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diff --git a/python-pykeen.spec b/python-pykeen.spec new file mode 100644 index 0000000..f08b9ac --- /dev/null +++ b/python-pykeen.spec @@ -0,0 +1,1514 @@ +%global _empty_manifest_terminate_build 0 +Name: python-pykeen +Version: 1.10.1 +Release: 1 +Summary: A package for training and evaluating multimodal knowledge graph embeddings +License: MIT +URL: https://pykeen.github.io +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/da/ec/b14d8fec164285a82bdb4739c0eccd8229e4b813a1aa2626a7b416ac0556/pykeen-1.10.1.tar.gz +BuildArch: noarch + +Requires: python3-dataclasses-json +Requires: python3-numpy +Requires: python3-scipy +Requires: python3-click +Requires: python3-click-default-group +Requires: python3-scikit-learn +Requires: python3-tqdm +Requires: python3-requests +Requires: python3-optuna +Requires: python3-pandas +Requires: python3-tabulate +Requires: python3-more-click +Requires: python3-more-itertools +Requires: python3-pystow +Requires: python3-docdata +Requires: python3-class-resolver +Requires: python3-pyyaml +Requires: python3-rexmex +Requires: python3-torch-max-mem +Requires: python3-torch-ppr +Requires: python3-protobuf +Requires: python3-typing-extensions +Requires: python3-torch +Requires: python3-bioregistry +Requires: python3-pyobo +Requires: python3-sphinx +Requires: python3-sphinx-rtd-theme +Requires: python3-sphinx-click +Requires: python3-sphinx-autodoc-typehints +Requires: python3-sphinx-automodapi +Requires: python3-texext +Requires: python3-pytorch-lightning +Requires: python3-mlflow +Requires: python3-neptune-client +Requires: python3-ogb +Requires: python3-opt-einsum +Requires: python3-matplotlib +Requires: python3-seaborn +Requires: python3-jinja2 +Requires: python3-tensorboard +Requires: python3-unittest-templates +Requires: python3-coverage +Requires: python3-pytest +Requires: python3-transformers +Requires: python3-wandb +Requires: python3-wordcloud +Requires: python3-IPython + +%description +<p align="center"> + <img src="docs/source/logo.png" height="150"> +</p> + +<h1 align="center"> + PyKEEN +</h1> + +<p align="center"> + <a href="https://github.com/pykeen/pykeen/actions"> + <img src="https://github.com/pykeen/pykeen/workflows/Tests%20master/badge.svg" + alt="GitHub Actions"> + </a> + + <a href='https://opensource.org/licenses/MIT'> + <img src='https://img.shields.io/badge/License-MIT-blue.svg' alt='License'/> + </a> + + <a href="https://zenodo.org/badge/latestdoi/242672435"> + <img src="https://zenodo.org/badge/242672435.svg" alt="DOI"> + </a> + + <a href="https://optuna.org"> + <img src="https://img.shields.io/badge/Optuna-integrated-blue" alt="Optuna integrated" height="20"> + </a> + + <a href="https://pytorchlightning.ai"> + <img src="https://img.shields.io/badge/-Lightning-792ee5?logo=pytorchlightning&logoColor=white" alt="PyTorch Lightning"> + </a> + + <a href="https://github.com/psf/black"> + <img src="https://img.shields.io/badge/code%20style-black-000000.svg" alt="Code style: black"> + </a> + + <a href=".github/CODE_OF_CONDUCT.md"> + <img src="https://img.shields.io/badge/Contributor%20Covenant-2.1-4baaaa.svg" alt="Contributor Covenant"> + </a> +</p> + +<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 (36)</a> • + <a href="#inductive-datasets">Inductive Datasets (5)</a> • + <a href="#models">Models (44)</a> • + <a href="#supporters">Support</a> • + <a href="#citation">Citation</a> +</p> + +## Installation   + +The latest stable version of PyKEEN requires Python 3.8+. 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 36 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) | 45085 | 51 | 5088433 | +| 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 | +| 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) | + +### Models + +The following 44 models are implemented by PyKEEN. + +| Name | Model | Interaction | Citation | +|--------------------------------|---------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------| +| AutoSF | [`pykeen.models.AutoSF`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.AutoSF.html) | [`pykeen.nn.AutoSFInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.AutoSFInteraction.html) | [Zhang *et al.*, 2020](https://arxiv.org/abs/1904.11682) | +| BoxE | [`pykeen.models.BoxE`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.BoxE.html) | [`pykeen.nn.BoxEInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.BoxEInteraction.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.CP.html) | [`pykeen.nn.CPInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.CPInteraction.html) | [Lacroix *et al.*, 2018](https://arxiv.org/abs/1806.07297) | +| CompGCN | [`pykeen.models.CompGCN`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.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.ComplEx.html) | [`pykeen.nn.ComplExInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.ComplExInteraction.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.ComplExLiteral.html) | [`pykeen.nn.ComplExInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.ComplExInteraction.html) | [Kristiadi *et al.