PyKEEN 1.0: A Python Library for Training and Evaluating Knowledge Graph Embeddings

TL;DR

PyKEEN 1.0 is a comprehensive Python library that facilitates the training, evaluation, and hyper-parameter optimization of knowledge graph embedding models, supporting diverse model configurations and efficient resource utilization.

Contribution

It re-implements and extends the original PyKEEN library with new features like inverse relation modeling and advanced hyper-parameter optimization, enhancing flexibility and performance.

Findings

Supports a wide range of KGEMs and training approaches

Includes automatic memory optimization for hardware efficiency

Provides extensive hyper-parameter tuning capabilities

Abstract

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.