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Knowledge graph reasoning is a critical task in natural language processing.
Rotate: Knowledge graph embedding by relational rotation in complex space
Zhiqing Sun, Zhi-Hong Deng, Jian-Yun Nie, and Jian Tang. 2019 · 1902
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Node embedding over temporal graphs
Uriel Singer, Ido Guy, and Kira Radinsky. 2019 · 1903
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Relational representation learning for dynamic (knowledge) graphs: A survey
Seyed Mehran Kazemi, Rishab Goel, Kshitij Jain, Ivan Kobyzev, Akshay Sethi, Peter Forsyth, and Pascal Poupart. 2019 · 1905
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Gdelt: Global data on events, location, and tone, 1979–2012
Kalev Leetaru and Philip A Schrodt. 2013 · 2012
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Translating embeddings for modeling multi-relational data
Antoine Bordes, Nicolas Usunier, Alberto García-Durán, Jason Weston, and Oksana Yakhnenko. 2013 · 2013
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Learning phrase representations using rnn encoder-decoder for statistical machine translation
Kyunghyun Cho, Bart van Merrienboer, Çaglar Gülçehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2014 · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba. 2014 · 2014
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Yago3: A knowledge base from multilingual wikipedias
Farzaneh Mahdisoltani, Joanna Asia Biega, and Fabian M. Suchanek. 2014 · 2014
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Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2015 · 2015
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Icews coded event data
Elizabeth Boschee, Jennifer Lautenschlager, Sean O’Brien, Steve Shellman, James Starz, and Michael Ward. 2015 · 2015
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Combining heterogeneous data sources for civil unrest forecasting
Gizem Korkmaz, Jose Cadena, Chris J Kuhlman, Achla Marathe, Anil Vullikanti, and Naren Ramakrishnan. 2015 · 2015
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Planned protest modeling in news and social media
Sathappan Muthiah, Bert Huang, Jaime Arredondo, David Mares, Lise Getoor, Graham Katz, and Naren Ramakrishnan. 2015 · 2015
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A note on the evaluation of generative models
Lucas Theis, Aäron van den Oord, and Matthias Bethge. 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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Semi-supervised classification with graph convolutional networks
Thomas N. Kipf and Max Welling. 2016 · 2016
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Using social media to predict the future: A systematic literature review
Deriving validity time in knowledge graph
Julien Leblay and Melisachew Wudage Chekol. 2018 · 2018
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Learning deep generative models of graphs
Yujia Li, Oriol Vinyals, Chris Dyer, Razvan Pascanu, and Peter Battaglia. 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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Graphrnn: Generating realistic graphs with deep auto-regressive models
Jiaxuan You, Rex Ying, Xiang Ren, William Hamilton, and Jure Leskovec. 2018 · 2018
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Dynamic network embedding by modeling triadic closure process
Lekui Zhou, Yang Yang, Xiang Ren, Fei Wu, and Yueting Zhuang. 2018 · 2018
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Structured sequence modeling with graph convolutional recurrent networks
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Know-evolve: Deep temporal reasoning for dynamic knowledge graphs
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Hyte: Hyperplane-based temporally aware knowledge graph embedding
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Convolutional 2d knowledge graph embeddings
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Learning sequence encoders for temporal knowledge graph completion
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