*, 2018](https://arxiv.org/abs/1802.00934) | +| ConvE | [`pykeen.models.ConvE`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.ConvE.html) | [`pykeen.nn.ConvEInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.ConvEInteraction.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.ConvKB.html) | [`pykeen.nn.ConvKBInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.ConvKBInteraction.html) | [Nguyen *et al.*, 2018](https://www.aclweb.org/anthology/N18-2053) | +| CooccurrenceFilteredModel | [`pykeen.models.CooccurrenceFilteredModel`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.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.CrossE.html) | [`pykeen.nn.CrossEInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.CrossEInteraction.html) | [Zhang *et al.*, 2019](https://arxiv.org/abs/1903.04750) | +| DistMA | [`pykeen.models.DistMA`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.DistMA.html) | [`pykeen.nn.DistMAInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.DistMAInteraction.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.DistMult.html) | [`pykeen.nn.DistMultInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.DistMultInteraction.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.DistMultLiteral.html) | [`pykeen.nn.DistMultInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.DistMultInteraction.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.DistMultLiteralGated.html) | [`pykeen.nn.DistMultInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.DistMultInteraction.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.ERMLP.html) | [`pykeen.nn.ERMLPInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.ERMLPInteraction.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.ERMLPE.html) | [`pykeen.nn.ERMLPEInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.ERMLPEInteraction.html) | [Sharifzadeh *et al.*, 2019](https://github.com/pykeen/pykeen) | +| Fixed Model | [`pykeen.models.FixedModel`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.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.HolE.html) | [`pykeen.nn.HolEInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.HolEInteraction.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.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.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.KG2E.html) | [`pykeen.nn.KG2EInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.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) | +| MuRE | [`pykeen.models.MuRE`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.MuRE.html) | [`pykeen.nn.MuREInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.MuREInteraction.html) | [Balažević *et al.*, 2019](https://arxiv.org/abs/1905.09791) | +| 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) | +| NTN | [`pykeen.models.NTN`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.NTN.html) | [`pykeen.nn.NTNInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.NTNInteraction.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.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.PairRE.html) | [`pykeen.nn.PairREInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.PairREInteraction.html) | [Chao *et al.*, 2020](http://arxiv.org/abs/2011.03798) | +| ProjE | [`pykeen.models.ProjE`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.ProjE.html) | [`pykeen.nn.ProjEInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.ProjEInteraction.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.QuatE.html) | [`pykeen.nn.QuatEInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.QuatEInteraction.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.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.RESCAL.html) | [`pykeen.nn.RESCALInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.RESCALInteraction.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.RotatE.html) | [`pykeen.nn.RotatEInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.RotatEInteraction.html) | [Sun *et al.*, 2019](https://arxiv.org/abs/1902.10197v1) | +| SimplE | [`pykeen.models.SimplE`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.SimplE.html) | [`pykeen.nn.SimplEInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.SimplEInteraction.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.SE.html) | [`pykeen.nn.SEInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.SEInteraction.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.TorusE.html) | [`pykeen.nn.TorusEInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.TorusEInteraction.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.TransD.html) | [`pykeen.nn.TransDInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.TransDInteraction.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.TransE.html) | [`pykeen.nn.TransEInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.TransEInteraction.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.TransF.html) | [`pykeen.nn.TransFInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.TransFInteraction.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.TransH.html) | [`pykeen.nn.TransHInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.TransHInteraction.html) | [Wang *et al.