Fetching the paper…
Reading the bibliography…
Learning logical rules is critical to improving reasoning in KGs.
Learning distributed representations of concepts
Geoffrey E Hinton et al · 1986
Earlier work this paper cites.
Efficient induction of logic programs
Stephen Muggleton, Cao Feng, et al · 1990
Earlier work this paper cites.
Learning logical definitions from relations
J. Ross Quinlan · 1990
Earlier work this paper cites.
Inductive logic programming
Stephen Muggleton · 1992
Earlier work this paper cites.
Learning semantic grammars with constructive inductive logic programming
John M Zelle and Raymond J Mooney · 1993
Earlier work this paper cites.
Inductive logic programming: Theory and methods
Stephen Muggleton and Luc De Raedt · 1994
Earlier work this paper cites.
Sound and complete forward and backward chainings of graph rules
Eric Salvat and Marie-Laure Mugnier · 1996
Earlier work this paper cites.
Foundations of inductive logic programming , volume 1228
Shan-Hwei Nienhuys-Cheng and Ronald De Wolf · 1997
Earlier work this paper cites.
Bidirectional recurrent neural networks
Mike Schuster and Kuldip K Paliwal · 1997
Earlier work this paper cites.
The SphereSearch engine for unified ranked retrieval of heterogeneous XML and web documents
Jens Graupmann, Ralf Schenkel, and Gerhard Weikum · 2005
Earlier work this paper cites.
Statistical predicate invention
Stanley Kok and Pedro Domingos · 2007
Earlier work this paper cites.
Yago: a core of semantic knowledge
Fabian M Suchanek, Gjergji Kasneci, and Gerhard Weikum · 2007
Earlier work this paper cites.
Scaffold hopping in drug discovery using inductive logic programming
Kazuhisa Tsunoyama, Ata Amini, Michael JE Sternberg, and Stephen H Muggleton · 2008
Earlier work this paper cites.
Long short-term memory
Alex Graves · 2012
Earlier work this paper cites.
Translating embeddings for modeling multi-relational data
Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, and Oksana Yakhnenko · 2013
Cited alongside, same era.
Principles of random walk , volume 34
Frank Spitzer · 2013
Cited alongside, same era.
Empirical evaluation of gated recurrent neural networks on sequence modeling
Junyoung Chung, Caglar Gulcehre, KyungHyun Cho, and Yoshua Bengio · 2014
Cited alongside, same era.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
Cited alongside, same era.
Embedding entities and relations for learning and inference in knowledge bases
Bishan Yang, Wen-tau Yih, Xiaodong He, Jianfeng Gao, and Li Deng · 2014
Cited alongside, same era.
Differentiable learning of logical rules for knowledge base reasoning
Fan Yang, Zhilin Yang, and William W Cohen · 2017
Later among the works it cites.
Logical rule induction and theory learning using neural theorem proving
Andres Campero, Aldo Pareja, Tim Klinger, Josh Tenenbaum, and Sebastian Riedel · 2018
Later among the works it cites.
Convolutional 2d knowledge graph embeddings
Tim Dettmers, Pasquale Minervini, Pontus Stenetorp, and Sebastian Riedel · 2018
Later among the works it cites.
Drum: End-to-end differentiable rule mining on knowledge graphs
Ali Sadeghian, Mohammadreza Armandpour, Patrick Ding, and Daisy Zhe Wang · 2019
Later among the works it cites.
Clutrr: A diagnostic benchmark for inductive reasoning from text
Koustuv Sinha, Shagun Sodhani, Jin Dong, Joelle Pineau, and William L Hamilton · 2019
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Observed versus latent features for knowledge base and text inference
Kristina Toutanova and Danqi Chen · 2015
Cited alongside, same era.
Semantic parsing via staged query graph generation: Question answering with knowledge base
Wentau Yih, Ming-Wei Chang, Xiaodong He, and Jianfeng Gao · 2015
Cited alongside, same era.
Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2016
Cited alongside, same era.
Complex embeddings for simple link prediction
Théo Trouillon, Johannes Welbl, Sebastian Riedel, Éric Gaussier, and Guillaume Bouchard · 2016
Cited alongside, same era.
Neural network-based question answering over knowledge graphs on word and character level
Denis Lukovnikov, Asja Fischer, Jens Lehmann, and Sören Auer · 2017
Cited alongside, same era.
End-to-end differentiable proving
Tim Rocktäschel and Sebastian Riedel · 2017
Cited alongside, same era.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
Cited alongside, same era.
Later among the works it cites.
Rotate: Knowledge graph embedding by relational rotation in complex space
Zhiqing Sun, Zhi-Hong Deng, Jian-Yun Nie, and Jian Tang · 2019
Later among the works it cites.
Learn to explain efficiently via neural logic inductive learning
Yuan Yang and Le Song · 2019
Later among the works it cites.
Compositionality decomposed: How do neural networks generalise?
Dieuwke Hupkes, Verna Dankers, Mathijs Mul, and Elia Bruni · 2020
Later among the works it cites.
Rnnlogic: Learning logic rules for reasoning on knowledge graphs
Meng Qu, Junkun Chen, Louis-Pascal Xhonneux, Yoshua Bengio, and Jian Tang · 2020
Later among the works it cites.
Evaluating logical generalization in graph neural networks
Koustuv Sinha, Shagun Sodhani, Joelle Pineau, and William L Hamilton · 2020
Later among the works it cites.
A survey on knowledge graphs: Representation, acquisition, and applications
Shaoxiong Ji, Shirui Pan, Erik Cambria, Pekka Marttinen, and S Yu Philip · 2021
Later among the works it cites.
Rlogic: Recursive logical rule learning from knowledge graphs
Kewei Cheng, Jiahao Liu, Wei Wang, and Yizhou Sun · 2022
Later among the works it cites.
Neuro-symbolic hierarchical rule induction
Claire Glanois, Zhaohui Jiang, Xuening Feng, Paul Weng, Matthieu Zimmer, Dong Li, Wulong Liu, and Jianye Hao · 2022
Later among the works it cites.
R5: Rule discovery with reinforced and recurrent relational reasoning
Shengyao Lu, Bang Liu, Keith G Mills, SHANGLING JUI, and Di Niu · 2022
Later among the works it cites.