TensorFlow-TransX - An implementation of TransE and its extended models for Knowledge Representation Learning on TensorFlow
The implementation of TransE , TransH , TransR , TransD  for knowledge representation learning (KRL). The overall framework is based on TensorFlow. We use C++ to implement some underlying operations such as data preprocessing and negative sampling. For each specific model, it is implemented by TensorFlow with Python interfaces so that there is a convenient platform to run models on GPUs. These codes will be gradually integrated into the new framework [OpenKE].