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PyKEEN

GitHub Actions License DOI Optuna integrated PyTorch Lightning Code style: black Contributor Covenant

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

Installation Quickstart Datasets (36) Inductive Datasets (5) Models (44) Support Citation

Installation PyPI - Python Version PyPI

The latest stable version of PyKEEN requires Python 3.8+. It can be downloaded and installed from PyPI with:

pip install pykeen

The latest version of PyKEEN can be installed directly from the source code on GitHub with:

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.

Quickstart Documentation Status

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 model on the Nations dataset. By default, the training loop uses the stochastic local closed world assumption (sLCWA) training approach and evaluates with rank-based evaluation.

from pykeen.pipeline import pipeline

result = pipeline(
    model='TransE',
    dataset='nations',
)

The results are returned in an instance of the PipelineResult dataclass that has attributes for the trained model, the training loop, the evaluation, and more. See the tutorials on using your own dataset, understanding the evaluation, and making novel link predictions.

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 tutorial. If you have a suggestion for another dataset to include in PyKEEN, please let us know here.

Name Documentation Citation Entities Relations Triples
Aristo-v4 pykeen.datasets.AristoV4 Chen et al., 2021 42016 1593 279425
BioKG pykeen.datasets.BioKG Walsh et al., 2019 105524 17 2067997
Clinical Knowledge Graph pykeen.datasets.CKG Santos et al., 2020 7617419 11 26691525
CN3l Family pykeen.datasets.CN3l Chen et al., 2017 3206 42 21777
CoDEx (large) pykeen.datasets.CoDExLarge Safavi et al., 2020 77951 69 612437
CoDEx (medium) pykeen.datasets.CoDExMedium Safavi et al., 2020 17050 51 206205
CoDEx (small) pykeen.datasets.CoDExSmall Safavi et al., 2020 2034 42 36543
ConceptNet pykeen.datasets.ConceptNet Speer et al., 2017 28370083 50 34074917
Countries pykeen.datasets.Countries Bouchard et al., 2015 271 2 1158
Commonsense Knowledge Graph pykeen.datasets.CSKG Ilievski et al., 2020 2087833 58 4598728
DB100K pykeen.datasets.DB100K Ding et al., 2018 99604 470 697479
DBpedia50 pykeen.datasets.DBpedia50 Shi et al., 2017 24624 351 34421
Drug Repositioning Knowledge Graph pykeen.datasets.DRKG gnn4dr/DRKG 97238 107 5874257
FB15k pykeen.datasets.FB15k Bordes et al., 2013 14951 1345 592213
FB15k-237 pykeen.datasets.FB15k237 Toutanova et al., 2015 14505 237 310079
Global Biotic Interactions pykeen.datasets.Globi Poelen et al., 2014 404207 39 1966385
Hetionet pykeen.datasets.Hetionet Himmelstein et al., 2017 45158 24 2250197
Kinships pykeen.datasets.Kinships Kemp et al., 2006 104 25 10686
Nations pykeen.datasets.Nations ZhenfengLei/KGDatasets 14 55 1992
NationsL pykeen.datasets.NationsLiteral pykeen/pykeen 14 55 1992
OGB BioKG pykeen.datasets.OGBBioKG Hu et al., 2020 45085 51 5088433
OGB WikiKG2 pykeen.datasets.OGBWikiKG2 Hu et al., 2020 2500604 535 17137181
OpenBioLink pykeen.datasets.OpenBioLink Breit et al., 2020 180992 28 4563407
OpenBioLink LQ pykeen.datasets.OpenBioLinkLQ Breit et al., 2020 480876 32 27320889
OpenEA Family pykeen.datasets.OpenEA Sun et al., 2020 15000 248 38265
PharmKG pykeen.datasets.PharmKG Zheng et al., 2020 188296 39 1093236
PharmKG8k pykeen.datasets.PharmKG8k Zheng et al., 2020 7247 28 485787
PrimeKG pykeen.datasets.PrimeKG Chandak et al., 2022 129375 30 8100498
Unified Medical Language System pykeen.datasets.UMLS ZhenfengLei/KGDatasets 135 46 6529
WD50K (triples) pykeen.datasets.WD50KT Galkin et al., 2020 40107 473 232344
Wikidata5M pykeen.datasets.Wikidata5M Wang et al., 2019 4594149 822 20624239
WK3l-120k Family pykeen.datasets.WK3l120k Chen et al., 2017 119748 3109 1375406
WK3l-15k Family pykeen.datasets.WK3l15k Chen et al., 2017 15126 1841 209041
WordNet-18 pykeen.datasets.WN18 Bordes et al., 2014 40943 18 151442
WordNet-18 (RR) pykeen.datasets.WN18RR Toutanova et al., 2015 40559 11 92583
YAGO3-10 pykeen.datasets.YAGO310 Mahdisoltani et al., 2015 123143 37 1089000

Inductive Datasets

The following 5 inductive datasets are built in to PyKEEN.

