pykeen Logo

Tutorials

  • Installation
  • First Steps
  • A Toy Example with Translational Distance Models
  • Inductive Link Prediction

How-to Guides

  • Bring Your Own Data
  • Bring Your Own Interaction
  • Extending the Datasets
  • Extending the Models
  • Splitting
  • Getting Started with NodePiece
  • Using Resolvers
  • Saving Checkpoints during Training
  • Optimizing a Model’s Hyper-parameters
  • Running an Ablation Study
  • Tracking Results during Training
  • Troubleshooting

Explanation

  • Knowledge Graph Embedding Models
  • Interaction Functions
  • Representations
  • Normalizer, Constrainer & Regularizer
  • Understanding the Evaluation
  • Performance Tricks
  • Dataset Degree Distributions

Reference

  • Pipeline
  • Models
  • Datasets
  • Inductive Datasets
  • Entity Alignment
  • Triples
  • Triples Workflows
  • Inverse Relations
  • Training
  • Stoppers
  • Loss Functions
  • Loss Weighting
  • Regularizers
  • Result Trackers
  • Negative Sampling
  • Filtering
  • Optimizers
  • Evaluation
  • Metrics
  • Hyper-parameter Optimization
  • Ablation
  • Prediction
  • Uncertainty
  • Sealant
  • Constants
  • Flexible Weight Checkpoints
  • pykeen.nn
  • PyTorch Lightning Integration
  • Utilities

Appendix

  • References
pykeen
  • Search

© Copyright 2019-2026, PyKEEN Project Team.

Built with Sphinx using a theme provided by Read the Docs.