*, 2014](https://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/viewFile/8531/8546) | +| TransR | [`pykeen.models.TransR`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.TransR.html) | [`pykeen.nn.TransRInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.TransRInteraction.html) | [Lin *et al.*, 2015](http://www.aaai.org/ocs/index.php/AAAI/AAAI15/paper/download/9571/9523/) | +| 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) | +| 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.models.TuckER`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.TuckER.html) | [`pykeen.nn.TuckerInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.TuckerInteraction.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.UM.html) | [`pykeen.nn.UMInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.UMInteraction.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) | | + +### 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 | +|------------------------------------------------------------------------------------------------------------------------|---------------------|-------------|-------------------------------------------------------------|----------------| +| [AUC-ROC](https://en.wikipedia.org/wiki/Receiver_operating_characteristic) | [0, 1] | 📈 | Area Under the ROC Curve | Classification | +| [Accuracy](https://en.wikipedia.org/wiki/Accuracy) | [0, 1] | 📈 | (TP + TN) / (TP + TN + FP + FN) | Classification | +| [Average Precision](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.average_precision_score.html) | [0, 1] | 📈 | A summary statistic over the precision-recall curve | Classification | +| [Balanced Accuracy](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.balanced_accuracy_score.html) | [0, 1] | 📈 | An adjusted version of the accuracy for imbalanced datasets | Classification | +| [Diagnostic Odds Ratio](https://en.wikipedia.org/wiki/Diagnostic_odds_ratio) | [0, ∞) | 📈 | LR+/LR- | Classification | +| [F1 Score](https://en.wikipedia.org/wiki/F1_score) | [0, 1] | 📈 | 2TP / (2TP + FP + FN) | Classification | +| [False Discovery Rate](https://en.wikipedia.org/wiki/False_discovery_rate) | [0, 1] | 📉 | FP / (FP + TP) | Classification | +| [False Negative Rate](https://en.wikipedia.org/wiki/Type_I_and_type_II_errors#False_positive_and_false_negative_rates) | [0, 1] | 📉 | FN / (FN + TP) | Classification | +| [False Omission Rate](https://en.wikipedia.org/wiki/Positive_and_negative_predictive_values) | [0, 1] | 📉 | FN / (FN + TN) | Classification | +| [False Positive Rate](https://en.wikipedia.org/wiki/False_positive_rate) | [0, 1] | 📉 | FP / (FP + TN) | Classification | +| [Fowlkes Mallows Index](https://en.wikipedia.org/wiki/Fowlkes%E2%80%93Mallows_index) | [0, 1] | 📈 | √PPV x √TPR | Classification | +| [Informedness](https://en.wikipedia.org/wiki/Informedness) | [0, 1] | 📈 | TPR + TNR - 1 | Classification | +| [Markedness](https://en.wikipedia.org/wiki/Markedness) | [0, 1] | 📈 | PPV + NPV - 1 | Classification | +| [Matthews Correlation Coefficient](https://en.wikipedia.org/wiki/Phi_coefficient) | [-1, 1] | 📈 | A balanced measure applicable even with class imbalance | Classification | +| [Negative Likelihood Ratio](https://en.wikipedia.org/wiki/Negative_likelihood_ratio) | [0, ∞) | 📉 | FNR / TNR | Classification | +| [Negative Predictive Value](https://en.wikipedia.org/wiki/Negative_predictive_value) | [0, 1] | 📈 | TN / (TN + FN) | Classification | +| [Positive Likelihood Ratio](https://en.wikipedia.org/wiki/Positive_likelihood_ratio) | [0, ∞) | 📈 | TPR / FPR | Classification | +| [Positive Predictive Value](https://en.wikipedia.org/wiki/Positive_predictive_value) | [0, 1] | 📈 | TP / (TP + FP) | Classification | +| [Prevalence Threshold](https://en.wikipedia.org/wiki/Prevalence_threshold) | [0, 1] | 📉 | √FPR / (√TPR + √FPR) | Classification | +| [Threat Score](https://rexmex.readthedocs.io/en/latest/modules/root.html#rexmex.metrics.classification.threat_score) | [0, 1] | 📈 | TP / (TP + FN + FP) | Classification | +| [True Negative Rate](https://en.wikipedia.org/wiki/Specificity_(tests)) | [0, 1] | 📈 | TN / (TN + FP) | Classification | +| [True Positive Rate](https://en.wikipedia.org/wiki/Sensitivity_(test)) | [0, 1] | 📈 | TP / (TP + FN) | 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) | (-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) | (-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) | (-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} +} +``` + + +%package -n python3-pykeen +Summary: A package for training and evaluating multimodal knowledge graph embeddings +Provides: python-pykeen +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-pykeen +<p align="center"> + <img src="docs/source/logo.png" height="150"> +</p> + +<h1 align="center"> + PyKEEN +</h1> + +<p align="center"> + <a href="https://github.com/pykeen/pykeen/actions"> + <img src="https://github.com/pykeen/pykeen/workflows/Tests%20master/badge.svg" + alt="GitHub Actions"> + </a> + + <a href='https://opensource.org/licenses/MIT'> + <img src='https://img.shields.io/badge/License-MIT-blue.svg' alt='License'/> + </a> + + <a href="https://zenodo.org/badge/latestdoi/242672435"> + <img src="https://zenodo.org/badge/242672435.svg" alt="DOI"> + </a> + + <a href="https://optuna.org"> + <img src="https://img.shields.io/badge/Optuna-integrated-blue" alt="Optuna