We consider the problem of embedding entities and relationships of multi-relational data in low-dimensional vector spaces. Our objective is to propose a canonical model which is easy to train, contains a reduced number of parameters and can scale up to very large databases. Hence, we propose, TransE, a method which models relationships by interpreting them as translations operating on the low-dimensional embeddings of the entities. Despite its simplicity, this assumption proves to be powerful since extensive experiments show that TransE significantly outperforms state-of-the-art methods in link prediction on two knowledge bases. Besides, it can be successfully trained on a large scale data set with 1M entities, 25k relationships and more than 17M training samples.

The architecture of this package has been designed by Xavier Glorot (https://github.com/glorotxa), Update (Nov 13): the code for Translating Embeddings (see https://everest.hds.utc.fr/doku.php?id en:transe) has been included along with a new version for Freebase (FB15k). You need to install Theano to use those scripts. It also requires: Python 2.4, Numpy 1.5.0, The experiment scripts are compatible with Jobman but this library is not mandatory.