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Conventional representation learning algorithms for knowledge graphs (KG) map each entity to a unique embedding vector.
KG-BERT: BERT for knowledge graph completion
Liang Yao, Chengsheng Mao, and Yuan Luo · 1909
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Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter
Victor Sanh, Lysandre Debut, Julien Chaumond, and Thomas Wolf · 1910
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The pagerank citation ranking: Bringing order to the web
Lawrence Page, Sergey Brin, Rajeev Motwani, and Terry Winograd · 1999
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Japanese and korean voice search
Mike Schuster and Kaisuke Nakajima · 2012
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Translating embeddings for modeling multi-relational data
Antoine Bordes, Nicolas Usunier, Alberto García-Durán, Jason Weston, and Oksana Yakhnenko · 2013
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Distributed representations of words and phrases and their compositionality
Tomás Mikolov, Ilya Sutskever, Kai Chen, Gregory S. Corrado, and Jeffrey Dean · 2013
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Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher D. Manning · 2014
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Wikidata: a free collaborative knowledgebase
Denny Vrandecic and Markus Krötzsch · 2014
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YAGO3: A knowledge base from multilingual wikipedias
Farzaneh Mahdisoltani, Joanna Biega, and Fabian M. Suchanek · 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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A review of relational machine learning for knowledge graphs
Maximilian Nickel, Kevin Murphy, Volker Tresp, and Evgeniy Gabrilovich · 2016
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Neural machine translation of rare words with subword units
Rico Sennrich, Barry Haddow, and Alexandra Birch · 2016
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Enriching word vectors with subword information
Piotr Bojanowski, Edouard Grave, Armand Joulin, and Tomas Mikolov · 2017
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Knowledge transfer for out-of-knowledge-base entities : A graph neural network approach
Takuo Hamaguchi, Hidekazu Oiwa, Masashi Shimbo, and Yuji Matsumoto · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin · 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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Convolutional 2d knowledge graph embeddings
Tim Dettmers, Pasquale Minervini, Pontus Stenetorp, and Sebastian Riedel · 2018
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Canonical tensor decomposition for knowledge base completion
Timothée Lacroix, Nicolas Usunier, and Guillaume Obozinski · 2018
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Fine-grained evaluation of rule- and embedding-based systems for knowledge graph completion
Christian Meilicke, Manuel Fink, Yanjie Wang, Daniel Ruffinelli, Rainer Gemulla, and Heiner Stuckenschmidt · 2018
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Modeling relational data with graph convolutional networks
Michael Sejr Schlichtkrull, Thomas N. Kipf, Peter Bloem, Rianne van den Berg, Ivan Titov, and Max Welling · 2018
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Multi-relational poincaré graph embeddings
Ivana Balazevic, Carl Allen, and Timothy M. Hospedales · 2019
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Meta relational learning for few-shot link prediction in knowledge graphs
Mingyang Chen, Wen Zhang, Wei Zhang, Qiang Chen, and Huajun Chen · 2019
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BERT: pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
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Fast graph representation learning with PyTorch Geometric
Matthias Fey and Jan E. Lenssen · 2019
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PyTorch-BigGraph: A Large-scale Graph Embedding System
Adam Lerer, Ledell Wu, Jiajun Shen, Timothee Lacroix, Luca Wehrstedt, Abhijit Bose, and Alex Peysakhovich · 2019
A survey on knowledge graphs: Representation, acquisition and applications
Shaoxiong Ji, Shirui Pan, Erik Cambria, Pekka Marttinen, and Philip S. Yu · 2020
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Scaling laws for neural language models
Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei · 2020
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Pre-trained models for natural language processing: A survey
Xipeng Qiu, Tianxiang Sun, Yige Xu, Yunfan Shao, Ning Dai, and Xuanjing Huang · 2020
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Beyond triplets: Hyper-relational knowledge graph embedding for link prediction
Paolo Rosso, Dingqi Yang, and Philippe Cudré-Mauroux · 2020
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Knowledge graph embedding compression
Mrinmaya Sachan · 2020
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Janossy pooling: Learning deep permutation-invariant functions for variable-size inputs, 2019
Ryan L. Murphy, Balasubramaniam Srinivasan, Vinayak Rao, and Bruno Ribeiro · 2019
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Köpf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever · 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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Rotate: Knowledge graph embedding by relational rotation in complex space
Zhiqing Sun, Zhi-Hong Deng, Jian-Yun Nie, and Jian Tang · 2019
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Logic attention based neighborhood aggregation for inductive knowledge graph embedding
Peifeng Wang, Jialong Han, Chenliang Li, and Rong Pan · 2019
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CoDEx: A Comprehensive Knowledge Graph Completion Benchmark
Tara Safavi and Danai Koutra · 2020
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Inductive relation prediction by subgraph reasoning
Komal K. 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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Mulde: Multi-teacher knowledge distillation for low-dimensional knowledge graph embeddings
Kai Wang, Yu Liu, Qian Ma, and Quan Z Sheng · 2020
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Autosf: Searching scoring functions for knowledge graph embedding
Yongqi Zhang, Quanming Yao, Wenyuan Dai, and Lei Chen · 2020
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Autosf: Searching scoring functions for knowledge graph embedding
Yongqi Zhang, Quanming Yao, Wenyuan Dai, and Lei Chen · 2020
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Distile: Distiling knowledge graph embeddings for faster and cheaper reasoning
Yushan Zhu, Wen Zhang, Hui Chen, Xu Cheng, Wei Zhang, and Huajun Chen · 2020
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PyKEEN 1.0: A Python Library for Training and Evaluating Knowledge Graph Embeddings
Mehdi Ali, Max Berrendorf, Charles Tapley Hoyt, Laurent Vermue, Sahand Sharifzadeh, Volker Tresp, and Jens Lehmann · 2021
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MLMLM: link prediction with mean likelihood masked language model
Louis Clouâtre, Philippe Trempe, Amal Zouaq, and Sarath Chandar · 2021
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Inductive entity representations from text via link prediction
Daniel Daza, Michael Cochez, and Paul Groth · 2021
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Anchor & transform: Learning sparse embeddings for large vocabularies
Paul Pu Liang, Manzil Zaheer, Yuan Wang, and Amr Ahmed · 2021
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{SOLAR}: Sparse orthogonal learned and random embeddings
Tharun Medini, Beidi Chen, and Anshumali Shrivastava · 2021
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KEPLER: A unified model for knowledge embedding and pre-trained language representation
Xiaozhi Wang, Tianyu Gao, Zhaocheng Zhu, Zhengyan Zhang, Zhiyuan Liu, Juanzi Li, and Jian Tang · 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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