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. LibKGE also provides the most common KGE models (e.g., RESCAL, TransE, DistMult, ComplEx, ConvE, RelationalTucker3/
LibKGE knowledge graph embedding library released
