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Translating embeddings for modeling multi-relational data | Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2

Article

Translating embeddings for modeling multi-relational data

Authors:

Antoine Bordes,

Nicolas Usunier,

Alberto Garcia-Durán,

Oksana YakhnenkoAuthors Info & Claims

NIPS'13: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2

Pages 2787 - 2795

Published: 05 December 2013 Publication History

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 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.

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cover image Guide Proceedings

NIPS'13: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2

December 2013

3236 pages

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Curran Associates Inc.

Red Hook, NY, United States

Publication History

Published: 05 December 2013

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Cited By

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https://dl.acm.org/doi/10.1145/3653015
Jiang CTan CZhang Z(2024)3VFP: Three-View Feature Propagation Entity AlignmentProceedings of the 2024 16th International Conference on Machine Learning and Computing10.1145/3651671.3651777(347-354)Online publication date: 2-Feb-2024
https://dl.acm.org/doi/10.1145/3651671.3651777
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https://dl.acm.org/doi/10.1145/3639315
Feng SJing BZhu YTong H(2024)ArieL: Adversarial Graph Contrastive LearningACM Transactions on Knowledge Discovery from Data10.1145/363805418:4(1-22)Online publication date: 12-Feb-2024
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Liu JChen CLee CHuang S(2024)Evolving Knowledge Graph Representation Learning with Multiple Attention Strategies for Citation Recommendation SystemACM Transactions on Intelligent Systems and Technology10.1145/363527315:2(1-26)Online publication date: 13-Jan-2024
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