Fetching the paper…
Reading the bibliography…
Knowledge Graph Embeddings (KGE) aim to map entities and relations to low dimensional spaces and have become the \textit{de-facto} standard for knowledge graph completion.
WordNet: a lexical database for English
George A Miller. 1995 · 1995
Earlier work this paper cites.
Dimensionality reduction by learning an invariant mapping. In 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’06) , Vol. 2. IEEE, 1735–1742
Raia Hadsell, Sumit Chopra, and Yann LeCun. 2006 · 2006
Earlier work this paper cites.
Statistical predicate invention. In Proceedings of the 24th international conference on Machine learning . 433–440
Stanley Kok and Pedro Domingos. 2007 · 2007
Earlier work this paper cites.
Yago: a core of semantic knowledge. In Proceedings of the 16th international conference on World Wide Web . 697–706
Fabian M Suchanek, Gjergji Kasneci, and Gerhard Weikum. 2007 · 2007
Earlier work this paper cites.
Freebase: a collaboratively created graph database for structuring human knowledge. In Proceedings of the 2008 ACM SIGMOD international conference on Management of data . 1247–1250
Kurt Bollacker, Colin Evans, Praveen Paritosh, Tim Sturge, and Jamie Taylor. 2008 · 2008
Earlier work this paper cites.
Visualizing data using t-SNE
Laurens Van der Maaten and Geoffrey Hinton. 2008 · 2008
Earlier work this paper cites.
Multi-task feature learning for knowledge graph enhanced recommendation. In The world wide web conference . 2000–2010
Hongwei Wang, Fuzheng Zhang, Miao Zhao, Wenjie Li, Xing Xie, and Minyi Guo. 2019 · 2010
Earlier work this paper cites.
A three-way model for collective learning on multi-relational data. In Icml
Maximilian Nickel, Volker Tresp, and Hans-Peter Kriegel. 2011 · 2011
Earlier work this paper cites.
Translating embeddings for modeling multi-relational data
Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, and Oksana Yakhnenko. 2013 · 2013
Earlier work this paper cites.
Knowledge vault: A web-scale approach to probabilistic knowledge fusion. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining . 601–610
Xin Dong, Evgeniy Gabrilovich, Geremy Heitz, Wilko Horn, Ni Lao, Kevin Murphy, Thomas Strohmann, Shaohua Sun, and Wei Zhang. 2014 · 2014
Earlier work this paper cites.
Knowledge graph embedding by translating on hyperplanes. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 28
Zhen Wang, Jianwen Zhang, Jianlin Feng, and Zheng Chen. 2014 · 2014
Earlier work this paper cites.
Learning entity and relation embeddings for knowledge graph completion. In Twenty-ninth AAAI conference on artificial intelligence
Yankai Lin, Zhiyuan Liu, Maosong Sun, Yang Liu, and Xuan Zhu. 2015 · 2015
Earlier work this paper cites.
Observed versus latent features for knowledge base and text inference. In Proceedings of the 3rd workshop on continuous vector space models and their compositionality . 57–66
Kristina Toutanova and Danqi Chen. 2015 · 2015
Earlier work this paper cites.
Embedding Entities and Relations for Learning and Inference in Knowledge Bases. In Proceedings of the International Conference on Learning Representations (ICLR) 2015
Bishan Yang, Scott Wen-tau Yih, Xiaodong He, Jianfeng Gao, and Li Deng. 2015 · 2015
Earlier work this paper cites.
Large-Margin Softmax Loss for Convolutional Neural Networks. In ICML
Weiyang Liu, Yandong Wen, Zhiding Yu, and Meng Yang. 2016 · 2016
Earlier work this paper cites.
Holographic embeddings of knowledge graphs. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 30
Maximilian Nickel, Lorenzo Rosasco, and Tomaso Poggio. 2016 · 2016
Earlier work this paper cites.
