org/abs/2203.07544) | $(-\u221e, \u221e)$ | \ud83d\udcc8 | The z-scored hits at K | Ranking |\n| [z-Mean Rank (zMR)](https://arxiv.org/abs/2203.07544) | $(-\u221e, \u221e)$ | \ud83d\udcc8 | The z-scored mean rank | Ranking |\n| [z-Mean Reciprocal Rank (zMRR)](https://arxiv.org/abs/2203.07544) | $(-\u221e, \u221e)$ | \ud83d\udcc8 | The z-scored mean reciprocal rank | Ranking |\n\n### Trackers\n\nThe following 8 trackers are implemented in PyKEEN.\n\n| Name | Reference | Description |\n|-------------|-----------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------|\n| console | [`pykeen.trackers.ConsoleResultTracker`](https://pykeen.readthedocs.io/en/latest/api/pykeen.trackers.ConsoleResultTracker.html) | A class that directly prints to console. |\n| csv | [`pykeen.trackers.CSVResultTracker`](https://pykeen.readthedocs.io/en/latest/api/pykeen.trackers.CSVResultTracker.html) | Tracking results to a CSV file. |\n| json | [`pykeen.trackers.JSONResultTracker`](https://pykeen.readthedocs.io/en/latest/api/pykeen.trackers.JSONResultTracker.html) | Tracking results to a JSON lines file. |\n| mlflow | [`pykeen.trackers.MLFlowResultTracker`](https://pykeen.readthedocs.io/en/latest/api/pykeen.trackers.MLFlowResultTracker.html) | A tracker for MLflow. |\n| neptune | [`pykeen.trackers.NeptuneResultTracker`](https://pykeen.readthedocs.io/en/latest/api/pykeen.trackers.NeptuneResultTracker.html) | A tracker for Neptune.ai. |\n| python | [`pykeen.trackers.PythonResultTracker`](https://pykeen.readthedocs.io/en/latest/api/pykeen.trackers.PythonResultTracker.html) | A tracker which stores everything in Python dictionaries. |\n| tensorboard | [`pykeen.trackers.TensorBoardResultTracker`](https://pykeen.readthedocs.io/en/latest/api/pykeen.trackers.TensorBoardResultTracker.html) | A tracker for TensorBoard. |\n| wandb | [`pykeen.trackers.WANDBResultTracker`](https://pykeen.readthedocs.io/en/latest/api/pykeen.trackers.WANDBResultTracker.html) | A tracker for Weights and Biases. |\n\n## Experimentation\n\n### Reproduction\n\nPyKEEN includes a set of curated experimental settings for reproducing past landmark\nexperiments. They can be accessed and run like:\n\n```shell\npykeen experiments reproduce tucker balazevic2019 fb15k\n```\n\nWhere the three arguments are the model name, the reference, and the dataset.\nThe output directory can be optionally set with `-d`.\n\n### Ablation\n\nPyKEEN includes the ability to specify ablation studies using the\nhyper-parameter optimization module. They can be run like:\n\n```shell\npykeen experiments ablation ~/path/to/config.json\n```\n\n### Large-scale Reproducibility and Benchmarking Study\n\nWe used PyKEEN to perform a large-scale reproducibility and benchmarking study which are described in\n[our article](https://doi.org/10.1109/TPAMI.2021.3124805):\n\n```bibtex\n@article{ali2020benchmarking,\n author={Ali, Mehdi and Berrendorf, Max and Hoyt, Charles Tapley and Vermue, Laurent and Galkin, Mikhail and Sharifzadeh, Sahand and Fischer, Asja and Tresp, Volker and Lehmann, Jens},\n journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},\n title={Bringing Light Into the Dark: A Large-scale Evaluation of Knowledge Graph Embedding Models under a Unified Framework},\n year={2021},\n pages={1-1},\n doi={10.1109/TPAMI.2021.3124805}}\n}\n```\n\nWe have made all code, experimental configurations, results, and analyses that lead to our interpretations available\nat https://github.com/pykeen/benchmarking.\n\n## Contributing\n\nContributions, whether filing an issue, making a pull request, or forking, are appreciated.