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Knowledge graph embedding (KGE) is a technique for learning continuous embeddings for entities and relations in the knowledge graph.Due to its benefit to a variety of downstream tasks such as knowledge graph completion, question answering and recommendation, KGE has gained significant attention recently.
Matrix perturbation theory
Gilbert W Stewart · 1990
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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
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Distant supervision for relation extraction without labeled data
Mike Mintz, Steven Bills, Rion Snow, and Dan Jurafsky · 2009
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A three-way model for collective learning on multi-relational data
Maximilian Nickel, Volker Tresp, and Hans-Peter Kriegel · 2011
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Poisoning attacks against support vector machines
Battista Biggio, Blaine Nelson, and Pavel Laskov · 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
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Yago2: A spatially and temporally enhanced knowledge base from wikipedia
Johannes Hoffart, Fabian M Suchanek, Klaus Berberich, and Gerhard Weikum · 2013
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Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2014
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Knowledge graph embedding by translating on hyperplanes
Zhen Wang, Jianwen Zhang, Jianlin Feng, and Zheng Chen · 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
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Learning multi-relational semantics using neural-embedding models
Bishan Yang, Wen-tau Yih, Xiaodong He, Jianfeng Gao, and Li Deng · 2014
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Knowledge graph embedding via dynamic mapping matrix
Guoliang Ji, Shizhu He, Liheng Xu, Kang Liu, and Jun Zhao · 2015
Cited alongside, same era.
Learning entity and relation embeddings for knowledge graph completion
Yankai Lin, Zhiyuan Liu, Maosong Sun, Yang Liu, and Xuan Zhu · 2015
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Transa: An adaptive approach for knowledge graph embedding
Han Xiao, Minlie Huang, Yu Hao, and Xiaoyan Zhu · 2015
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Semantic parsing via staged query graph generation: Question answering with knowledge base
Wen-tau Yih, Ming-Wei Chang, Xiaodong He, and Jianfeng Gao · 2015
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Certified defenses for data poisoning attacks
Jacob Steinhardt, Pang Wei W Koh, and Percy S Liang · 2017
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Knowledge graph embedding: A survey of approaches and applications
Quan Wang, Zhendong Mao, Bin Wang, and Li Guo · 2017
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Adversarial attacks on node embeddings
Aleksandar Bojcheski and Stephan Günnemann · 2018
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Fast gradient attack on network embedding
Jinyin Chen, Yangyang Wu, Xuanheng Xu, Yixian Chen, Haibin Zheng, and Qi Xuan · 2018
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Adversarial attack on graph structured data
Hanjun Dai, Hui Li, Tian Tian, Xin Huang, Lin Wang, Jun Zhu, and Le Song · 2018
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Thomas N Kipf and Max Welling · 2016
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Adversarial examples in the physical world
Alexey Kurakin, Ian Goodfellow, and Samy Bengio · 2016
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Complex embeddings for simple link prediction
Théo Trouillon, Johannes Welbl, Sebastian Riedel, Éric Gaussier, and Guillaume Bouchard · 2016
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Collaborative knowledge base embedding for recommender systems
Fuzheng Zhang, Nicholas Jing Yuan, Defu Lian, Xing Xie, and Wei-Ying Ma · 2016
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Towards evaluating the robustness of neural networks
Nicholas Carlini and David Wagner · 2017
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Openke: An open toolkit for knowledge embedding
Xu Han, Shulin Cao, Xin Lv, Yankai Lin, Zhiyuan Liu, Maosong Sun, and Juanzi Li · 2018
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Data poisoning attack against unsupervised node embedding methods
Mingjie Sun, Jian Tang, Huichen Li, Bo Li, Chaowei Xiao, Yao Chen, and Dawn Song · 2018
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Dkn: Deep knowledge-aware network for news recommendation
Hongwei Wang, Fuzheng Zhang, Xing Xie, and Minyi Guo · 2018
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Adversarial attacks on classification models for graphs
Daniel Zügner, Amir Akbarnejad, and Stephan Günnemann · 2018
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