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Rule learning is critical to improving knowledge graph (KG) reasoning due to their ability to provide logical and interpretable explanations.
Learning distributed representations of concepts
Geoffrey E Hinton et al · 1986
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Inductive logic programming
Nada Lavrac and Saso Dzeroski · 1994
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A note on graded modal logic
Maarten De Rijke · 2000
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Stanley Kok and Pedro Domingos · 2007
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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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Observed versus latent features for knowledge base and text inference
Kristina Toutanova and Danqi Chen · 2015
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Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 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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Convolutional 2D knowledge graph embeddings
Tim Dettmers, Pasquale Minervini, Pontus Stenetorp, and Sebastian Riedel · 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
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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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Relational inductive biases, deep learning, and graph networks
Peter W Battaglia, Jessica B Hamrick, Victor Bapst, Alvaro Sanchez-Gonzalez, Vinicius Zambaldi, Mateusz Malinowski, Andrea Tacchetti, David Raposo, Adam Santoro, Ryan Faulkner, et al · 2018
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Embedding logical queries on knowledge graphs
Will Hamilton, Payal Bajaj, Marinka Zitnik, Dan Jurafsky, and Jure Leskovec · 2018
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Tune: A research platform for distributed model selection and training
Richard Liaw, Eric Liang, Robert Nishihara, Philipp Moritz, Joseph E Gonzalez, and Ion Stoica · 2018
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Modeling relational data with graph convolutional networks
Michael Schlichtkrull, Thomas N Kipf, Peter Bloem, Rianne van den Berg, Ivan Titov, and Max Welling · 2018
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Unifying knowledge graph learning and recommendation: Towards a better understanding of user preferences
Yixin Cao, Xiang Wang, Xiangnan He, Zikun Hu, and Tat-Seng Chua · 2019
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Discovering protein drug targets using knowledge graph embeddings
Sameh K. Mohamed, Vít Novácek, and Aayah Nounu · 2019
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Christopher Morris, Martin Ritzert, Matthias Fey, William L Hamilton, Jan Eric Lenssen, Gaurav Rattan, and Martin Grohe · 2019
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Graded modal logic and counting bisimulation
Martin Otto · 2019
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Query2box: Reasoning over knowledge graphs in vector space using box embeddings
Hongyu Ren, Weihua Hu, and Jure Leskovec · 2019
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The surprising power of graph neural networks with random node initialization
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Complex query answering with neural link predictors
Erik Arakelyan, Daniel Daza, Pasquale Minervini, and Michael Cochez · 2021
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A survey on knowledge graphs: Representation, acquisition, and applications
Shaoxiong Ji, Shirui Pan, Erik Cambria, Pekka Marttinen, and S Yu Philip · 2021
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Random features strengthen graph neural networks
Ryoma Sato, Makoto Yamada, and Hisashi Kashima · 2021
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Identity-aware graph neural networks
Jiaxuan You, Jonathan M Gomes-Selman, Rex Ying, and Jure Leskovec · 2021
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Labeling trick: A theory of using graph neural networks for multi-node representation learning
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Drum: End-to-end differentiable rule mining on knowledge graphs
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Weisfeiler and leman go relational
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Explainable gnn-based models over knowledge graphs
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Knowledge graph reasoning with relational digraph
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A*net: A scalable path-based reasoning approach for knowledge graphs
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On the correspondence between monotonic max-sum gnns and datalog
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