Holographic embeddings of knowledge graphs
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1510.04935.pdf
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360.65 KB
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Author(s)
Nickel, Maximilian
Rosasco, Lorenzo
Poggio, Tomaso A
Date Issued
March 2016
Journal
Thirtieth AAAI Conference on Artificial Intelligence
Publisher
Association for the Advancement of Artificial Intelligence
Citation
Nickel,Maximilian et al. "Holographic embeddings of knowledge graphs." Thirtieth AAAI Conference on Artificial Intelligence, February 12-17 2016, Phoenix, Arizona, Association for the Advancement of Artificial Intelligence, March 2016 © 2016 Association for the Advancement of Artificial Intelligence
Version
Author's final manuscript
Abstract
Learning embeddings of entities and relations is an efficient and versatile method to perform machine learning on relational data such as knowledge graphs. In this work, we propose holographic embeddings (HOLE) to learn compositional vector space representations of entire knowledge graphs. The proposed method is related to holographic models of associative memory in that it employs circular correlation to create compositional representations. By using correlation as the compositional operator, HOLE can capture rich interactions but simultaneously remains efficient to compute, easy to train, and scalable to very large datasets. Experimentally, we show that holographic embeddings are able to outperform state-ofthe-Art methods for link prediction on knowledge graphs and relational learning benchmark datasets.
MIT Department
McGovern Institute for Brain Research at MIT. Center for Brains, Minds, and Machines
Massachusetts Institute of Technology. Laboratory for Computational and Statistical Learning
McGovern Institute for Brain Research at MIT
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/view/12484
