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Knowledge graphs enable data scientists to learn end-to-end on heterogeneous knowledge.
A Test for Homogeneity of the Marginal Distributions in a Two-Way Classification
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Yago: a core of semantic knowledge
Fabian M Suchanek, Gjergji Kasneci, and Gerhard Weikum · 2007
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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A three-way model for collective learning on multi-relational data
Maximilian Nickel, Volker Tresp, and Hans-Peter Kriegel · 2011
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Weisfeiler-lehman graph kernels
Nino Shervashidze, Pascal Schweitzer, Erik Jan van Leeuwen, Kurt Mehlhorn, and Karsten M Borgwardt · 2011
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Factorizing yago: scalable machine learning for linked data
Maximilian Nickel, Volker Tresp, and Hans-Peter Kriegel · 2012
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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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Distributed representations of words and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean · 2013
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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 · 2014
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The movielens datasets: History and context
F Maxwell Harper and Joseph A Konstan · 2015
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Character-level convolutional networks for text classification
Xiang Zhang, Junbo Zhao, and Yann LeCun · 2015
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Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2016
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A collection of benchmark datasets for systematic evaluations of machine learning on the semantic web
Petar Ristoski, Gerben Klaas Dirk De Vries, and Heiko Paulheim · 2016
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Representation learning of knowledge graphs with entity descriptions
Ruobing Xie, Zhiyuan Liu, Jia Jia, Huanbo Luan, and Maosong Sun · 2016
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A Comprehensive Survey of Graph Embedding: Problems, Techniques and Applications
Hongyun Cai, Vincent W. Zheng, and Kevin Chen-Chuan Chang · 2018
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A first experiment on including text literals in kglove
Michael Cochez, Martina Garofalo, Jérôme Lenßen, and Maria Angela Pellegrino · 2018
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Towards exploring literals to enrich data linking in knowledge graphs
Gustavo de Assis Costa and José Maria Parente de Oliveira · 2018
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Incorporating literals into knowledge graph embeddings
Agustinus Kristiadi, Mohammad Asif Khan, Denis Lukovnikov, Jens Lehmann, and Asja Fischer · 2018
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Embedding multimodal relational data for knowledge base completion
Pouya Pezeshkpour, Liyan Chen, and Sameer Singh · 2018
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Ruobing Xie, Zhiyuan Liu, Huanbo Luan, and Maosong Sun · 2016
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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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Mobilenets: Efficient convolutional neural networks for mobile vision applications
Andrew G Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, and Hartwig Adam · 2017
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Inception-v4, inception-resnet and the impact of residual connections on learning
Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, and Alexander A Alemi · 2017
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Multi-task neural network for non-discrete attribute prediction in knowledge graphs
Yi Tay, Luu Anh Tuan, Minh C Phan, and Siu Cheung Hui · 2017
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Knowledge graph embedding: A survey of approaches and applications
Quan Wang, Zhendong Mao, Bin Wang, and Li Guo · 2017
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The knowledge graph as the default data model for learning on heterogeneous knowledge
Xander Wilcke, Peter Bloem, and Victor de Boer · 2017
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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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Deep learning for classification tasks on geospatial vector polygons
Rein van’t Veer, Peter Bloem, and Erwin Folmer · 2018
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Knowledge graph embedding with numeric attributes of entities
Yanrong Wu and Zhichun Wang · 2018
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A survey on knowledge graph embeddings with literals: Which model links better literal-ly?
Genet Asefa Gesese, Russa Biswas, Mehwish Alam, and Harald Sack · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever · 2019
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You CAN teach an old dog new tricks! on training knowledge graph embeddings
Daniel Ruffinelli, Samuel Broscheit, and Rainer Gemulla · 2019
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kgbench: A collection of datasets for multimodal and relational learning on heterogeneous knowledge
P Bloem, L van Berkel, WX Wilcke, and V de Boer · 2021
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