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Knowledge graph embedding models have gained significant attention in AI research.
Classification ability of single hidden layer feedforward neural networks
G.B Huang, Y.Q Chen, and H.A Babri · 2000
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Classification ability of single hidden layer feedforward neural networks
G.B Huang, Y.Q Chen, and H.A Babri · 2000
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Multivariable calculus, applications and theory
Kenneth Kuttler · 2011
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Multivariable calculus, applications and theory
Kenneth Kuttler · 2011
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Factorizing YAGO: scalable machine learning for linked data
M. Nickel, V. Tresp, and H.P. Kriegel · 2012
Earlier work this paper cites.
Factorizing YAGO: scalable machine learning for linked data
M. Nickel, V. Tresp, and H.P. Kriegel · 2012
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Translating embeddings for modeling multi-relational data
A. Bordes, N. Usunier, A. Garcia-Duran, J. Weston, and O. Yakhnenko · 2013
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Reasoning with neural tensor networks for knowledge base completion
R. Socher, D. Chen, C.D Manning, and Andrew Ng · 2013
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Translating embeddings for modeling multi-relational data
A. Bordes, N. Usunier, A. Garcia-Duran, J. Weston, and O. Yakhnenko · 2013
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Reasoning with neural tensor networks for knowledge base completion
R. Socher, D. Chen, C.D Manning, and Andrew Ng · 2013
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Knowledge vault: A web-scale approach to probabilistic knowledge fusion
X. Dong, E. Gabrilovich, G. Heitz, W. Horn, Ni Lao, K. Murphy, T. Strohmann, S. Sun, and W. Zhang · 2014
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Knowledge graph embedding by translating on hyperplanes
Z. Wang, J. Zhang, J. Feng, and Z. Chen · 2014
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Knowledge vault: A web-scale approach to probabilistic knowledge fusion
X. Dong, E. Gabrilovich, G. Heitz, W. Horn, Ni Lao, K. Murphy, T. Strohmann, S. Sun, and W. Zhang · 2014
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Knowledge graph embedding by translating on hyperplanes
Z. Wang, J. Zhang, J. Feng, and Z. Chen · 2014
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Traversing knowledge graphs in vector space
K. Guu, J. Miller, and P. Liang · 2015
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Modeling relation paths for representation learning of knowledge bases
Y. Lin, Z. Liu, H. Luan, M. Sun, S. Rao, and S. Liu · 2015
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Learning entity and relation embeddings for knowledge graph completion
Y. Lin, Z. Liu, M. Sun, Y. Liu, and X. Zhu · 2015
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Compositional vector space models for knowledge base completion
A. Neelakantan, B. Roth, and A. McCallum · 2015
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Injecting logical background knowledge into embeddings for relation extraction
T. Rocktäschel, S. Singh, and S. Riedel · 2015
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Embedding entities and relations for learning and inference in knowledge bases
B. Yang, W.t Yih, X. He, J. Gao, and Li Deng · 2015
Earlier work this paper cites.
Traversing knowledge graphs in vector space
K. Guu, J. Miller, and P. Liang · 2015
Earlier work this paper cites.
Modeling relation paths for representation learning of knowledge bases
Y. Lin, Z. Liu, H. Luan, M. Sun, S. Rao, and S. Liu · 2015
Earlier work this paper cites.
Learning entity and relation embeddings for knowledge graph completion
Y. Lin, Z. Liu, M. Sun, Y. Liu, and X. Zhu · 2015
Earlier work this paper cites.
Compositional vector space models for knowledge base completion
A. Neelakantan, B. Roth, and A. McCallum · 2015
Earlier work this paper cites.
Injecting logical background knowledge into embeddings for relation extraction
T. Rocktäschel, S. Singh, and S. Riedel · 2015
Cited alongside, same era.
Embedding entities and relations for learning and inference in knowledge bases
B. Yang, W.t Yih, X. He, J. Gao, and Li Deng · 2015
Cited alongside, same era.
Lifted rule injection for relation embeddings
T. Demeester, T. Rocktäschel, and S. Riedel · 2016
Cited alongside, same era.
Jointly embedding knowledge graphs and logical rules
S. Guo, Q. Wang, L. Wang, B. Wang, and Li Guo · 2016
Cited alongside, same era.
A review of relational machine learning for knowledge graphs
M. Nickel, K. Murphy, V. Tresp, and E. Gabrilovich · 2016
Cited alongside, same era.
