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Translating Embeddings for Modeling Multi-relational Data

Abstract : 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 assump- tion 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.
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Submitted on : Thursday, December 19, 2013 - 10:41:53 AM
Last modification on : Tuesday, November 16, 2021 - 4:30:13 AM
Long-term archiving on: : Thursday, March 20, 2014 - 11:47:05 AM


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  • HAL Id : hal-00920777, version 1



Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, Oksana Yakhnenko. Translating Embeddings for Modeling Multi-relational Data. Neural Information Processing Systems (NIPS), Dec 2013, South Lake Tahoe, United States. pp.1-9. ⟨hal-00920777⟩



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