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Reasoning on large-scale knowledge graphs has been long dominated by embedding methods.
On a routing problem
Richard Bellman · 1958
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A formal basis for the heuristic determination of minimum cost paths
Peter E Hart, Nils J Nilsson, and Bertram Raphael · 1968
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Probabilistic reasoning in intelligent systems: networks of plausible inference
Judea Pearl · 1988
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Semirings: algebraic theory and applications in computer science
Udo Hebisch and Hanns Joachim Weinert · 1998
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Dbpedia: A nucleus for a web of open data
Sören Auer, Christian Bizer, Georgi Kobilarov, Jens Lehmann, Richard Cyganiak, and Zachary Ives · 2007
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Artificial general intelligence
Ben Goertzel and Cassio Pennachin · 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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Semantic parsing on freebase from question-answer pairs
Jonathan Berant, Andrew Chou, Roy Frostig, and Percy Liang · 2013
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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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Yago3: A knowledge base from multilingual wikipedias
Farzaneh Mahdisoltani, Joanna Biega, and Fabian Suchanek · 2014
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Wikidata: a free collaborative knowledgebase
Denny Vrandečić and Markus Krötzsch · 2014
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Efficient and expressive knowledge base completion using subgraph feature extraction
Matt Gardner and Tom Mitchell · 2015
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Compositional vector space models for knowledge base completion
Arvind Neelakantan, Benjamin Roth, and Andrew McCallum · 2015
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Observed versus latent features for knowledge base and text inference
Kristina Toutanova and Danqi Chen · 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
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Compositional learning of embeddings for relation paths in knowledge base and text
Kristina Toutanova, Xi Victoria Lin, Wen-tau Yih, Hoifung Poon, and Chris Quirk · 2016
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Complex embeddings for simple link prediction
Théo Trouillon, Johannes Welbl, Sebastian Riedel, Éric Gaussier, and Guillaume Bouchard · 2016
Earlier work this paper cites.
Collaborative knowledge base embedding for recommender systems
Fuzheng Zhang, Nicholas Jing Yuan, Defu Lian, Xing Xie, and Wei-Ying Ma · 2016
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Chains of reasoning over entities, relations, and text using recurrent neural networks
Rajarshi Das, Arvind Neelakantan, David Belanger, and Andrew McCallum · 2017
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Inductive representation learning on large graphs
Will Hamilton, Zhitao Ying, and Jure Leskovec · 2017
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Deeppath: A reinforcement learning method for knowledge graph reasoning
Wenhan Xiong, Thien Hoang, and William Yang Wang · 2017
Cited alongside, same era.
Differentiable learning of logical rules for knowledge base reasoning
Fan Yang, Zhilin Yang, and William W Cohen · 2017
Cited alongside, same era.
Fastgcn: Fast learning with graph convolutional networks via importance sampling
Jie Chen, Tengfei Ma, and Cao Xiao · 2018
Cited alongside, same era.
Variational knowledge graph reasoning
Wenhu Chen, Wenhan Xiong, Xifeng Yan, and William Yang Wang · 2018
Cited alongside, same era.
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
Cited alongside, same era.
Convolutional 2d knowledge graph embeddings
Principal neighbourhood aggregation for graph nets
Gabriele Corso, Luca Cavalleri, Dominique Beaini, Pietro Liò, and Petar Veličković · 2020
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Sign: Scalable inception graph neural networks
Fabrizio Frasca, Emanuele Rossi, Davide Eynard, Ben Chamberlain, Michael Bronstein, and Federico Monti · 2020
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Reasoning on knowledge graphs with debate dynamics
Marcel Hildebrandt, Jorge Andres Quintero Serna, Yunpu Ma, Martin Ringsquandl, Mitchell Joblin, and Volker Tresp · 2020
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Inductive relation prediction by subgraph reasoning
Komal Teru, Etienne Denis, and Will Hamilton · 2020
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Composition-based multi-relational graph convolutional networks
Shikhar Vashishth, Soumya Sanyal, Vikram Nitin, and Partha Talukdar · 2020
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Tim Dettmers, Pasquale Minervini, Pontus Stenetorp, and Sebastian Riedel · 2018
Cited alongside, same era.
