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Commonsense Knowledge Graphs (CSKGs) are crucial for commonsense reasoning, yet constructing them through human annotations can be costly.
Roberta: A robustly optimized BERT pretraining approach
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KG-BERT: BERT for knowledge graph completion
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Joint language semantic and structure embedding for knowledge graph completion
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Kgclean: An embedding powered knowledge graph cleaning framework
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Conceptnet — a practical commonsense reasoning tool-kit
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AMIE: association rule mining under incomplete evidence in ontological knowledge bases
Luis Antonio Galárraga, Christina Teflioudi, Katja Hose, and Fabian M. Suchanek. 2013 · 2013
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Acquiring comparative commonsense knowledge from the web
Niket Tandon, Gerard de Melo, and Gerhard Weikum. 2014 · 2014
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Commonsense reasoning and commonsense knowledge in artificial intelligence
Ernest Davis and Gary Marcus. 2015 · 2015
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Fast rule mining in ontological knowledge bases with AMIE+
Luis Galárraga, Christina Teflioudi, Katja Hose, and Fabian M. Suchanek. 2015 · 2015
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba. 2015 · 2015
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Modeling relation paths for representation learning of knowledge bases
Yankai Lin, Zhiyuan Liu, Huan-Bo Luan, Maosong Sun, Siwei Rao, and Song Liu. 2015 · 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 · 2015
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Jointly embedding knowledge graphs and logical rules
Shu Guo, Quan Wang, Lihong Wang, Bin Wang, and Li Guo. 2016 · 2016
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Commonsense knowledge base completion
Xiang Li, Aynaz Taheri, Lifu Tu, and Kevin Gimpel. 2016 · 2016
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Text-enhanced representation learning for knowledge graph
Zhigang Wang and Juan-Zi Li. 2016 · 2016
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Mining semantic association rules from RDF data
Molood Barati, Quan Bai, and Qing Liu. 2017 · 2017
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Conceptnet 5.5: An open multilingual graph of general knowledge
Robyn Speer, Joshua Chin, and Catherine Havasi. 2017 · 2017
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Webchild 2.0 : Fine-grained commonsense knowledge distillation
Niket Tandon, Gerard de Melo, and Gerhard Weikum. 2017 · 2017
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Differentiable learning of logical rules for knowledge base reasoning
Fan Yang, Zhilin Yang, and William W. Cohen. 2017 · 2017
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Accurate text-enhanced knowledge graph representation learning
Bo An, Bo Chen, Xianpei Han, and Le Sun. 2018 · 2018
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Knowledge graph embedding with iterative guidance from soft rules
Shu Guo, Quan Wang, Lihong Wang, Bin Wang, and Li Guo. 2018 · 2018
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Openke: An open toolkit for knowledge embedding
Xu Han, Shulin Cao, Xin Lv, Yankai Lin, Zhiyuan Liu, Maosong Sun, and Juanzi Li. 2018 · 2018
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Graph attention networks
Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio. 2018 · 2018
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Does william shakespeare REALLY write hamlet? knowledge representation learning with confidence
Ruobing Xie, Zhiyuan Liu, Fen Lin, and Leyu Lin. 2018 · 2018
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COMET: commonsense transformers for automatic knowledge graph construction
Antoine Bosselut, Hannah Rashkin, Maarten Sap, Chaitanya Malaviya, Asli Celikyilmaz, and Yejin Choi. 2019 · 2019
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Triple trustworthiness measurement for knowledge graph
Shengbin Jia, Yang Xiang, Xiaojun Chen, Kun Wang, and Shijia E. 2019 · 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 · 2019
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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. 2019a · 2019
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Structure-augmented text representation learning for efficient knowledge graph completion
Bo Wang, Tao Shen, Guodong Long, Tianyi Zhou, Ying Wang, and Yi Chang. 2021b · 2021
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Rlogic: Recursive logical rule learning from knowledge graphs
Kewei Cheng, Jiahao Liu, Wei Wang, and Yizhou Sun. 2022 · 2022
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Acquiring and modelling abstract commonsense knowledge via conceptualization
Mutian He, Tianqing Fang, Weiqi Wang, and Yangqiu Song. 2022 · 2022
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Modularized transfer learning with multiple knowledge graphs for zero-shot commonsense reasoning
