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 [![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](./LICENSE)
 
+Documentation | [Installation](https://aws-dglke.readthedocs.io/en/latest/install.html) | [DGL-KE Command Line](https://aws-dglke.readthedocs.io/en/latest/hyper_param.html) | [Built-in KG](https://aws-dglke.readthedocs.io/en/latest/train_built_in.html) | [User-defined KG](https://aws-dglke.readthedocs.io/en/latest/train_user_data.html) | [Distributed Training](https://aws-dglke.readthedocs.io/en/latest/dist_train.html)
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 Knowledge graphs (KGs) are data structures that store information about different entities (nodes) and their relations (edges). A common approach of using KGs in various machine learning tasks is to compute knowledge graph embeddings. DGL-KE is a high performance, easy-to-use, and scalable package for learning large-scale knowledge graph embeddings. The package is implemented on the top of *[Deep Graph Library (DGL)](https://github.com/dmlc/dgl)* and developers can run DGL-KE on CPU machine, GPU machine, as well as clusters with a set of popular models, including [TransE](https://www.utc.fr/~bordesan/dokuwiki/_media/en/transe_nips13.pdf), [TransR](https://www.aaai.org/ocs/index.php/AAAI/AAAI15/paper/viewPaper/9571), [RESCAL](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.383.2015&rep=rep1&type=pdf), [DistMult](https://arxiv.org/abs/1412.6575), [ComplEx](http://proceedings.mlr.press/v48/trouillon16.pdf), and [RotatE](https://arxiv.org/pdf/1902.10197.pdf).
 
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