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Project: Translating Embeddings for Modeling Multi-relational Data

This page proposes material (pdf, code and data) related to the paper “Translating Embeddings for Modeling Multi-relational Data” published by A. Bordes et al. in Proceedings of NIPS 2013 [1].


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.


  • Conference version (NIPS 13): (pdf) (nips) (poster)
  • Related paper on using this method for improving relation extraction (EMNLP 13): (pdf)


The Python code used to run the experiments in [1] is now available from Github as part of the SME library [3]: (code). It allows to reproduce the main experiments from the paper (from Table 3) using the Raw metric (Results can be slightly different due to the Stochastic Gradient training – for FB15k, we can reach even better results). See the included README and comments in the code for details on how to use it. The code requires the Theano library.


  • Freebase (FB15k). ASCII format: (data). See [1] or the included README for more details. (FB1M also in [1] will not be released).
  • WordNet. ASCII format: (data). See [3] or the included README for more details.


Antoine Bordes: Heudiasyc, UMR CNRS 7253, Université de Technologie de Compiègne, France.
Nicolas Usunier: Heudiasyc, UMR CNRS 7253, Université de Technologie de Compiègne, France.
Jason Weston: Google, New York, USA.


[1] A. Bordes, N. Usunier, A. Garcia-Duran, J. Weston and O. Yakhnenko. Translating Embeddings for Modeling Multi-relational Data. In Advances of Neural Information Processing Systems 2013.
[2] J. Weston, A. Bordes, O. Yakhnenko and N. Usunier. Connecting Language and Knowledge Bases with Embedding Models for Relation Extraction. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, Seattle, USA. 2013.
[3] A. Bordes, X. Glorot, J. Weston and Y. Bengio. A Semantic Matching Energy Function for Learning with Multi-relational Data. Machine Learning Journal - Special Issue on Learning Semantics. 2012.