integrated" height="20"> + </a> + + <a href="https://pytorchlightning.ai"> + <img src="https://img.shields.io/badge/-Lightning-792ee5?logo=pytorchlightning&logoColor=white" alt="PyTorch Lightning"> + </a> + + <a href="https://github.com/psf/black"> + <img src="https://img.shields.io/badge/code%20style-black-000000.svg" alt="Code style: black"> + </a> + + <a href=".github/CODE_OF_CONDUCT.md"> + <img src="https://img.shields.io/badge/Contributor%20Covenant-2.1-4baaaa.svg" alt="Contributor Covenant"> + </a> +</p> + +<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 (36)</a> • + <a href="#inductive-datasets">Inductive Datasets (5)</a> • + <a href="#models">Models (44)</a> • + <a href="#supporters">Support</a> • + <a href="#citation">Citation</a> +</p> + +## Installation   + +The latest stable version of PyKEEN requires Python 3.8+. 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 36 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) | 45085 | 51 | 5088433 | +| 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 | +| 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) | + +### Models + +The following 44 models are implemented by PyKEEN. + +| Name | Model | Interaction | Citation | +|--------------------------------|---------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------| +| AutoSF | [`pykeen.models.AutoSF`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.AutoSF.html) | [`pykeen.nn.AutoSFInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.AutoSFInteraction.html) | [Zhang *et al.*, 2020](https://arxiv.org/abs/1904.11682) | +| BoxE | [`pykeen.models.BoxE`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.BoxE.html) | [`pykeen.nn.BoxEInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.BoxEInteraction.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.CP.html) | [`pykeen.nn.CPInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.CPInteraction.html) | [Lacroix *et al.*, 2018](https://arxiv.org/abs/1806.07297) | +| CompGCN | [`pykeen.models.CompGCN`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.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.ComplEx.html) | [`pykeen.nn.ComplExInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.ComplExInteraction.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.ComplExLiteral.html) | [`pykeen.nn.ComplExInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.ComplExInteraction.html) | [Kristiadi *et al.*, 2018](https://arxiv.org/abs/1802.00934) | +| ConvE | [`pykeen.models.ConvE`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.ConvE.html) | [`pykeen.nn.ConvEInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.ConvEInteraction.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.ConvKB.html) | [`pykeen.nn.ConvKBInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.ConvKBInteraction.html) | [Nguyen *et al.*, 2018](https://www.aclweb.org/anthology/N18-2053) | +| CooccurrenceFilteredModel | [`pykeen.models.CooccurrenceFilteredModel`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.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.CrossE.html) | [`pykeen.nn.CrossEInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.CrossEInteraction.html) | [Zhang *et al.*, 2019](https://arxiv.org/abs/1903.04750) | +| DistMA | [`pykeen.models.DistMA`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.DistMA.html) | [`pykeen.nn.DistMAInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.DistMAInteraction.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.DistMult.html) | [`pykeen.nn.DistMultInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.DistMultInteraction.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.DistMultLiteral.html) | [`pykeen.nn.DistMultInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.DistMultInteraction.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.DistMultLiteralGated.html) | [`pykeen.nn.DistMultInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.DistMultInteraction.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.ERMLP.html) | [`pykeen.nn.ERMLPInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.ERMLPInteraction.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.ERMLPE.html) | [`pykeen.nn.ERMLPEInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.ERMLPEInteraction.html) | [Sharifzadeh *et al.*, 2019](https://github.com/pykeen/pykeen) | +| Fixed Model | [`pykeen.models.FixedModel`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.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.HolE.html) | [`pykeen.nn.HolEInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.HolEInteraction.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.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.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.KG2E.html) | [`pykeen.nn.KG2EInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.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) | +| MuRE | [`pykeen.models.MuRE`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.MuRE.html) | [`pykeen.nn.MuREInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.MuREInteraction.html) | [Balažević *et al.