Fetching the paper…
Reading the bibliography…
Commonsense knowledge graphs (CKGs) like Atomic and ASER are substantially different from conventional KGs as they consist of much larger number of nodes formed by loosely-structured text, which, though, enables them to handle highly diverse queries in natural language related to commonsense, leads to unique challenges for automatic KG construction methods.
ASER: A large-scale eventuality knowledge graph
Hongming Zhang, Xin Liu, Haojie Pan, Yangqiu Song, Cane Wing-Ki, et al. 2019a · 1905
Earlier work this paper cites.
RoBERTa: A robustly optimized BERT pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019 · 1907
Earlier work this paper cites.
KG-BERT: BERT for knowledge graph completion
Liang Yao, Chengsheng Mao, and Yuan Luo. 2019 · 1909
Earlier work this paper cites.
Commonsense knowledge base completion with structural and semantic context
Chaitanya Malaviya, Chandra Bhagavatula, Antoine Bosselut, and Yejin Choi. 2019 · 1910
Earlier work this paper cites.
The big book of concepts
Gregory Murphy. 2004 · 2004
Earlier work this paper cites.
A three-way model for collective learning on multi-relational data
Maximilian Nickel, Volker Tresp, and Hans-Peter Kriegel. 2011 · 2011
Earlier work this paper cites.
Short text conceptualization using a probabilistic knowledgebase
Yangqiu Song, Haixun Wang, Zhongyuan Wang, Hongsong Li, and Weizhu Chen. 2011 · 2011
Earlier work this paper cites.
Incorporating GAN for negative sampling in knowledge representation learning
Peifeng Wang, Shuangyin Li, and Rong Pan. 2018 · 2012
Earlier work this paper cites.
Probase: A probabilistic taxonomy for text understanding
Wentao Wu, Hongsong Li, Haixun Wang, and Kenny Q Zhu. 2012 · 2012
Earlier work this paper cites.
Translating embeddings for modeling multi-relational data
Antoine Bordes, Nicolas Usunier, Alberto Garcia-Durán, Jason Weston, and Oksana Yakhnenko. 2013 · 2013
Earlier work this paper cites.
Reasoning with neural tensor networks for knowledge base completion
Richard Socher, Danqi Chen, Christopher D Manning, and Andrew Ng. 2013 · 2013
Earlier work this paper cites.
Knowledge graph embedding by translating on hyperplanes
Zhen Wang, Jianwen Zhang, Jianlin Feng, and Zheng Chen. 2014 · 2014
Earlier work this paper cites.
Embedding entities and relations for learning and inference in knowledge bases
Bishan Yang, Wen-tau Yih, Xiaodong He, Jianfeng Gao, and Li Deng. 2014 · 2014
Earlier work this paper cites.
Short text understanding through lexical-semantic analysis
Wen Hua, Zhongyuan Wang, Haixun Wang, Kai Zheng, and Xiaofang Zhou. 2015 · 2015
Cited alongside, same era.
Knowledge graph embedding via dynamic mapping matrix
Guoliang Ji, Shizhu He, Liheng Xu, Kang Liu, and Jun Zhao. 2015 · 2015
Cited alongside, same era.
Learning entity and relation embeddings for knowledge graph completion
Yankai Lin, Zhiyuan Liu, Maosong Sun, Yang Liu, and Xuan Zhu. 2015 · 2015
Cited alongside, same era.
Query understanding through knowledge-based conceptualization
Zhongyuan Wang, Kejun Zhao, Haixun Wang, Xiaofeng Meng, and Ji-Rong Wen. 2015 · 2015
Cited alongside, same era.
Commonsense knowledge base completion
Xiang Li, Aynaz Taheri, Lifu Tu, and Kevin Gimpel. 2016 · 2016
Cited alongside, same era.
Text-enhanced representation learning for knowledge graph
Zhigang Wang and Juanzi Li. 2016 · 2016
BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018
Later among the works it cites.
Commonsense knowledge base completion and generation
Itsumi Saito, Kyosuke Nishida, Hisako Asano, and Junji Tomita. 2018 · 2018
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 · 2018
Later among the works it cites.
A simple method for commonsense reasoning
Trieu H Trinh and Quoc V Le. 2018 · 2018
Later among the works it cites.
COMET: Commonsense transformers for automatic knowledge graph construction
Antoine Bosselut, Hannah Rashkin, Maarten Sap, Chaitanya Malaviya, Asli Celikyilmaz, and Yejin Choi. 2019 · 2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Representation learning of knowledge graphs with entity descriptions
Ruobing Xie, Zhiyuan Liu, Jia Jia, Huanbo Luan, and Maosong Sun. 2016 · 2016
Cited alongside, same era.
spacy 2: Natural language understanding with bloom embeddings, convolutional neural networks and incremental parsing
Matthew Honnibal and Ines Montani. 2017 · 2017
Cited alongside, same era.
ConceptNet 5.5: An open multilingual graph of general knowledge
Robyn Speer, Joshua Chin, and Catherine Havasi. 2017 · 2017
Cited alongside, same era.
SSP: semantic space projection for knowledge graph embedding with text descriptions
Han Xiao, Minlie Huang, Lian Meng, and Xiaoyan Zhu. 2017 · 2017
Cited alongside, same era.
Accurate text-enhanced knowledge graph representation learning
Bo An, Bo Chen, Xianpei Han, and Le Sun. 2018 · 2018
Cited alongside, same era.
KBGAN: Adversarial learning for knowledge graph embeddings
Liwei Cai and William Yang Wang. 2018 · 2018
Cited alongside, same era.
Commonsense knowledge mining from pretrained models
Joe Davison, Joshua Feldman, and Alexander Rush. 2019 · 2019
Later among the works it cites.
Language models as knowledge bases?
Fabio Petroni, Tim Rocktäschel, Sebastian Riedel, Patrick Lewis, Anton Bakhtin, Yuxiang Wu, and Alexander Miller. 2019 · 2019
Later among the works it cites.
Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019 · 2019
Later among the works it cites.
ATOMIC: An atlas of machine commonsense for if-then reasoning
Maarten Sap, Ronan Le Bras, Emily Allaway, Chandra Bhagavatula, Nicholas Lourie, Hannah Rashkin, Brendan Roof, Noah A Smith, and Yejin Choi. 2019 · 2019
Later among the works it cites.
End-to-end structure-aware convolutional networks for knowledge base completion
Chao Shang, Yun Tang, Jing Huang, Jinbo Bi, Xiaodong He, and Bowen Zhou. 2019 · 2019
Later among the works it cites.
NSCaching: Simple and efficient negative sampling for knowledge graph embedding
Yongqi Zhang, Quanming Yao, Yingxia Shao, and Lei Chen. 2019b · 2019
Later among the works it cites.
Complex embeddings for simple link prediction
Théo Trouillon, Johannes Welbl, Sebastian Riedel, Éric Gaussier, and Guillaume Bouchard. 2016 · 2080
Closest in time.