pykeen/pykeen

Health v2 · maintenance only

Adoption not part of the score

2032 stars · 224 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

PyKEEN is a Python library for training and evaluating multimodal knowledge graph embedding models built on PyTorch. It provides a high-level pipeline, 40+ models, and 37+ datasets for tasks like link prediction and knowledge base completion.

Use cases

• train knowledge graph embedding models in python
• predict missing links in a knowledge graph
• evaluate knowledge graph embeddings on link prediction benchmarks
• complete a knowledge base with new triples
• run hyperparameter optimization for KG embedding models
• benchmark different knowledge graph embedding algorithms

When to choose

• you need a Python/PyTorch library with many KG embedding models and datasets out of the box
• you want a simple pipeline API for training and evaluating embeddings
• you need reproducible research with integrated hyperparameter tuning via Optuna

When to avoid

• you need general-purpose graph neural networks rather than KG embeddings
• you work outside Python or need a non-PyTorch deep learning stack
• you only need graph storage or querying rather than embedding models

Facets

library · maturity active

machine-learning deep-learning data-science machine-learning artificial-intelligence data-science python cross-platform knowledge-graph-embeddings link-prediction knowledge-base-completion pytorch knowledge-graphs optuna-hyperparameter-tuning gpu

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