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Getting Started

  • Installation
  • First Steps
  • Knowledge Graph Embedding Models
  • Tracking Results during Training
  • Saving Checkpoints during Training
  • A Toy Example with Translational Distance Models
  • Understanding the Evaluation
  • Optimizing a Model’s Hyper-parameters
  • Running an Ablation Study
  • Performance Tricks
  • Representations
  • Getting Started with NodePiece
  • Inductive Link Prediction
  • PyTorch Lightning Integration
  • Using Resolvers
  • Trobleshooting

Bring Your Own

  • Bring Your Own Data
  • Bring Your Own Interaction

Extending PyKEEN

  • Extending the Datasets
  • Extending the Models

Reference

  • Pipeline
  • Models
  • Datasets
  • Inductive Datasets
  • Entity Alignment
  • Triples
  • Training
  • Stoppers
  • Loss Functions
  • Regularizers
  • Result Trackers
  • Negative Sampling
  • Filtering
  • Evaluation
  • Metrics
  • Hyper-parameter Optimization
  • Ablation
  • Lookup
  • Prediction
  • Uncertainty
  • Sealant
  • Constants
  • pykeen.nn
  • Utilities

Appendix

  • Analysis
  • References
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