Trouillon, T;
Welbl, J;
Riedel, S;
Gaussier, É;
Bouchard, G;
(2016)
Complex Embeddings for Simple Link Prediction.
In:
Proceedings of The 33rd International Conference on Machine Learning.
(pp. pp. 2071-2080).
International Conference on Machine Learning (ICML): New York, NY, USA.
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Abstract
In statistical relational learning, the link prediction problem is key to automatically understand the structure of large knowledge bases. As in previous studies, we propose to solve this problem through latent factorization. However, here we make use of complex valued embeddings. The composition of complex embeddings can handle a large variety of binary relations, among them symmetric and antisymmetric relations. Compared to state-of-the-art models such as Neural Tensor Network and Holographic Embeddings, our approach based on complex embeddings is arguably simpler, as it only uses the Hermitian dot product, the complex counterpart of the standard dot product between real vectors. Our approach is scalable to large datasets as it remains linear in both space and time, while consistently outperforming alternative approaches on standard link prediction benchmarks.
Type: | Proceedings paper |
---|---|
Title: | Complex Embeddings for Simple Link Prediction |
Event: | The 33rd International Conference on Machine Learning (ICML 2016) |
Location: | New York, United States |
Dates: | 19 June 2016 - 24 June 2016 |
ISBN-13: | 9781510829008 |
Open access status: | An open access version is available from UCL Discovery |
Publisher version: | http://jmlr.org/proceedings/papers/v48/ |
Language: | English |
Additional information: | Copyright 2016 by the author(s). |
Keywords: | cs.AI, cs.AI, cs.LG, stat.ML |
UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
URI: | https://discovery.ucl.ac.uk/id/eprint/1503053 |
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