Name Documentation Citation
ILPC2022 Large pykeen.datasets.ILPC2022Large Galkin et al., 2022
ILPC2022 Small pykeen.datasets.ILPC2022Small Galkin et al., 2022
FB15k-237 pykeen.datasets.InductiveFB15k237 Teru et al., 2020
NELL pykeen.datasets.InductiveNELL Teru et al., 2020
WordNet-18 (RR) pykeen.datasets.InductiveWN18RR Teru et al., 2020

Models

The following 44 models are implemented by PyKEEN.

Name Model Interaction Citation
AutoSF pykeen.models.AutoSF pykeen.nn.AutoSFInteraction Zhang et al., 2020
BoxE pykeen.models.BoxE pykeen.nn.BoxEInteraction Abboud et al., 2020
Canonical Tensor Decomposition pykeen.models.CP pykeen.nn.CPInteraction Lacroix et al., 2018
CompGCN pykeen.models.CompGCN Vashishth et al., 2020
ComplEx pykeen.models.ComplEx pykeen.nn.ComplExInteraction Trouillon et al., 2016
ComplEx Literal pykeen.models.ComplExLiteral pykeen.nn.ComplExInteraction Kristiadi et al., 2018
ConvE pykeen.models.ConvE pykeen.nn.ConvEInteraction Dettmers et al., 2018
ConvKB pykeen.models.ConvKB pykeen.nn.ConvKBInteraction Nguyen et al., 2018
CooccurrenceFilteredModel pykeen.models.CooccurrenceFilteredModel Berrendorf et al., 2022
CrossE pykeen.models.CrossE pykeen.nn.CrossEInteraction Zhang et al., 2019
DistMA pykeen.models.DistMA pykeen.nn.DistMAInteraction Shi et al., 2019
DistMult pykeen.models.DistMult pykeen.nn.DistMultInteraction Yang et al., 2014
DistMult Literal pykeen.models.DistMultLiteral pykeen.nn.DistMultInteraction Kristiadi et al., 2018
DistMult Literal (Gated) pykeen.models.DistMultLiteralGated pykeen.nn.DistMultInteraction Kristiadi et al., 2018
ER-MLP pykeen.models.ERMLP pykeen.nn.ERMLPInteraction Dong et al., 2014
ER-MLP (E) pykeen.models.ERMLPE pykeen.nn.ERMLPEInteraction Sharifzadeh et al., 2019
Fixed Model pykeen.models.FixedModel Berrendorf et al., 2021
HolE pykeen.models.HolE pykeen.nn.HolEInteraction Nickel et al., 2016
InductiveNodePiece pykeen.models.InductiveNodePiece Galkin et al., 2021
InductiveNodePieceGNN pykeen.models.InductiveNodePieceGNN Galkin et al., 2021
KG2E pykeen.models.KG2E pykeen.nn.KG2EInteraction He et al., 2015
LineaRE pykeen.nn.LineaREInteraction Peng et al., 2020
MuRE pykeen.models.MuRE pykeen.nn.MuREInteraction Bala鱉evi et al., 2019
MultiLinearTucker pykeen.nn.MultiLinearTuckerInteraction Tucker et al., 1966
NTN pykeen.models.NTN pykeen.nn.NTNInteraction Socher et al., 2013
NodePiece pykeen.models.NodePiece Galkin et al., 2021
PairRE pykeen.models.PairRE pykeen.nn.PairREInteraction Chao et al., 2020
ProjE pykeen.models.ProjE pykeen.nn.ProjEInteraction Shi et al., 2017
QuatE pykeen.models.QuatE pykeen.nn.QuatEInteraction Zhang et al., 2019
R-GCN pykeen.models.RGCN Schlichtkrull et al., 2018
RESCAL pykeen.models.RESCAL pykeen.nn.RESCALInteraction Nickel et al., 2011
RotatE pykeen.models.RotatE pykeen.nn.RotatEInteraction Sun et al., 2019
SimplE pykeen.models.SimplE pykeen.nn.SimplEInteraction Kazemi et al., 2018
Structured Embedding pykeen.models.SE pykeen.nn.SEInteraction Bordes et al., 2011
TorusE pykeen.models.TorusE pykeen.nn.TorusEInteraction Ebisu et al., 2018
TransD pykeen.models.TransD pykeen.nn.TransDInteraction Ji et al., 2015
TransE pykeen.models.TransE pykeen.nn.TransEInteraction Bordes et al., 2013
TransF pykeen.models.TransF pykeen.nn.TransFInteraction Feng et al., 2016
TransH pykeen.models.TransH pykeen.nn.TransHInteraction Wang et al., 2014
TransR pykeen.models.TransR pykeen.nn.TransRInteraction Lin et al., 2015
Transformer pykeen.nn.TransformerInteraction Galkin et al., 2020
TripleRE pykeen.nn.TripleREInteraction Yu et al., 2021
TuckER pykeen.models.TuckER pykeen.nn.TuckerInteraction Bala鱉evi et al., 2019
Unstructured Model pykeen.models.UM pykeen.nn.UMInteraction Bordes et al., 2014

Losses

The following 15 losses are implemented by PyKEEN.

