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Inferring missing links in knowledge graphs (KG) has attracted a lot of attention from the research community.
Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J Williams. 1992 · 1992
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Convolutional networks for images, speech, and time series
Yann LeCun, Yoshua Bengio, et al. 1995 · 1995
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Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber. 1997 · 1997
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Random walk inference and learning in a large scale knowledge base
Ni Lao, Tom Mitchell, and William W Cohen. 2011 · 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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Translating embeddings for modeling multi-relational data
Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, and Oksana Yakhnenko. 2013 · 2013
Earlier work this paper cites.
Improving learning and inference in a large knowledge-base using latent syntactic cues
Matt Gardner, Partha Pratim Talukdar, Bryan Kisiel, and Tom M. Mitchell. 2013 · 2013
Earlier work this paper cites.
Auto-encoding variational bayes
Diederik P Kingma and Max Welling. 2013 · 2013
Earlier work this paper cites.
Reasoning with neural tensor networks for knowledge base completion
Richard Socher, Danqi Chen, Christopher D Manning, and Andrew Ng. 2013 · 2013
Cited alongside, same era.
Incorporating vector space similarity in random walk inference over knowledge bases
Matt Gardner, Partha Pratim Talukdar, Jayant Krishnamurthy, and Tom M. Mitchell. 2014 · 2014
Cited alongside, same era.
Knowledge graph embedding by translating on hyperplanes
Zhen Wang, Jianwen Zhang, Jianlin Feng, and Zheng Chen. 2014 · 2014
Cited alongside, same era.
Knowledge graph embedding via dynamic mapping matrix
Guoliang Ji, Shizhu He, Liheng Xu, Kang Liu, and Jun Zhao. 2015 · 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 · 2015
Cited alongside, same era.
Representing text for joint embedding of text and knowledge bases
Kristina Toutanova, Danqi Chen, Patrick Pantel, Hoifung Poon, Pallavi Choudhury, and Michael Gamon. 2015 · 2015
Later among the works it cites.
Chains of reasoning over entities, relations, and text using recurrent neural networks
Rajarshi Das, Arvind Neelakantan, David Belanger, and Andrew McCallum. 2016 · 2016
Later among the works it cites.
Variational neural machine translation
Biao Zhang, Deyi Xiong, Jinsong Su, Hong Duan, and Min Zhang. 2016 · 2016
Later among the works it cites.
Knowledge graph completion via complex tensor factorization
Théo Trouillon, Christopher R Dance, Johannes Welbl, Sebastian Riedel, Éric Gaussier, and Guillaume Bouchard. 2017 · 2017
Later among the works it cites.
Deeppath: A reinforcement learning method for knowledge graph reasoning
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Elman Mansimov, Emilio Parisotto, Jimmy Lei Ba, and Ruslan Salakhutdinov. 2015 · 2015
Cited alongside, same era.
Compositional vector space models for knowledge base inference
Arvind Neelakantan, Benjamin Roth, and Andrew McCallum. 2015 · 2015
Cited alongside, same era.
Generating sentences by editing prototypes
Kelvin Guu, Tatsunori B Hashimoto, Yonatan Oren, and Percy Liang. 2017a
Cited in the paper.
From language to programs: Bridging reinforcement learning and maximum marginal likelihood
Kelvin Guu, Panupong Pasupat, Evan Zheran Liu, and Percy Liang. 2017b
Cited in the paper.
Wenhan Xiong, Thien Hoang, and William Yang Wang. 2017 · 2017
Later among the works it cites.
Variational reasoning for question answering with knowledge graph
Yuyu Zhang, Hanjun Dai, Zornitsa Kozareva, Alexander J Smola, and Le Song. 2017 · 2017
Later among the works it cites.
Go for a walk and arrive at the answer: Reasoning over paths in knowledge bases using reinforcement learning
Rajarshi Das, Shehzaad Dhuliawala, Manzil Zaheer, Luke Vilnis, Ishan Durugkar, Akshay Krishnamurthy, Alex Smola, and Andrew McCallum. 2018 · 2018
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