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Link prediction is critical for the application of incomplete knowledge graph (KG) in the downstream tasks.
YAGO: A core of semantic knowledge
Fabian M. Suchanek, Gjergji Kasneci, and Gerhard Weikum. 2007 · 2007
Earlier work this paper cites.
Adaptive subgradient methods for online learning and stochastic optimization
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Maximilian Nickel, Volker Tresp, and Hans-Peter Kriegel. 2011 · 2011
Earlier work this paper cites.
Representation Theory of Finite Groups
Benjamin Steinberg. 2012 · 2012
Earlier work this paper cites.
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Yoshua Bengio, Nicholas Léonard, and Aaron C. Courville. 2013 · 2013
Earlier work this paper cites.
Semantic parsing on Freebase from question-answer pairs
Jonathan Berant, Andrew Chou, Roy Frostig, and Percy Liang. 2013 · 2013
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Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, and Oksana Yakhnenko. 2013 · 2013
Earlier work this paper cites.
Reasoning with neural tensor networks for knowledge base completion
Richard Socher, Danqi Chen, Christopher D. Manning, and Andrew Y. Ng. 2013 · 2013
Earlier work this paper cites.
Question answering with subgraph embeddings
Antoine Bordes, Sumit Chopra, and Jason Weston. 2014 · 2014
Earlier work this paper cites.
Knowledge graph embedding by translating on hyperplanes
Zhen Wang, Jianwen Zhang, Jianlin Feng, and Zheng Chen. 2014 · 2014
Earlier work this paper cites.
Traversing knowledge graphs in vector space
Kelvin Guu, John Miller, and Percy Liang. 2015 · 2015
Earlier work this paper cites.
Compositional vector space models for knowledge base completion
Arvind Neelakantan, Benjamin Roth, and Andrew McCallum. 2015 · 2015
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Representing text for joint embedding of text and knowledge bases
Kristina Toutanova, Danqi Chen, Patrick Pantel, Hoifung Poon, Pallavi Choudhury, and Michael Gamon. 2015 · 2015
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Embedding entities and relations for learning and inference in knowledge bases
Bishan Yang, Wen-tau Yih, Xiaodong He, Jianfeng Gao, and Li Deng. 2015 · 2015
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Holographic embeddings of knowledge graphs
Maximilian Nickel, Lorenzo Rosasco, and Tomaso Poggio. 2016 · 2016
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Complex embeddings for simple link prediction
Théo Trouillon, Johannes Welbl, Sebastian Riedel, Éric Gaussier, and Guillaume Bouchard. 2016 · 2016
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ProjE: Embedding projection for knowledge graph completion
Baoxu Shi and Tim Weninger. 2017 · 2017
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DeepPath: A reinforcement learning method for knowledge graph reasoning
Wenhan Xiong, Thien Hoang, and William Yang Wang. 2017 · 2017
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Go for a walk and arrive at the answer: Reasoning over paths in knowledge bases using reinforcement learning
Rajarshi Das, Shehzaad Dhuliawala, Manzil Zaheer, Luke Vilnis, Ishan Durugkar, Akshay Krishnamurthy, Alex Smola, and Andrew McCallum. 2018 · 2018
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Convolutional 2d knowledge graph embeddings
Tim Dettmers, Pasquale Minervini, Pontus Stenetorp, and Sebastian Riedel. 2018 · 2018
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TorusE: Knowledge graph embedding on a lie group
Takuma Ebisu and Ryutaro Ichise. 2018 · 2018
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SimplE embedding for link prediction in knowledge graphs
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Katsuhiko Hayashi and Masashi Shimbo. 2017 · 2017
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Learning symmetric collaborative dialogue agents with dynamic knowledge graph embeddings
He He, Anusha Balakrishnan, Mihail Eric, and Percy Liang. 2017 · 2017
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Categorical reparameterization with gumbel-softmax
Eric Jang, Shixiang Gu, and Ben Poole. 2017 · 2017
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Knowledge base completion: Baselines strike back
Rudolf Kadlec, Ondrej Bajgar, and Jan Kleindienst. 2017 · 2017
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Analogical inference for multi-relational embeddings
Hanxiao Liu, Yuexin Wu, and Yiming Yang. 2017 · 2017
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Automatic differentiation in PyTorch
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer. 2017 · 2017
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Modeling relation paths for representation learning of knowledge bases
Yankai Lin, Zhiyuan Liu, Huan-Bo Luan, Maosong Sun, Siwei Rao, and Song Liu. 2015a
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Seyed Mehran Kazemi and David Poole. 2018 · 2018
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Multi-hop knowledge graph reasoning with reward shaping
Xi Victoria Lin, Richard Socher, and Caiming Xiong. 2018 · 2018
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Modeling relational data with graph convolutional networks
Michael Sejr Schlichtkrull, Thomas N. Kipf, Peter Bloem, Rianne van den Berg, Ivan Titov, and Max Welling. 2018 · 2018
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Learning to exploit long-term relational dependencies in knowledge graphs
Lingbing Guo, Zequn Sun, and Wei Hu. 2019 · 2019
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Understanding straight-through estimator in training activation quantized neural nets
Penghang Yin, Jiancheng Lyu, Shuai Zhang, Stanley J. Osher, Yingyong Qi, and Jack Xin. 2019 · 2019
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