Name Reference Description
Adversarially weighted binary cross entropy (with logits) pykeen.losses.AdversarialBCEWithLogitsLoss An adversarially weighted BCE loss.
Binary cross entropy (after sigmoid) pykeen.losses.BCEAfterSigmoidLoss The numerically unstable version of explicit Sigmoid + BCE loss.
Binary cross entropy (with logits) pykeen.losses.BCEWithLogitsLoss The binary cross entropy loss.
Cross entropy pykeen.losses.CrossEntropyLoss The cross entropy loss that evaluates the cross entropy after softmax output.
Double Margin pykeen.losses.DoubleMarginLoss A limit-based scoring loss, with separate margins for positive and negative elements from [sun2018]_.
Focal pykeen.losses.FocalLoss The focal loss proposed by [lin2018]_.
InfoNCE loss with additive margin pykeen.losses.InfoNCELoss The InfoNCE loss with additive margin proposed by [wang2022]_.
Margin ranking pykeen.losses.MarginRankingLoss The pairwise hinge loss (i.e., margin ranking loss).
Mean squared error pykeen.losses.MSELoss The mean squared error loss.
Self-adversarial negative sampling pykeen.losses.NSSALoss The self-adversarial negative sampling loss function proposed by [sun2019]_.
Pairwise logistic pykeen.losses.PairwiseLogisticLoss The pairwise logistic loss.
Pointwise Hinge pykeen.losses.PointwiseHingeLoss The pointwise hinge loss.
Soft margin ranking pykeen.losses.SoftMarginRankingLoss The soft pairwise hinge loss (i.e., soft margin ranking loss).
Softplus pykeen.losses.SoftplusLoss The pointwise logistic loss (i.e., softplus loss).
Soft Pointwise Hinge pykeen.losses.SoftPointwiseHingeLoss The soft pointwise hinge loss.

Regularizers

The following 6 regularizers are implemented by PyKEEN.

Name Reference Description
combined pykeen.regularizers.CombinedRegularizer A convex combination of regularizers.
lp pykeen.regularizers.LpRegularizer A simple L_p norm based regularizer.
no pykeen.regularizers.NoRegularizer A regularizer which does not perform any regularization.
normlimit pykeen.regularizers.NormLimitRegularizer A regularizer which formulates a soft constraint on a maximum norm.
orthogonality pykeen.regularizers.OrthogonalityRegularizer A regularizer for the soft orthogonality constraints from [wang2014]_.
powersum pykeen.regularizers.PowerSumRegularizer A simple x^p based regularizer.

Training Loops

The following 3 training loops are implemented in PyKEEN.

Name Reference Description
lcwa pykeen.training.LCWATrainingLoop A training loop that is based upon the local closed world assumption (LCWA).
slcwa pykeen.training.SLCWATrainingLoop A training loop that uses the stochastic local closed world assumption training approach.
symmetriclcwa pykeen.training.SymmetricLCWATrainingLoop

Negative Samplers

The following 3 negative samplers are implemented in PyKEEN.

Name Reference Description
basic pykeen.sampling.BasicNegativeSampler A basic negative sampler.
bernoulli pykeen.sampling.BernoulliNegativeSampler An implementation of the Bernoulli negative sampling approach proposed by [wang2014]_.
pseudotyped pykeen.sampling.PseudoTypedNegativeSampler 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 A harness for early stopping.
nop pykeen.stoppers.NopStopper A stopper that does nothing.

Evaluators

The following 5 evaluators are implemented in PyKEEN.

