KGATE : a Knowledge Graph Embedding Training Environment

KGATE is a modular Python library for training knowledge graph embedding (KGE) models, built on PyTorch Geometric and TorchKGE. It allows users to assemble or plug in components such as initializers, encoders, decoders, losses, negative samplers, and evaluation metrics, and includes a preprocessing step to prevent data leakage. Benchmarks against six existing KGE libraries show that KGATE’s training time is comparable to the fastest while offering a broader set of features.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

• rag
• diffusion
• benchmarks