Complex embeddings for simple link prediction
Convolutional 2d knowledge graph embeddings
T. Dettmers, P. Minervini, P. Stenetorp, and S. Riedel · 2018
Later among the works it cites.
Improving knowledge graph embedding using simple constraints
B. Ding, Q. Wang, B. Wang, and L. Guo · 2018
Later among the works it cites.
Shared embedding based neural networks for knowledge graph completion
S. Guan, X. Jin, Y. Wang, and X. Cheng · 2018
Later among the works it cites.
Knowledge graph embedding with iterative guidance from soft rules
S. Guo, Q. Wang, L. Wang, B. Wang, and Li Guo · 2018
Later among the works it cites.
A triple-branch neural network for knowledge graph embedding
X. Han, C. Zhang, T. Sun, Y. Ji, and Z. Hu · 2018
Later among the works it cites.
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T. Trouillon, J. Welbl, S. Riedel, É. Gaussier, and G. Bouchard · 2016
Cited alongside, same era.
A translation-based knowledge graph embedding preserving logical property of relations
H.G Yoon, H.J Song, S.B Park, and S.Y Park · 2016
Cited alongside, same era.
Lifted rule injection for relation embeddings
T. Demeester, T. Rocktäschel, and S. Riedel · 2016
Cited alongside, same era.
Jointly embedding knowledge graphs and logical rules
S. Guo, Q. Wang, L. Wang, B. Wang, and Li Guo · 2016
Cited alongside, same era.
A review of relational machine learning for knowledge graphs
M. Nickel, K. Murphy, V. Tresp, and E. Gabrilovich · 2016
Cited alongside, same era.
Complex embeddings for simple link prediction
T. Trouillon, J. Welbl, S. Riedel, É. Gaussier, and G. Bouchard · 2016
Cited alongside, same era.
A translation-based knowledge graph embedding preserving logical property of relations
H.G Yoon, H.J Song, S.B Park, and S.Y Park · 2016
Cited alongside, same era.
S.M Kazemi and D. Poole · 2018
Later among the works it cites.
Canonical tensor decomposition for knowledge base completion
T. Lacroix, N. Usunier, and G. Obozinski · 2018
Later among the works it cites.
A novel embedding model for knowledge base completion based on convolutional neural network
D.Q Nguyen, T.D Nguyen, D.Q Nguyen, and D. Phung · 2018
Later among the works it cites.
On multi-relational link prediction with bilinear models
Y. Wang, R. Gemulla, and H. Li · 2018
Later among the works it cites.
Re-evaluating embedding-based knowledge graph completion methods
F. Akrami, L. Guo, W. Hu, and C. Li · 2018
Later among the works it cites.
Convolutional 2d knowledge graph embeddings
T. Dettmers, P. Minervini, P. Stenetorp, and S. Riedel · 2018
Later among the works it cites.
Improving knowledge graph embedding using simple constraints
B. Ding, Q. Wang, B. Wang, and L. Guo · 2018
Later among the works it cites.
Shared embedding based neural networks for knowledge graph completion
S. Guan, X. Jin, Y. Wang, and X. Cheng · 2018
Later among the works it cites.
Knowledge graph embedding with iterative guidance from soft rules
S. Guo, Q. Wang, L. Wang, B. Wang, and Li Guo · 2018
Later among the works it cites.
A triple-branch neural network for knowledge graph embedding
X. Han, C. Zhang, T. Sun, Y. Ji, and Z. Hu · 2018
Later among the works it cites.
Simple embedding for link prediction in knowledge graphs
S.M Kazemi and D. Poole · 2018
Later among the works it cites.
Canonical tensor decomposition for knowledge base completion
T. Lacroix, N. Usunier, and G. Obozinski · 2018
Later among the works it cites.
A novel embedding model for knowledge base completion based on convolutional neural network
D.Q Nguyen, T.D Nguyen, D.Q Nguyen, and D. Phung · 2018
Later among the works it cites.
On multi-relational link prediction with bilinear models
Y. Wang, R. Gemulla, and H. Li · 2018
Later among the works it cites.
Factorizing yago: scalable machine learning for linked data
Z. Sun, Z. Deng, J. Nie, and J. Tang · 2019
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Quaternion knowledge graph embedding
Shuai Zhang, Yi Tay, Lina Yao, and Qi Liu · 2019
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Factorizing yago: scalable machine learning for linked data
Z. Sun, Z. Deng, J. Nie, and J. Tang · 2019
Closest in time.
Quaternion knowledge graph embedding
Shuai Zhang, Yi Tay, Lina Yao, and Qi Liu · 2019
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