Yu Jin Kim, Beong-woo Kwak, Youngwook Kim, Reinald Kim Amplayo, Seung-won Hwang, and Jinyoung Yeo. 2022 · 2022
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Statik: Structure and text for inductive knowledge graph completion
Elan Markowitz, Keshav Balasubramanian, Mehrnoosh Mirtaheri, Murali Annavaram, Aram Galstyan, and Greg Ver Steeg. 2022 · 2022
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Social iqa: Commonsense reasoning about social interactions
Maarten Sap, Hannah Rashkin, Derek Chen, Ronan Le Bras, and Yejin Choi. 2019b · 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 · 2019
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Commonsenseqa: A question answering challenge targeting commonsense knowledge
Alon Talmor, Jonathan Herzig, Nicholas Lourie, and Jonathan Berant. 2019 · 2019
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Data poisoning attack against knowledge graph embedding
Hengtong Zhang, Tianhang Zheng, Jing Gao, Chenglin Miao, Lu Su, Yaliang Li, and Kui Ren. 2019a · 2019
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Iteratively learning embeddings and rules for knowledge graph reasoning
Wen Zhang, Bibek Paudel, Liang Wang, Jiaoyan Chen, Hai Zhu, Wei Zhang, Abraham Bernstein, and Huajun Chen. 2019b · 2019
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What is normal, what is strange, and what is missing in a knowledge graph: Unified characterization via inductive summarization
Caleb Belth, Xinyi Zheng, Jilles Vreeken, and Danai Koutra. 2020 · 2020
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Abductive commonsense reasoning
Chandra Bhagavatula, Ronan Le Bras, Chaitanya Malaviya, Keisuke Sakaguchi, Ari Holtzman, Hannah Rashkin, Doug Downey, Wen-tau Yih, and Yejin Choi. 2020 · 2020
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Sentence-t5: Scalable sentence encoders from pre-trained text-to-text models
Jianmo Ni, Gustavo Hernández Ábrego, Noah Constant, Ji Ma, Keith B. Hall, Daniel Cer, and Yinfei Yang. 2022 · 2022
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Chatgpt: Optimizing language models for dialogue
OpenAI. 2022 · 2022
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Training language models to follow instructions with human feedback
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll L. Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kelton, Luke Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul F. Christiano, Jan Leike, and Ryan Lowe. 2022 · 2022
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MICO: A multi-alternative contrastive learning framework for commonsense knowledge representation
Ying Su, Zihao Wang, Tianqing Fang, Hongming Zhang, Yangqiu Song, and Tong Zhang. 2022 · 2022
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Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed H. Chi, Quoc V. Le, and Denny Zhou. 2022 · 2022
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Symbolic knowledge distillation: from general language models to commonsense models
Peter West, Chandra Bhagavatula, Jack Hessel, Jena D. Hwang, Liwei Jiang, Ronan Le Bras, Ximing Lu, Sean Welleck, and Yejin Choi. 2022 · 2022
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Deep bidirectional language-knowledge graph pretraining
Michihiro Yasunaga, Antoine Bosselut, Hongyu Ren, Xikun Zhang, Christopher D. Manning, Percy Liang, and Jure Leskovec. 2022 · 2022
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Folkscope: Intention knowledge graph construction for discovering e-commerce commonsense
Changlong Yu, Weiqi Wang, Xin Liu, Jiaxin Bai, Yangqiu Song, Zheng Li, Yifan Gao, Tianyu Cao, and Bing Yin. 2022 · 2022
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Contrastive knowledge graph error detection
Qinggang Zhang, Junnan Dong, Keyu Duan, Xiao Huang, Yezi Liu, and Linchuan Xu. 2022b · 2022
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Complex query answering on eventuality knowledge graph with implicit logical constraints
Jiaxin Bai, Xin Liu, Weiqi Wang, Chen Luo, and Yangqiu Song. 2023 · 2023
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Say what you mean! large language models speak too positively about negative commonsense knowledge
Jiangjie Chen, Wei Shi, Ziquan Fu, Sijie Cheng, Lei Li, and Yanghua Xiao. 2023 · 2023
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Neural compositional rule learning for knowledge graph reasoning
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CKBP v2: An expert-annotated evaluation set for commonsense knowledge base population
Tianqing Fang, Quyet V. Do, Sehyun Choi, Weiqi Wang, and Yangqiu Song. 2023 · 2023
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Mapping and cleaning open commonsense knowledge bases with generative translation
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CAT: A contextualized conceptualization and instantiation framework for commonsense reasoning
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COLA: contextualized commonsense causal reasoning from the causal inference perspective
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Grounded conversation generation as guided traverses in commonsense knowledge graphs
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