*, 2019](https://arxiv.org/abs/1905.09791) | +| 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) | +| NTN | [`pykeen.models.NTN`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.NTN.html) | [`pykeen.nn.NTNInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.NTNInteraction.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.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.PairRE.html) | [`pykeen.nn.PairREInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.PairREInteraction.html) | [Chao *et al.*, 2020](http://arxiv.org/abs/2011.03798) | +| ProjE | [`pykeen.models.ProjE`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.ProjE.html) | [`pykeen.nn.ProjEInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.ProjEInteraction.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.QuatE.html) | [`pykeen.nn.QuatEInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.QuatEInteraction.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.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.RESCAL.html) | [`pykeen.nn.RESCALInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.RESCALInteraction.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.RotatE.html) | [`pykeen.nn.RotatEInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.RotatEInteraction.html) | [Sun *et al.*, 2019](https://arxiv.org/abs/1902.10197v1) | +| SimplE | [`pykeen.models.SimplE`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.SimplE.html) | [`pykeen.nn.SimplEInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.SimplEInteraction.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.SE.html) | [`pykeen.nn.SEInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.SEInteraction.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.TorusE.html) | [`pykeen.nn.TorusEInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.TorusEInteraction.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.TransD.html) | [`pykeen.nn.TransDInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.TransDInteraction.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.TransE.html) | [`pykeen.nn.TransEInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.TransEInteraction.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.TransF.html) | [`pykeen.nn.TransFInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.TransFInteraction.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.TransH.html) | [`pykeen.nn.TransHInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.TransHInteraction.html) | [Wang *et al.*, 2014](https://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/viewFile/8531/8546) | +| TransR | [`pykeen.models.TransR`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.TransR.html) | [`pykeen.nn.TransRInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.TransRInteraction.html) | [Lin *et al.*, 2015](http://www.aaai.org/ocs/index.php/AAAI/AAAI15/paper/download/9571/9523/) | +| 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) | +| 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.models.TuckER`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.TuckER.html) | [`pykeen.nn.TuckerInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.TuckerInteraction.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.UM.html) | [`pykeen.nn.UMInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.UMInteraction.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) | | + +### 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 | +|------------------------------------------------------------------------------------------------------------------------|---------------------|-------------|-------------------------------------------------------------|----------------| +| [AUC-ROC](https://en.wikipedia.org/wiki/Receiver_operating_characteristic) | [0, 1] | 📈 | Area Under the ROC Curve | Classification | +| [Accuracy](https://en.wikipedia.org/wiki/Accuracy) | [0, 1] | 📈 | (TP + TN) / (TP + TN + FP + FN) | Classification | +| [Average Precision](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.average_precision_score.html) | [0, 1] | 📈 | A summary statistic over the precision-recall curve | Classification | +| [Balanced Accuracy](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.balanced_accuracy_score.html) | [0, 1] | 📈 | An adjusted version of the accuracy for imbalanced datasets | Classification | +| [Diagnostic Odds Ratio](https://en.wikipedia.org/wiki/Diagnostic_odds_ratio) | [0, ∞) | 📈 | LR+/LR- | Classification | +| [F1 Score](https://en.wikipedia.org/wiki/F1_score) | [0, 1] | 📈 | 2TP / (2TP + FP + FN) | Classification | +| [False Discovery Rate](https://en.wikipedia.org/wiki/False_discovery_rate) | [0, 1] | 📉 | FP / (FP + TP) | Classification | +| [False Negative Rate](https://en.wikipedia.org/wiki/Type_I_and_type_II_errors#False_positive_and_false_negative_rates) | [0, 1] | 📉 | FN / (FN + TP) | Classification | +| [False Omission Rate](https://en.wikipedia.org/wiki/Positive_and_negative_predictive_values) | [0, 1] | 📉 | FN / (FN + TN) | Classification | +| [False Positive Rate](https://en.wikipedia.org/wiki/False_positive_rate) | [0, 1] | 📉 | FP / (FP + TN) | Classification | +| [Fowlkes Mallows Index](https://en.wikipedia.org/wiki/Fowlkes%E2%80%93Mallows_index) | [0, 1] | 📈 | √PPV x √TPR | Classification | +| [Informedness](https://en.wikipedia.org/wiki/Informedness) | [0, 1] | 📈 | TPR + TNR - 1 | Classification | +| [Markedness](https://en.wikipedia.org/wiki/Markedness) | [0, 1] | 📈 | PPV + NPV - 1 | Classification | +| [Matthews Correlation Coefficient](https://en.wikipedia.org/wiki/Phi_coefficient) | [-1, 1] | 📈 | A balanced measure applicable even with class imbalance | Classification | +| [Negative Likelihood Ratio](https://en.wikipedia.org/wiki/Negative_likelihood_ratio) | [0, ∞) | 📉 | FNR / TNR | Classification | +| [Negative Predictive Value](https://en.wikipedia.org/wiki/Negative_predictive_value) | [0, 1] | 📈 | TN / (TN + FN) | Classification | +| [Positive Likelihood Ratio](https://en.wikipedia.org/wiki/Positive_likelihood_ratio) | [0, ∞) | 📈 | TPR / FPR | Classification | +| [Positive Predictive Value](https://en.wikipedia.org/wiki/Positive_predictive_value) | [0, 1] | 📈 | TP / (TP + FP) | Classification | +| [Prevalence Threshold](https://en.wikipedia.org/wiki/Prevalence_threshold) | [0, 1] | 📉 | √FPR / (√TPR + √FPR) | Classification | +| [Threat Score](https://rexmex.readthedocs.io/en/latest/modules/root.html#rexmex.metrics.classification.threat_score) | [0, 1] | 📈 | TP / (TP + FN + FP) | Classification | +| [True Negative Rate](https://en.wikipedia.org/wiki/Specificity_(tests)) | [0, 1] | 📈 | TN / (TN + FP) | Classification | +| [True Positive Rate](https://en.wikipedia.org/wiki/Sensitivity_(test)) | [0, 1] | 📈 | TP / (TP + FN) | 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) | (-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) | (-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) | (-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} +} +``` + + +%package help +Summary: Development documents and examples for pykeen +Provides: python3-pykeen-doc +%description help +<p align="center"> + <img src="docs/source/logo.png" height="150"> +</p> + +<h1 align="center"> + PyKEEN +</h1> + +<p align="center"> + <a href="https://github.com/pykeen/pykeen/actions"> + <img src="https://github.com/pykeen/pykeen/workflows/Tests%20master/badge.svg" + alt="GitHub Actions"> + </a> + + <a href='https://opensource.org/licenses/MIT'> + <img src='https://img.shields.io/badge/License-MIT-blue.svg' alt='License'/> + </a> + + <a href="https://zenodo.org/badge/latestdoi/242672435"> + <img src="https://zenodo.org/badge/242672435.svg" alt="DOI"> + </a> + + <a href="https://optuna.org"> + <img src="https://img.shields.io/badge/Optuna-integrated-blue" alt="Optuna integrated" height="20"> + </a> + + <a href="https://pytorchlightning.ai"> + <img src="https://img.shields.io/badge/-Lightning-792ee5?logo=pytorchlightning&logoColor=white" alt="PyTorch Lightning"> + </a> + + <a href="https://github.com/psf/black"> + <img src="https://img.shields.io/badge/code%20style-black-000000.svg" alt="Code style: black"> + </a> + + <a href=".github/CODE_OF_CONDUCT.md"> + <img src="https://img.shields.io/badge/Contributor%20Covenant-2.1-4baaaa.svg" alt="Contributor Covenant"> + </a> +</p> + +<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 (36)</a> • + <a href="#inductive-datasets">Inductive Datasets (5)</a> • + <a href="#models">Models (44)</a> • + <a href="#supporters">Support</a> • + <a href="#citation">Citation</a> +</p> + +## Installation   + +The latest stable version of PyKEEN requires Python 3.8+. 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 36 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) | 45085 | 51 | 5088433 | +| 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 | +| 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) | + +### Models + +The following 44 models are implemented by PyKEEN. + +| Name | Model | Interaction | Citation | +|--------------------------------|---------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------| +| AutoSF | [`pykeen.models.AutoSF`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.AutoSF.html) | [`pykeen.nn.AutoSFInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.AutoSFInteraction.html) | [Zhang *et al.*, 2020](https://arxiv.org/abs/1904.11682) | +| BoxE | [`pykeen.models.BoxE`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.BoxE.html) | [`pykeen.nn.BoxEInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.BoxEInteraction.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.CP.html) | [`pykeen.nn.CPInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.CPInteraction.html) | [Lacroix *et al.*, 2018](https://arxiv.org/abs/1806.07297) | +| CompGCN | [`pykeen.models.CompGCN`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.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.ComplEx.html) | [`pykeen.nn.ComplExInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.ComplExInteraction.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.ComplExLiteral.html) | [`pykeen.nn.ComplExInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.ComplExInteraction.html) | [Kristiadi *et al.*, 2018](https://arxiv.org/abs/1802.00934) | +| ConvE | [`pykeen.models.ConvE`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.ConvE.html) | [`pykeen.nn.ConvEInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.ConvEInteraction.