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Foundation models in language and vision have the ability to run inference on any textual and visual inputs thanks to the transferable representations such as a vocabulary of tokens in language.
Yago3: A knowledge base from multilingual wikipedias
Farzaneh Mahdisoltani, Joanna Biega, and Fabian Suchanek · 2014
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Observed versus latent features for knowledge base and text inference
Kristina Toutanova and Danqi Chen · 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
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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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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Systematic integration of biomedical knowledge prioritizes drugs for repurposing
Daniel Scott Himmelstein, Antoine Lizee, Christine Hessler, Leo Brueggeman, Sabrina L Chen, Dexter Hadley, Ari Green, Pouya Khankhanian, and Sergio E Baranzini · 2017
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Semi-supervised classification with graph convolutional networks
Thomas N. Kipf and Max Welling · 2017
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DeepPath: A reinforcement learning method for knowledge graph reasoning
Wenhan Xiong, Thien Hoang, and William Yang Wang · 2017
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Convolutional 2d knowledge graph embeddings
Tim Dettmers, Pasquale Minervini, Pontus Stenetorp, and Sebastian Riedel · 2018
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Improving knowledge graph embedding using simple constraints
Boyang Ding, Quan Wang, Bin Wang, and Li Guo · 2018
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Challenges and innovations in building a product knowledge graph
Xin Luna Dong · 2018
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Graph attention networks
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio · 2018
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Link prediction based on graph neural networks
Muhan Zhang and Yixin Chen · 2018
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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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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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Distance encoding: Design provably more powerful neural networks for graph representation learning
Pan Li, Yanbang Wang, Hongwei Wang, and Jure Leskovec · 2020
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Dynamic anticipation and completion for multi-hop reasoning over sparse knowledge graph
Xin Lv, Xu Han, Lei Hou, Juanzi Li, Zhiyuan Liu, Wei Zhang, Yichi Zhang, Hao Kong, and Suhui Wu · 2020
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Commonsense knowledge base completion with structural and semantic context
Chaitanya Malaviya, Chandra Bhagavatula, Antoine Bosselut, and Yejin Choi · 2020
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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 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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Few-shot knowledge graph completion
Chuxu Zhang, Huaxiu Yao, Chao Huang, Meng Jiang, Zhenhui Li, and Nitesh V Chawla · 2020
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Bringing light into the dark: A large-scale evaluation of knowledge graph embedding models under a unified framework
Mehdi Ali, Max Berrendorf, Charles Tapley Hoyt, Laurent Vermue, Mikhail Galkin, Sahand Sharifzadeh, Asja Fischer, Volker Tresp, and Jens Lehmann · 2021
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On the opportunities and risks of foundation models
Rishi Bommasani, Drew A. Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S. Bernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, Erik Brynjolfsson, S. Buch, Dallas Card, Rodrigo Castellon, Niladri S. Chatterji, Annie S. Chen, Kathleen A. Creel, Jared Davis, Dora Demszky, Chris Donahue, Moussa Doumbouya, Esin Durmus, Stefano Ermon, John Etchemendy, Kawin Ethayarajh, Li Fei-Fei, Chelsea Finn, Trevor Gale, Lauren E. Gillespie, Karan Goel, Noah D. Goodman, Shelby Grossman, Neel Guha, Tatsunori Hashimoto, Peter Henderson, John Hewitt, Daniel E. Ho, Jenny Hong, Kyle Hsu, Jing Huang, Thomas F. Icard, Saahil Jain, Dan Jurafsky, Pratyusha Kalluri, Siddharth Karamcheti, Geoff Keeling, Fereshte Khani, O. Khattab, Pang Wei Koh, Mark S. Krass, Ranjay Krishna, Rohith Kuditipudi, Ananya Kumar, Faisal Ladhak, Mina Lee, Tony Lee, Jure Leskovec, Isabelle Levent, Xiang Lisa Li, Xuechen Li, Tengyu Ma, Ali Malik, Christopher D. Manning, Suvir P. Mirchandani, Eric Mitchell, Zanele Munyikwa, Suraj Nair, Avanika Narayan, Deepak Narayanan, Benjamin Newman, Allen Nie, Juan Carlos Niebles, Hamed Nilforoshan, J. F. Nyarko, Giray Ogut, Laurel Orr, Isabel Papadimitriou, Joon Sung Park, Chris Piech, Eva Portelance, Christopher Potts, Aditi Raghunathan, Robert Reich, Hongyu Ren, Frieda Rong, Yusuf H. Roohani, Camilo Ruiz, Jack Ryan, Christopher R’e, Dorsa Sadigh, Shiori Sagawa, Keshav Santhanam, Andy Shih, Krishna Parasuram Srinivasan, Alex Tamkin, Rohan Taori, Armin W. Thomas, Florian Tramèr, Rose E. Wang, William Wang, Bohan Wu, Jiajun Wu, Yuhuai Wu, Sang Michael Xie, Michihiro Yasunaga, Jiaxuan You, Matei A. Zaharia, Michael Zhang, Tianyi Zhang, Xikun Zhang, Yuhui Zhang, Lucia Zheng, Kaitlyn Zhou, and Percy Liang · 2021
StATIK: Structure and text for inductive knowledge graph completion
Elan Markowitz, Keshav Balasubramanian, Mehrnoosh Mirtaheri, Murali Annavaram, Aram Galstyan, and Greg Ver Steeg · 2022
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Vineeth Venugopal, Sumit Pai, and Elsa Olivetti · 2022
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Knowledge graph reasoning with relational digraph
Yongqi Zhang and Quanming Yao · 2022
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Ood link prediction generalization capabilities of message-passing gnns in larger test graphs
Yangze Zhou, Gitta Kutyniok, and Bruno Ribeiro · 2022
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Neural-symbolic models for logical queries on knowledge graphs
Zhaocheng Zhu, Mikhail Galkin, Zuobai Zhang, and Jian Tang · 2022
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Relation prediction as an auxiliary training objective for improving multi-relational graph representations
Yihong Chen, Pasquale Minervini, Sebastian Riedel, and Pontus Stenetorp · 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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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby · 2021
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BiQUE: Biquaternionic embeddings of knowledge graphs
Jia Guo and Stanley Kok · 2021
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Indigo: Gnn-based inductive knowledge graph completion using pair-wise encoding
Shuwen Liu, Bernardo Grau, Ian Horrocks, and Egor Kostylev · 2021
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Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever · 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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Graph neural networks for link prediction with subgraph sketching
Benjamin Paul Chamberlain, Sergey Shirobokov, Emanuele Rossi, Fabrizio Frasca, Thomas Markovich, Nils Yannick Hammerla, Michael M. Bronstein, and Max Hansmire · 2023
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Building a knowledge graph to enable precision medicine
Payal Chandak, Kexin Huang, and Marinka Zitnik · 2023
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Generalizing to unseen elements: A survey on knowledge extrapolation for knowledge graphs
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On over-squashing in message passing neural networks: The impact of width, depth, and topology
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Double permutation equivariance for knowledge graph completion
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Relational message passing for fully inductive knowledge graph completion
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Exploring & exploiting high-order graph structure for sparse knowledge graph completion
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InGram: Inductive knowledge graph embedding via relation graphs
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The materials experiment knowledge graph
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Llama 2: Open foundation and fine-tuned chat models
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Adaprop: Learning adaptive propagation for graph neural network based knowledge graph reasoning
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Towards predicting equilibrium distributions for molecular systems with deep learning
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An ood multi-task perspective for link prediction with new relation types and nodes
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A*net: A scalable path-based reasoning approach for knowledge graphs
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