Name Reference Description
classification pykeen.evaluation.ClassificationEvaluator An evaluator that uses a classification metrics.
macrorankbased pykeen.evaluation.MacroRankBasedEvaluator Macro-average rank-based evaluation.
ogb pykeen.evaluation.OGBEvaluator A sampled, rank-based evaluator that applies a custom OGB evaluation.
rankbased pykeen.evaluation.RankBasedEvaluator A rank-based evaluator for KGE models.
sampledrankbased pykeen.evaluation.SampledRankBasedEvaluator 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 [0, 1] Area Under the ROC Curve Classification
Accuracy [0, 1] (TP + TN) / (TP + TN + FP + FN) Classification
Average Precision [0, 1] A summary statistic over the precision-recall curve Classification
Balanced Accuracy [0, 1] An adjusted version of the accuracy for imbalanced datasets Classification
Diagnostic Odds Ratio [0, ) LR+/LR- Classification
F1 Score [0, 1] 2TP / (2TP + FP + FN) Classification
False Discovery Rate [0, 1] FP / (FP + TP) Classification
False Negative Rate [0, 1] FN / (FN + TP) Classification
False Omission Rate [0, 1] FN / (FN + TN) Classification
False Positive Rate [0, 1] FP / (FP + TN) Classification
Fowlkes Mallows Index [0, 1] PPV x TPR Classification
Informedness [0, 1] TPR + TNR - 1 Classification
Markedness [0, 1] PPV + NPV - 1 Classification
Matthews Correlation Coefficient [-1, 1] A balanced measure applicable even with class imbalance Classification
Negative Likelihood Ratio [0, ) FNR / TNR Classification
Negative Predictive Value [0, 1] TN / (TN + FN) Classification
Positive Likelihood Ratio [0, ) TPR / FPR Classification
Positive Predictive Value [0, 1] TP / (TP + FP) Classification
Prevalence Threshold [0, 1] FPR / (TPR + FPR) Classification
Threat Score [0, 1] TP / (TP + FN + FP) Classification
True Negative Rate [0, 1] TN / (TN + FP) Classification
True Positive Rate [0, 1] TP / (TP + FN) Classification
Adjusted Arithmetic Mean Rank (AAMR) [0, 2) The mean over all ranks divided by its expected value. Ranking
Adjusted Arithmetic Mean Rank Index (AAMRI) [-1, 1] The re-indexed adjusted mean rank (AAMR) Ranking
Adjusted Geometric Mean Rank Index (AGMRI) (-E[f]/(1-E[f]), 1] The re-indexed adjusted geometric mean rank (AGMRI) Ranking
Adjusted Hits at K (-E[f]/(1-E[f]), 1] The re-indexed adjusted hits at K Ranking
Adjusted Inverse Harmonic Mean Rank (-E[f]/(1-E[f]), 1] The re-indexed adjusted MRR Ranking
Geometric Mean Rank (GMR) [1, ) The geometric mean over all ranks. Ranking
Harmonic Mean Rank (HMR) [1, ) The harmonic mean over all ranks. Ranking
Hits @ K [0, 1] The relative frequency of ranks not larger than a given k. Ranking
Inverse Arithmetic Mean Rank (IAMR) (0, 1] The inverse of the arithmetic mean over all ranks. Ranking
Inverse Geometric Mean Rank (IGMR) (0, 1] The inverse of the geometric mean over all ranks. Ranking
Inverse Median Rank (0, 1] The inverse of the median over all ranks. Ranking
Mean Rank (MR) [1, ) The arithmetic mean over all ranks. Ranking
Mean Reciprocal Rank (MRR) (0, 1] The inverse of the harmonic mean over all ranks. Ranking
Median Rank [1, ) The median over all ranks. Ranking
z-Geometric Mean Rank (zGMR) (-, ) The z-scored geometric mean rank Ranking
z-Hits at K (-, ) The z-scored hits at K Ranking
z-Mean Rank (zMR) (-, ) The z-scored mean rank Ranking
z-Mean Reciprocal Rank (zMRR) (-, ) The z-scored mean reciprocal rank Ranking

Trackers

The following 8 trackers are implemented in PyKEEN.

Name Reference Description
console pykeen.trackers.ConsoleResultTracker A class that directly prints to console.
csv pykeen.trackers.CSVResultTracker Tracking results to a CSV file.
json pykeen.trackers.JSONResultTracker Tracking results to a JSON lines file.
mlflow pykeen.trackers.MLFlowResultTracker A tracker for MLflow.
neptune pykeen.trackers.NeptuneResultTracker A tracker for Neptune.ai.
python pykeen.trackers.PythonResultTracker A tracker which stores everything in Python dictionaries.
tensorboard pykeen.trackers.TensorBoardResultTracker A tracker for TensorBoard.
wandb pykeen.trackers.WANDBResultTracker 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:

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:

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:

@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 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):

Funding

The development of PyKEEN has been funded by the following grants:

Funding Body Program Grant
DARPA Young Faculty Award (PI: Benjamin Gyori) W911NF2010255
DARPA Automating Scientific Knowledge Extraction (ASKE) HR00111990009
German Federal Ministry of Education and Research (BMBF) Maschinelles Lernen mit Wissensgraphen (MLWin) 01IS18050D
German Federal Ministry of Education and Research (BMBF) Munich Center for Machine Learning (MCML) 01IS18036A
Innovation Fund Denmark (Innovationsfonden) Danish Center for Big Data Analytics driven Innovation (DABAI) Grand Solutions

Logo

The PyKEEN logo was designed by Carina Steinborn

Citation

If you have found PyKEEN useful in your work, please consider citing our article:

@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}
}