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Knowledge Graphs are increasingly becoming popular for a variety of downstream tasks like Question Answering and Information Retrieval.
A re-evaluation of knowledge graph completion methods
Zhiqing Sun, Shikhar Vashishth, Soumya Sanyal, Partha P. Talukdar, and Yiming Yang. 2019b · 1911
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Efficient probabilistic logic reasoning with graph neural networks
Yuyu Zhang, Xinshi Chen, Yuan Yang, Arun Ramamurthy, Bo Li, Yuan Qi, and Le Song. 2020 · 2001
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Markov logic networks
Matthew Richardson and Pedro M. Domingos. 2006 · 2006
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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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Knowledge graph embedding by translating on hyperplanes
Zhen Wang, Jianwen Zhang, Jianlin Feng, and Zheng Chen. 2014 · 2014
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Observed versus latent features for knowledge base and text inference
Kristina Toutanova and Danqi Chen. 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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Yoshua Bengio. 2017 · 2017
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KBQA: learning question answering over QA corpora and knowledge bases
Wanyun Cui, Yanghua Xiao, Haixun Wang, Yangqiu Song, Seung-won Hwang, and Wei Wang. 2017 · 2017
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Neural message passing for quantum chemistry
Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley, Oriol Vinyals, and George E. Dahl. 2017 · 2017
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Knowledge transfer for out-of-knowledge-base entities : A graph neural network approach
Takuo Hamaguchi, Hidekazu Oiwa, Masashi Shimbo, and Yuji Matsumoto. 2017 · 2017
Cited alongside, same era.
Inductive representation learning on large graphs
William L. Hamilton, Zhitao Ying, and Jure Leskovec. 2017 · 2017
Cited alongside, same era.
Semi-supervised classification with graph convolutional networks
Thomas N. Kipf and Max Welling. 2017 · 2017
Cited alongside, same era.
Convolutional 2d knowledge graph embeddings
Tim Dettmers, Pasquale Minervini, Pontus Stenetorp, and Sebastian Riedel. 2018 · 2018
Cited alongside, same era.
Knowledge diffusion for neural dialogue generation
Shuman Liu, Hongshen Chen, Zhaochun Ren, Yang Feng, Qun Liu, and Dawei Yin. 2018 · 2018
Cited alongside, same era.
A novel embedding model for knowledge base completion based on convolutional neural network
Multi-relational poincaré graph embeddings
Ivana Balazevic, Carl Allen, and Timothy M. Hospedales. 2019 · 2019
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Transgcn: Coupling transformation assumptions with graph convolutional networks for link prediction
Ling Cai, Bo Yan, Gengchen Mai, Krzysztof Janowicz, and Rui Zhu. 2019 · 2019
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Learning attention-based embeddings for relation prediction in knowledge graphs
Deepak Nathani, Jatin Chauhan, Charu Sharma, and Manohar Kaul. 2019 · 2019
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End-to-end structure-aware convolutional networks for knowledge base completion
Chao Shang, Yun Tang, Jing Huang, Jinbo Bi, Xiaodong He, and Bowen Zhou. 2019 · 2019
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Rotate: Knowledge graph embedding by relational rotation in complex space
Zhiqing Sun, Zhi-Hong Deng, Jian-Yun Nie, and Jian Tang. 2019a · 2019
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Dai Quoc Nguyen, Tu Dinh Nguyen, Dat Quoc Nguyen, and Dinh Q. Phung. 2018 · 2018
Cited alongside, same era.
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
Cited alongside, same era.
Modeling semantics with gated graph neural networks for knowledge base question answering
Daniil Sorokin and Iryna Gurevych. 2018 · 2018
Cited alongside, same era.
Graph attention networks
Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio. 2018 · 2018
Cited alongside, same era.
Link prediction based on graph neural networks
Muhan Zhang and Yixin Chen. 2018 · 2018
Cited alongside, same era.
PeiFeng Wang, Jialong Han, Chenliang Li, and Rong Pan. 2019 · 2019
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OAG: toward linking large-scale heterogeneous entity graphs
Fanjin Zhang, Xiao Liu, Jie Tang, Yuxiao Dong, Peiran Yao, Jie Zhang, Xiaotao Gu, Yan Wang, Bin Shao, Rui Li, and Kuansan Wang. 2019 · 2019
Later among the works it cites.
Dynamically pruned message passing networks for large-scale knowledge graph reasoning
Xiaoran Xu, Wei Feng, Yunsheng Jiang, Xiaohui Xie, Zhiqing Sun, and Zhi-Hong Deng. 2020 · 2020
Closest in time.
Complex embeddings for simple link prediction
Théo Trouillon, Johannes Welbl, Sebastian Riedel, Éric Gaussier, and Guillaume Bouchard. 2016 · 2080
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