Adaptive sampling towards fast graph representation learning
Wenbing Huang, Tong Zhang, Yu Rong, and Junzhou Huang · 2018
Cited alongside, same era.
Predict then propagate: Graph neural networks meet personalized pagerank
Johannes Klicpera, Aleksandar Bojchevski, and Stephan Günnemann · 2018
Cited alongside, same era.
Multi-hop knowledge graph reasoning with reward shaping
Xi Victoria Lin, Richard Socher, and Caiming Xiong · 2018
Cited alongside, same era.
Modeling relational data with graph convolutional networks
Michael Schlichtkrull, Thomas N Kipf, Peter Bloem, Rianne van den Berg, Ivan Titov, and Max Welling · 2018
Cited alongside, same era.
M-walk: Learning to walk over graphs using monte carlo tree search
Yelong Shen, Jianshu Chen, Po-Sen Huang, Yuqing Guo, and Jianfeng Gao · 2018
Cited alongside, same era.
Drum: End-to-end differentiable rule mining on knowledge graphs
Ali Sadeghian, Mohammadreza Armandpour, Patrick Ding, and Daisy Zhe Wang · 2019
Cited alongside, same era.
Zhanqiu Zhang, Jianyu Cai, Yongdong Zhang, and Jie Wang · 2020
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Dgl-ke: Training knowledge graph embeddings at scale
Da Zheng, Xiang Song, Chao Ma, Zeyuan Tan, Zihao Ye, Jin Dong, Hao Xiong, Zheng Zhang, and George Karypis · 2020
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Sqaler: Scaling question answering by decoupling multi-hop and logical reasoning
Mattia Atzeni, Jasmina Bogojeska, and Andreas Loukas · 2021
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Modeling heterogeneous hierarchies with relation-specific hyperbolic cones
Yushi Bai, Zhitao Ying, Hongyu Ren, and Jure Leskovec · 2021
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Pairre: Knowledge graph embeddings via paired relation vectors
Linlin Chao, Jianshan He, Taifeng Wang, and Wei Chu · 2021
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Relation prediction as an auxiliary training objective for improving multi-relational graph representations
Yihong Chen, Pasquale Minervini, Sebastian Riedel, and Pontus Stenetorp · 2021
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Arthur Feeney, Rishabh Gupta, Veronika Thost, Rico Angell, Gayathri Chandu, Yash Adhikari, and Tengfei Ma · 2021
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Ogb-lsc: A large-scale challenge for machine learning on graphs
Weihua Hu, Matthias Fey, Hongyu Ren, Maho Nakata, Yuxiao Dong, and Jure Leskovec · 2021
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Rnnlogic: Learning logic rules for reasoning on knowledge graphs
Meng Qu, Junkun Chen, Louis-Pascal Xhonneux, Yoshua Bengio, and Jian Tang · 2021
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Relational message passing for knowledge graph completion
Hongwei Wang, Hongyu Ren, and Jure Leskovec · 2021
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Neural bellman-ford networks: A general graph neural network framework for link prediction
Zhaocheng Zhu, Zuobai Zhang, Louis-Pascal Xhonneux, and Jian Tang · 2021
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Learning to walk with dual agents for knowledge graph reasoning
Denghui Zhang, Zixuan Yuan, Hao Liu, Hui Xiong, et al · 2022
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Knowledge graph reasoning with relational digraph
Yongqi Zhang and Quanming Yao · 2022
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Neural graph reasoning: Complex logical query answering meets graph databases
Hongyu Ren, Mikhail Galkin, Michael Cochez, Zhaocheng Zhu, and Jure Leskovec · 2023
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