Knowledge graph link prediction and KGEM hyperparameter preference can be modelled from graph structural features alone; TWIG and TWIG-I demonstrate this and enable cross-KG transfer.
TorchKGE: Knowledge Graph Embedding in Python and PyTorch
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abstract
TorchKGE is a Python module for knowledge graph (KG) embedding relying solely on PyTorch. This package provides researchers and engineers with a clean and efficient API to design and test new models. It features a KG data structure, simple model interfaces and modules for negative sampling and model evaluation. Its main strength is a very fast evaluation module for the link prediction task, a central application of KG embedding. Various KG embedding models are also already implemented. Special attention has been paid to code efficiency and simplicity, documentation and API consistency. It is distributed using PyPI under BSD license. Source code and pointers to documentation and deployment can be found at https://github.com/torchkge-team/torchkge.
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cs.LG 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
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Structural Alignment in Link Prediction
Knowledge graph link prediction and KGEM hyperparameter preference can be modelled from graph structural features alone; TWIG and TWIG-I demonstrate this and enable cross-KG transfer.