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.ConvKB.html) | [`pykeen.nn.ConvKBInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.ConvKBInteraction.html) | [Nguyen *et al.*, 2018](https://www.aclweb.org/anthology/N18-2053) | +| CooccurrenceFilteredModel | [`pykeen.models.CooccurrenceFilteredModel`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.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.CrossE.html) | [`pykeen.nn.CrossEInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.CrossEInteraction.html) | [Zhang *et al.*, 2019](https://arxiv.org/abs/1903.04750) | +| DistMA | [`pykeen.models.DistMA`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.DistMA.html) | [`pykeen.nn.DistMAInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.DistMAInteraction.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.DistMult.html) | [`pykeen.nn.DistMultInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.DistMultInteraction.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.DistMultLiteral.html) | [`pykeen.nn.DistMultInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.DistMultInteraction.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.DistMultLiteralGated.html) | [`pykeen.nn.DistMultInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.DistMultInteraction.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.ERMLP.html) | [`pykeen.nn.ERMLPInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.ERMLPInteraction.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.ERMLPE.html) | [`pykeen.nn.ERMLPEInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.ERMLPEInteraction.html) | [Sharifzadeh *et al.*, 2019](https://github.com/pykeen/pykeen) | +| Fixed Model | [`pykeen.models.FixedModel`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.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.HolE.html) | [`pykeen.nn.HolEInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.HolEInteraction.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.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.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.KG2E.html) | [`pykeen.nn.KG2EInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.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) | +| MuRE | [`pykeen.models.MuRE`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.MuRE.html) | [`pykeen.nn.MuREInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.MuREInteraction.html) | [Balažević *et al.*, 2019](https://arxiv.org/abs/1905.09791) | +| 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) | +| NTN | [`pykeen.models.NTN`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.NTN.html) | [`pykeen.nn.NTNInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.NTNInteraction.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.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.PairRE.html) | [`pykeen.nn.PairREInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.PairREInteraction.html) | [Chao *et al.*, 2020](http://arxiv.org/abs/2011.03798) | +| ProjE | [`pykeen.models.ProjE`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.ProjE.html) | [`pykeen.nn.ProjEInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.ProjEInteraction.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.QuatE.html) | [`pykeen.nn.QuatEInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.QuatEInteraction.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.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.RESCAL.html) | [`pykeen.nn.RESCALInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.RESCALInteraction.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.RotatE.html) | [`pykeen.nn.RotatEInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.RotatEInteraction.html) | [Sun *et al.*, 2019](https://arxiv.org/abs/1902.10197v1) | +| SimplE | [`pykeen.models.SimplE`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.SimplE.html) | [`pykeen.nn.SimplEInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.SimplEInteraction.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.SE.html) | [`pykeen.nn.SEInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.SEInteraction.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.TorusE.html) | [`pykeen.nn.TorusEInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.TorusEInteraction.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.TransD.html) | [`pykeen.nn.TransDInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.TransDInteraction.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.TransE.html) | [`pykeen.nn.TransEInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.TransEInteraction.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.TransF.html) | [`pykeen.nn.TransFInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.TransFInteraction.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.TransH.html) | [`pykeen.nn.TransHInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.TransHInteraction.html) | [Wang *et al.*, 2014](https://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/viewFile/8531/8546) | +| TransR | [`pykeen.models.TransR`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.TransR.html) | [`pykeen.nn.TransRInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.TransRInteraction.html) | [Lin *et al.