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There has recently been increasing interest in learning representations of temporal knowledge graphs (KGs), which record the dynamic relationships between entities over time.
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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Recurrent event network for reasoning over temporal knowledge graphs
Woojeong Jin, Changlin Zhang, Pedro Szekely, and Xiang Ren. 2019 · 1904
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Learning attention-based embeddings for relation prediction in knowledge graphs
Deepak Nathani, Jatin Chauhan, Charu Sharma, and Manohar Kaul. 2019 · 1906
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Diachronic embedding for temporal knowledge graph completion
Rishab Goel, Seyed Mehran Kazemi, Marcus Brubaker, and Pascal Poupart. 2019 · 1907
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Hyperkg: Hyperbolic knowledge graph embeddings for knowledge base completion
Prodromos Kolyvakis, Alexandros Kalousis, and Dimitris Kiritsis. 2019 · 1908
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Constant curvature graph convolutional networks
Gregor Bachmann, Gary Bécigneul, and Octavian-Eugen Ganea. 2019 · 1911
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Mixed-curvature variational autoencoders
Ondrej Skopek, Octavian-Eugen Ganea, and Gary Bécigneul. 2019 · 1911
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Reasoning on knowledge graphs with debate dynamics
Marcel Hildebrandt, Jorge Andres Quintero Serna, Yunpu Ma, Martin Ringsquandl, Mitchell Joblin, and Volker Tresp. 2020 · 2001
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The graph hawkes network for reasoning on temporal knowledge graphs
Zhen Han, Yuyi Wang, Yunpu Ma, Stephan Guünnemann, and Volker Tresp. 2020 · 2003
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Freebase: a collaboratively created graph database for structuring human knowledge
Kurt Bollacker, Colin Evans, Praveen Paritosh, Tim Sturge, and Jamie Taylor. 2008 · 2008
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Random features for large-scale kernel machines
Ali Rahimi and Benjamin Recht. 2008 · 2008
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A gyrovector space approach to hyperbolic geometry
Abraham Albert Ungar. 2008 · 2008
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Hyte: Hyperplane-based temporally aware knowledge graph embedding
Shib Sankar Dasgupta, Swayambhu Nath Ray, and Partha Talukdar. 2018 · 2011
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A three-way model for collective learning on multi-relational data
Maximilian Nickel, Volker Tresp, and Hans-Peter Kriegel. 2011 · 2011
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A panoramic view of Riemannian geometry
Marcel Berger. 2012 · 2012
Cited alongside, same era.
Gdelt: Global data on events, location, and tone, 1979–2012
Kalev Leetaru and Philip A Schrodt. 2013 · 2012
Cited alongside, same era.
Stochastic gradient descent on riemannian manifolds
Silvere Bonnabel. 2013 · 2013
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Translating embeddings for modeling multi-relational data
Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, and Oksana Yakhnenko. 2013 · 2013
Cited alongside, same era.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba. 2014 · 2014
Cited alongside, same era.
Wikidata: a free collaborative knowledgebase
Denny Vrandečić and Markus Krötzsch. 2014 · 2014
Cited alongside, same era.
Hyperbolic neural networks
Octavian Ganea, Gary Bécigneul, and Thomas Hofmann. 2018 · 2018
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Learning sequence encoders for temporal knowledge graph completion
Alberto García-Durán, Sebastijan Dumančić, and Mathias Niepert. 2018 · 2018
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Learning mixed-curvature representations in product spaces
Albert Gu, Frederic Sala, Beliz Gunel, and Christopher Ré. 2018 · 2018
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Simple embedding for link prediction in knowledge graphs
Seyed Mehran Kazemi and David Poole. 2018 · 2018
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Canonical tensor decomposition for knowledge base completion
Timothée Lacroix, Nicolas Usunier, and Guillaume Obozinski. 2018 · 2018
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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. 2014 · 2014
Cited alongside, same era.
Icews coded event data
Elizabeth Boschee, Jennifer Lautenschlager, Sean O’Brien, Steve Shellman, James Starz, and Michael Ward. 2015 · 2015
Cited alongside, same era.
Complex embeddings for simple link prediction
Théo Trouillon, Johannes Welbl, Sebastian Riedel, Éric Gaussier, and Guillaume Bouchard. 2016 · 2016
Cited alongside, same era.
A novel embedding model for knowledge base completion based on convolutional neural network
Dai Quoc Nguyen, Tu Dinh Nguyen, Dat Quoc Nguyen, and Dinh Phung. 2017 · 2017
Cited alongside, same era.
Poincaré embeddings for learning hierarchical representations
Maximillian Nickel and Douwe Kiela. 2017 · 2017
Cited alongside, same era.
Know-evolve: Deep temporal reasoning for dynamic knowledge graphs
Rakshit Trivedi, Hanjun Dai, Yichen Wang, and Le Song. 2017 · 2017
Cited alongside, same era.
Julien Leblay and Melisachew Wudage Chekol. 2018 · 2018
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Modeling relational data with graph convolutional networks
Michael Schlichtkrull, Thomas N Kipf, Peter Bloem, Rianne Van Den Berg, Ivan Titov, and Max Welling. 2018 · 2018
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Poincar \ \backslash ’e glove: Hyperbolic word embeddings
Alexandru Tifrea, Gary Bécigneul, and Octavian-Eugen Ganea. 2018 · 2018
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Multi-relational poincaré graph embeddings
Ivana Balazevic, Carl Allen, and Timothy Hospedales. 2019 · 2019
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A recommender system for complex real-world applications with nonlinear dependencies and knowledge graph context
Marcel Hildebrandt, Swathi Shyam Sunder, Serghei Mogoreanu, Mitchell Joblin, Akhil Mehta, Ingo Thon, and Volker Tresp. 2019 · 2019
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. 2019 · 2019
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Variational reasoning for question answering with knowledge graph
Yuyu Zhang, Hanjun Dai, Zornitsa Kozareva, Alexander J Smola, and Le Song. 2018 · 2019
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Tensor decompositions for temporal knowledge base completion
Timothee Lacroix, Guillaume Obozinski, and Nicolas Usunier. 2020 · 2020
Closest in time.
You { \{ can } \} teach an old dog new tricks! on training knowledge graph embeddings
Daniel Ruffinelli, Samuel Broscheit, and Rainer Gemulla. 2020 · 2020
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