LibKGE is a PyTorch-based library for efficient training, evaluation, and hyperparameter optimization of knowledge graph embeddings (KGE). It is highly configurable, easy to use, and extensible. Other KGE frameworks are listed below.
The key goal of LibKGE is to foster reproducible research into (as well as meaningful comparisons between) KGE models and training methods. As we argue in our ICLR 2020 paper, the choice of training strategy and hyperparameters are very influential on model performance, often more so than the model class itself. LibKGE aims to provide clean implementations of training, hyperparameter optimization, and evaluation strategies that can be used with any model. Every potential knob or heuristic implemented in the framework is exposed explicitly via well-documented configuration files (e.g., see here and here). LibKGE also provides the most common KGE models and new ones can be easily added (contributions welcome!).
For link prediction tasks, rule-based systems such as AnyBURL are a competitive alternative to KGE.
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