*, 2015](http://www.aaai.org/ocs/index.php/AAAI/AAAI15/paper/download/9571/9523/) | +| 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) | +| 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.models.TuckER`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.TuckER.html) | [`pykeen.nn.TuckerInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.TuckerInteraction.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.UM.html) | [`pykeen.nn.UMInteraction`](https://pykeen.readthedocs.io/en/latest/api/pykeen.nn.UMInteraction.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) | | + +### 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 | +|------------------------------------------------------------------------------------------------------------------------|---------------------|-------------|-------------------------------------------------------------|----------------| +| [AUC-ROC](https://en.wikipedia.org/wiki/Receiver_operating_characteristic) | [0, 1] | 📈 | Area Under the ROC Curve | Classification | +| [Accuracy](https://en.wikipedia.org/wiki/Accuracy) | [0, 1] | 📈 | (TP + TN) / (TP + TN + FP + FN) | Classification | +| [Average Precision](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.average_precision_score.html) | [0, 1] | 📈 | A summary statistic over the precision-recall curve | Classification | +| [Balanced Accuracy](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.balanced_accuracy_score.html) | [0, 1] | 📈 | An adjusted version of the accuracy for imbalanced datasets | Classification | +| [Diagnostic Odds Ratio](https://en.wikipedia.org/wiki/Diagnostic_odds_ratio) | [0, ∞) | 📈 | LR+/LR- | Classification | +| [F1 Score](https://en.wikipedia.org/wiki/F1_score) | [0, 1] | 📈 | 2TP / (2TP + FP + FN) | Classification | +| [False Discovery Rate](https://en.wikipedia.org/wiki/False_discovery_rate) | [0, 1] | 📉 | FP / (FP + TP) | Classification | +| [False Negative Rate](https://en.wikipedia.org/wiki/Type_I_and_type_II_errors#False_positive_and_false_negative_rates) | [0, 1] | 📉 | FN / (FN + TP) | Classification | +| [False Omission Rate](https://en.wikipedia.org/wiki/Positive_and_negative_predictive_values) | [0, 1] | 📉 | FN / (FN + TN) | Classification | +| [False Positive Rate](https://en.wikipedia.org/wiki/False_positive_rate) | [0, 1] | 📉 | FP / (FP + TN) | Classification | +| [Fowlkes Mallows Index](https://en.wikipedia.org/wiki/Fowlkes%E2%80%93Mallows_index) | [0, 1] | 📈 | √PPV x √TPR | Classification | +| [Informedness](https://en.wikipedia.org/wiki/Informedness) | [0, 1] | 📈 | TPR + TNR - 1 | Classification | +| [Markedness](https://en.wikipedia.org/wiki/Markedness) | [0, 1] | 📈 | PPV + NPV - 1 | Classification | +| [Matthews Correlation Coefficient](https://en.wikipedia.org/wiki/Phi_coefficient) | [-1, 1] | 📈 | A balanced measure applicable even with class imbalance | Classification | +| [Negative Likelihood Ratio](https://en.wikipedia.org/wiki/Negative_likelihood_ratio) | [0, ∞) | 📉 | FNR / TNR | Classification | +| [Negative Predictive Value](https://en.wikipedia.org/wiki/Negative_predictive_value) | [0, 1] | 📈 | TN / (TN + FN) | Classification | +| [Positive Likelihood Ratio](https://en.wikipedia.org/wiki/Positive_likelihood_ratio) | [0, ∞) | 📈 | TPR / FPR | Classification | +| [Positive Predictive Value](https://en.wikipedia.org/wiki/Positive_predictive_value) | [0, 1] | 📈 | TP / (TP + FP) | Classification | +| [Prevalence Threshold](https://en.wikipedia.org/wiki/Prevalence_threshold) | [0, 1] | 📉 | √FPR / (√TPR + √FPR) | Classification | +| [Threat Score](https://rexmex.readthedocs.io/en/latest/modules/root.html#rexmex.metrics.classification.threat_score) | [0, 1] | 📈 | TP / (TP + FN + FP) | Classification | +| [True Negative Rate](https://en.wikipedia.org/wiki/Specificity_(tests)) | [0, 1] | 📈 | TN / (TN + FP) | Classification | +| [True Positive Rate](https://en.wikipedia.org/wiki/Sensitivity_(test)) | [0, 1] | 📈 | TP / (TP + FN) | 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) | (-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) | (-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) | (-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} +} +``` + + +%prep +%autosetup -n pykeen-1.10.1 + +%build +%py3_build + +%install +%py3_install +install -d -m755 %{buildroot}/%{_pkgdocdir} +if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi +if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi +if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi +if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi +pushd %{buildroot} +if [ -d usr/lib ]; then + find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/lib64 ]; then + find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/bin ]; then + find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/sbin ]; then + find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst +fi +touch doclist.lst +if [ -d usr/share/man ]; then + find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst +fi +popd +mv %{buildroot}/filelist.lst . +mv %{buildroot}/doclist.lst . + +%files -n python3-pykeen -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Mon May 29 2023 Python_Bot <Python_Bot@openeuler.org> - 1.10.1-1 +- Package Spec generated |