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Logical rules are essential for uncovering the logical connections between relations, which could improve reasoning performance and provide interpretable results on knowledge graphs (KGs).
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Jon Barwise · 1977
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Geoffrey E Hinton et al · 1986
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Sound and complete forward and backward chainings of graph rules
Eric Salvat and Marie-Laure Mugnier · 1996
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Stanley Kok and Pedro Domingos · 2007
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Yago: a core of semantic knowledge
Fabian M Suchanek, Gjergji Kasneci, and Gerhard Weikum · 2007
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Relational retrieval using a combination of path-constrained random walks
Ni Lao and William W Cohen · 2010
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Amie: association rule mining under incomplete evidence in ontological knowledge bases
Luis Antonio Galárraga, Christina Teflioudi, Katja Hose, and Fabian Suchanek · 2013
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Ontological pathfinding
Yang Chen, Sean Goldberg, Daisy Zhe Wang, and Soumitra Siddharth Johri · 2016
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Differentiable learning of logical rules for knowledge base reasoning
Fan Yang, Zhilin Yang, and William W Cohen · 2017
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Convolutional 2d knowledge graph embeddings
Tim Dettmers, Pasquale Minervini, Pontus Stenetorp, and Sebastian Riedel · 2018
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Scalable rule learning via learning representation
Pouya Ghiasnezhad Omran, Kewen Wang, and Zhe Wang · 2018
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Probabilistic logic neural networks for reasoning
Meng Qu and Jian Tang · 2019
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Drum: End-to-end differentiable rule mining on knowledge graphs
Ali Sadeghian, Mohammadreza Armandpour, Patrick Ding, and Daisy Zhe Wang · 2019
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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Meng Qu, Junkun Chen, Louis-Pascal Xhonneux, Yoshua Bengio, and Jian Tang · 2020
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Ruleformer: Context-aware rule mining over knowledge graph
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