Fetching the paper…
Reading the bibliography…
Markov Logic Networks (MLNs), which elegantly combine logic rules and probabilistic graphical models, can be used to address many knowledge graph problems.
The detection of patterns in Alyawara nonverbal behavior
Woodrow W Denham · 1973
Earlier work this paper cites.
Introduction to expert systems , volume 21
James Ignizio · 1991
Earlier work this paper cites.
Inductive logic programming: Theory and methods
Stephen Muggleton and Luc De Raedt · 1994
Earlier work this paper cites.
Markov chain Monte Carlo in practice
Walter R Gilks, Sylvia Richardson, and David Spiegelhalter · 1995
Earlier work this paper cites.
Graphical models and variational methods
Zoubin Ghahramani, Matthew J Beal, et al · 2000
Earlier work this paper cites.
Generalized belief propagation
Jonathan S Yedidia, William T Freeman, and Yair Weiss · 2001
Earlier work this paper cites.
Discriminative training of markov logic networks
Parag Singla and Pedro Domingos · 2005
Earlier work this paper cites.
Sound and efficient inference with probabilistic and deterministic dependencies
Hoifung Poon and Pedro Domingos · 2006
Earlier work this paper cites.
Markov logic networks
Matthew Richardson and Pedro Domingos · 2006
Earlier work this paper cites.
Entity resolution with markov logic
Parag Singla and Pedro Domingos · 2006
Earlier work this paper cites.
Bottom-up learning of markov logic network structure
Lilyana Mihalkova and Raymond J Mooney · 2007
Earlier work this paper cites.
Joint inference in information extraction
Hoifung Poon and Pedro Domingos · 2007
Earlier work this paper cites.
Relational markov networks
Ben Taskar, Pieter Abbeel, Ming-Fai Wong, and Daphne Koller · 2007
Earlier work this paper cites.
Freebase: a collaboratively created graph database for structuring human knowledge
Kurt Bollacker, Colin Evans, Praveen Paritosh, Tim Sturge, and Jamie Taylor · 2008
Cited alongside, same era.
Lifted first-order belief propagation
Parag Singla and Pedro M Domingos · 2008
Cited alongside, same era.
Learning markov logic networks via functional gradient boosting
Tushar Khot, Sriraam Natarajan, Kristian Kersting, and Jude Shavlik · 2011
Cited alongside, same era.
Weisfeiler-lehman graph kernels
Nino Shervashidze, Pascal Schweitzer, Erik Jan van Leeuwen, Kurt Mehlhorn, and Karsten M Borgwardt · 2011
Cited alongside, same era.
Translating embeddings for modeling multi-relational data
Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, and Oksana Yakhnenko · 2013
Cited alongside, same era.
Stochastic variational inference
Matthew D Hoffman, David M Blei, Chong Wang, and John Paisley · 2013
Complex embeddings for simple link prediction
Théo Trouillon, Johannes Welbl, Sebastian Riedel, Éric Gaussier, and Guillaume Bouchard · 2016
Later among the works it cites.
Knowledge base completion: Baselines strike back
Rudolf Kadlec, Ondrej Bajgar, and Jan Kleindienst · 2017
Later among the works it cites.
Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2017
Later among the works it cites.
Differentiable learning of logical rules for knowledge base completion
Fan Yang, Zhilin Yang, and William W Cohen · 2017
Later among the works it cites.
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
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Reasoning with neural tensor networks for knowledge base completion
Richard Socher, Danqi Chen, Christopher D Manning, and Andrew Ng · 2013
Cited alongside, same era.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
Cited alongside, same era.
Identifying network public opinion leaders based on markov logic networks
Weizhe Zhang, Xiaoqiang Li, Hui He, and Xing Wang · 2014
Cited alongside, same era.
Hinge-loss markov random fields and probabilistic soft logic
Stephen H Bach, Matthias Broecheler, Bert Huang, and Lise Getoor · 2015
Cited alongside, same era.
Observed versus latent features for knowledge base and text inference
Kristina Toutanova and Danqi Chen · 2015
Cited alongside, same era.
Discriminative embeddings of latent variable models for structured data
Hanjun Dai, Bo Dai, and Le Song · 2016
Cited alongside, same era.
Later among the works it cites.
Modeling relational data with graph convolutional networks
Michael Schlichtkrull, Thomas N Kipf, Peter Bloem, Rianne Van Den Berg, Ivan Titov, and Max Welling · 2018
Later among the works it cites.
One-shot relational learning for knowledge graphs
Wenhan Xiong, Mo Yu, Shiyu Chang, Xiaoxiao Guo, and William Yang Wang · 2018
Later among the works it cites.
How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2018
Later among the works it cites.
Provably powerful graph networks
Haggai Maron, Heli Ben-Hamu, Hadar Serviansky, and Yaron Lipman · 2019
Later among the works it cites.
Probabilistic logic neural networks for reasoning
Meng Qu and Jian Tang · 2019
Later among the works it cites.
GMNN: Graph Markov neural networks
Meng Qu, Yoshua Bengio, and Jian Tang · 2019
Later among the works it cites.
Lifted hinge-loss markov random fields
Sriram Srinivasan, Behrouz Babaki, Golnoosh Farnadi, and Lise Getoor · 2019
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.