🤖 A Python library for learning and evaluating knowledge graph embeddings

🌐 The website for PyKEEN and the KEEN universe at https://pykeen.github.io

📖 Knowledge graph embedding model meta-review at https://pykeen.github.io/kgem-meta-review/

📖 A review of KGEM packages and frameworks at https://pykeen.github.io/kgem-software-review.

PyKEEN benchmarks with airspeed velocity served at https://pykeen.github.io/asv-benchmark/

🪑 Benchmark extraction of diagonal entries of a sparse matrix

📊 Results from the reproducibility and benchmarking studies presented in "Bringing Light Into the Dark: A Large-scale Evaluation of Knowledge Graph Embedding Models Under a Unified Framework" (http://arxiv.org/abs/2006.13365)

📐 Results for the Ranking Metrics submission @ GLB 2022

🏅 KG Inductive Link Prediction Challenge (ILPC) 2022