  Recently, knowledge graph embeddings (KGEs) received significant attention,
and several software libraries have been developed for training and evaluating
KGEs. While each of them addresses specific needs, we re-designed and
re-implemented PyKEEN, one of the first KGE libraries, in a community effort.
PyKEEN 1.0 enables users to compose knowledge graph embedding models (KGEMs)
based on a wide range of interaction models, training approaches, loss
functions, and permits the explicit modeling of inverse relations. Besides, an
automatic memory optimization has been realized in order to exploit the
provided hardware optimally, and through the integration of Optuna extensive
hyper-parameter optimization (HPO) functionalities are provided.
