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This paper shows that a simple baseline based on a Bag-of-Words (BoW) representation learns surprisingly good knowledge graph embeddings.
Dimensions of meaning
Hinrich Schutze. 1992 · 1992
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Text categorization with support vector machines: Learning with many relevant features
Thorsten Joachims. 1998 · 1998
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Latent space approaches to social network analysis
Peter D Hoff, Adrian E Raftery, and Mark S Handcock. 2002 · 2002
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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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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 latent factor model for highly multi-relational data
Rodolphe Jenatton, Nicolas L Roux, Antoine Bordes, and Guillaume R Obozinski. 2012 · 2012
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Factorizing yago: Scalable machine learning for linked data
Maximilian Nickel, Volker Tresp, and Hans-Peter Kriegel. 2012 · 2012
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Baselines and bigrams: Simple, good sentiment and topic classification
Sida Wang and Christopher D Manning. 2012 · 2012
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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
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Efficient estimation of word representations in vector space
Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013 · 2013
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Large-scale object classification using label relation graphs
Jia Deng, Nan Ding, Yangqing Jia, Andrea Frome, Kevin Murphy, Samy Bengio, Yuan Li, Hartmut Neven, and Hartwig Adam. 2014 · 2014
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Deepwalk: Online learning of social representations
Bryan Perozzi, Rami Al-Rfou, and Steven Skiena. 2014 · 2014
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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
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Information extraction over structured data: Question answering with freebase
Xuchen Yao and Benjamin Van Durme. 2014 · 2014
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Large-scale simple question answering with memory networks
Antoine Bordes, Nicolas Usunier, Sumit Chopra, and Jason Weston. 2015 · 2015
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Combining two and three-way embeddings models for link prediction in knowledge bases
Alberto Garcia-Duran, Antoine Bordes, Nicolas Usunier, and Yves Grandvalet. 2015 · 2015
Holographic embeddings of knowledge graphs
Maximilian Nickel, Lorenzo Rosasco, and Tomaso Poggio. 2016 · 2016
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Implicit reasonet: Modeling large-scale structured relationships with shared memory
Yelong Shen, Po-Sen Huang, Ming-Wei Chang, and Jianfeng Gao. 2016 · 2016
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Simple question answering by attentive convolutional neural network
Wenpeng Yin, Mo Yu, Bing Xiang, Bowen Zhou, and Hinrich Schütze. 2016 · 2016
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Enriching word vectors with subword information
Piotr Bojanowski, Edouard Grave, Armand Joulin, and Tomas Mikolov. 2017 · 2017
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Convolutional 2d knowledge graph embeddings
Tim Dettmers, Pasquale Minervini, Pontus Stenetorp, and Sebastian Riedel. 2017 · 2017
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Learning entity and relation embeddings for knowledge graph completion
Yankai Lin, Zhiyuan Liu, Maosong Sun, Yang Liu, and Xuan Zhu. 2015 · 2015
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Representing text for joint embedding of text and knowledge bases
Kristina Toutanova, Danqi Chen, and Patrick Pantel. 2015 · 2015
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Cfo: Conditional focused neural question answering with large-scale knowledge bases
Zihang Dai, Lei Li, and Wei Xu. 2016 · 2016
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Character-level question answering with attention
David Golub and Xiaodong He. 2016 · 2016
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Key-value memory networks for directly reading documents
Alexander Miller, Adam Fisch, Jesse Dodge, Amir-Hossein Karimi, Antoine Bordes, and Jason Weston. 2016 · 2016
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Question answering with subgraph embeddings
Antoine Bordes, Sumit Chopra, and Jason Weston. 2014a
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A semantic matching energy function for learning with multi-relational data
Antoine Bordes, Xavier Glorot, Jason Weston, and Yoshua Bengio. 2014b
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Bag of tricks for efficient text classification
Armand Joulin, Edouard Grave, Piotr Bojanowski, and Tomas Mikolov. 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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Modeling relational data with graph convolutional networks
Michael Schlichtkrull, Thomas N Kipf, Peter Bloem, Rianne van den Berg, Ivan Titov, and Max Welling. 2017 · 2017
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Knowledge graph completion via complex tensor factorization
Théo Trouillon, Christopher R Dance, Johannes Welbl, Sebastian Riedel, Éric Gaussier, and Guillaume Bouchard. 2017 · 2017
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Complex and holographic embeddings of knowledge graphs: a comparison
Théo Trouillon and Maximilian Nickel. 2017 · 2017
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