\nSee [CONTRIBUTING.md](/CONTRIBUTING.md) for more information on getting involved.\n\nIf you have questions, please use the GitHub discussions feature at\nhttps://github.com/pykeen/pykeen/discussions/new.\n\n## Acknowledgements\n\n### Supporters\n\nThis project has been supported by several organizations (in alphabetical order):\n\n- [Bayer](https://www.bayer.com/)\n- [CoronaWhy](https://www.coronawhy.org/)\n- [Enveda Biosciences](https://www.envedabio.com/)\n- [Fraunhofer Institute for Algorithms and Scientific Computing](https://www.scai.fraunhofer.de)\n- [Fraunhofer Institute for Intelligent Analysis and Information Systems](https://www.iais.fraunhofer.de)\n- [Fraunhofer Center for Machine Learning](https://www.cit.fraunhofer.de/de/zentren/maschinelles-lernen.html)\n- [Harvard Program in Therapeutic Science - Laboratory of Systems Pharmacology](https://hits.harvard.edu/the-program/laboratory-of-systems-pharmacology/)\n- [Ludwig-Maximilians-Universit\u00e4t M\u00fcnchen](https://www.en.uni-muenchen.de/index.html)\n- [Munich Center for Machine Learning (MCML)](https://mcml.ai/)\n- [Siemens](https://new.siemens.com/global/en.html)\n- [Smart Data Analytics Research Group (University of Bonn & Fraunhofer IAIS)](https://sda.tech)\n- [Technical University of Denmark - DTU Compute - Section for Cognitive Systems](https://www.compute.dtu.dk/english/research/research-sections/cogsys)\n- [Technical University of Denmark - DTU Compute - Section for Statistics and Data Analysis](https://www.compute.dtu.dk/english/research/research-sections/stat)\n- [University of Bonn](https://www.uni-bonn.de/)\n\n### Funding\n\nThe development of PyKEEN has been funded by the following grants:\n\n| Funding Body | Program | Grant |\n|----------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------|-----------------|\n| DARPA | [Young Faculty Award (PI: Benjamin Gyori)](https://indralab.github.io/#projects) | W911NF2010255 |\n| DARPA | [Automating Scientific Knowledge Extraction (ASKE)](https://www.darpa.mil/program/automating-scientific-knowledge-extraction) | HR00111990009 |\n| German Federal Ministry of Education and Research (BMBF) | [Maschinelles Lernen mit Wissensgraphen (MLWin)](https://mlwin.de) | 01IS18050D |\n| German Federal Ministry of Education and Research (BMBF) | [Munich Center for Machine Learning (MCML)](https://mcml.ai) | 01IS18036A |\n| Innovation Fund Denmark (Innovationsfonden) | [Danish Center for Big Data Analytics driven Innovation (DABAI)](https://dabai.dk) | Grand Solutions |\n\n### Logo\n\nThe PyKEEN logo was designed by [Carina Steinborn](https://www.xing.com/profile/Carina_Steinborn2)\n\n## Citation\n\nIf you have found PyKEEN useful in your work, please consider citing\n[our article](http://jmlr.org/papers/v22/20-825.html):\n\n```bibtex\n@article{ali2021pykeen,\n author = {Ali, Mehdi and Berrendorf, Max and Hoyt, Charles Tapley and Vermue, Laurent and Sharifzadeh, Sahand and Tresp, Volker and Lehmann, Jens},\n journal = {Journal of Machine Learning Research},\n number = {82},\n pages = {1--6},\n title = {{PyKEEN 1.0: A Python Library for Training and Evaluating Knowledge Graph Embeddings}},\n url = {http://jmlr.org/papers/v22/20-825.html},\n volume = {22},\n year = {2021}\n}\n```\n",
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"license": "MIT License Copyright (c) 2019-2024 PyKEEN Project Team Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the \"Software\"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. ",
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