Object detection meets knowledge graphs. International Joint Conferences on Artificial Intelligence
Yuan Fang, Kingsley Kuan, Jie Lin, Cheston Tan, and Vijay Chandrasekhar. 2017 · 2017
Earlier work this paper cites.
Automatic differentiation in pytorch
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer. 2017 · 2017
Earlier work this paper cites.
Sparsity and noise: Where knowledge graph embeddings fall short. In Proceedings of the 2017 conference on empirical methods in natural language processing . 1751–1756
Jay Pujara, Eriq Augustine, and Lise Getoor. 2017 · 2017
Earlier work this paper cites.
DeepPath: A Reinforcement Learning Method for Knowledge Graph Reasoning. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing . 564–573
Wenhan Xiong, Thien Hoang, and William Yang Wang. 2017 · 2017
Earlier work this paper cites.
A Novel Embedding Model for Knowledge Base Completion Based on Convolutional Neural Network. In Proceedings of NAACL-HLT . 327–333
Tu Dinh Nguyen Dai Quoc Nguyen, Dat Quoc Nguyen, and Dinh Phung. 2018 · 2018
Earlier work this paper cites.
Convolutional 2d knowledge graph embeddings. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 32
Tim Dettmers, Pasquale Minervini, Pontus Stenetorp, and Sebastian Riedel. 2018 · 2018
Earlier work this paper cites.
Openke: An open toolkit for knowledge embedding. In Proceedings of the 2018 conference on empirical methods in natural language processing: system demonstrations . 139–144
Xu Han, Shulin Cao, Xin Lv, Yankai Lin, Zhiyuan Liu, Maosong Sun, and Juanzi Li. 2018 · 2018
Earlier work this paper cites.
Multi-Hop Knowledge Graph Reasoning with Reward Shaping. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing . 3243–3253
Xi Victoria Lin, Richard Socher, and Caiming Xiong. 2018 · 2018
Earlier work this paper cites.
Never-ending learning
Tom Mitchell, William Cohen, Estevam Hruschka, Partha Talukdar, Bishan Yang, Justin Betteridge, Andrew Carlson, Bhavana Dalvi, Matt Gardner, Bryan Kisiel, et al · 2018
Earlier work this paper cites.
Modeling relational data with graph convolutional networks. In European semantic web conference . Springer, 593–607
Michael Schlichtkrull, Thomas N Kipf, Peter Bloem, Rianne van den Berg, Ivan Titov, and Max Welling. 2018 · 2018
Earlier work this paper cites.
RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space. In International Conference on Learning Representations
Zhiqing Sun, Zhi-Hong Deng, Jian-Yun Nie, and Jian Tang. 2018 · 2018
Earlier work this paper cites.
Graph Attention Networks. In International Conference on Learning Representations
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio. 2018 · 2018
Earlier work this paper cites.
Hypernetwork Knowledge Graph Embeddings. In International Conference on Artificial Neural Networks
Ivana Balažević, Carl Allen, and Timothy M Hospedales. 2019 · 2019
Cited alongside, same era.
Multi-relational poincaré graph embeddings
Ivana Balazevic, Carl Allen, and Timothy Hospedales. 2019 · 2019
Cited alongside, same era.
TuckER: Tensor Factorization for Knowledge Graph Completion. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) . 5185–5194
Ivana Balažević, Carl Allen, and Timothy Hospedales. 2019 · 2019
Cited alongside, same era.
A2N: Attending to neighbors for knowledge graph inference. In Proceedings of the 57th annual meeting of the association for computational linguistics . 4387–4392
Trapit Bansal, Da-Cheng Juan, Sujith Ravi, and Andrew McCallum. 2019 · 2019
Cited alongside, same era.
Learning imbalanced datasets with label-distribution-aware margin loss
5* knowledge graph embeddings with projective transformations. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 35. 9064–9072
Mojtaba Nayyeri, Sahar Vahdati, Can Aykul, and Jens Lehmann. 2021 · 2021
Later among the works it cites.
Learning transferable visual models from natural language supervision. In International Conference on Machine Learning . PMLR, 8748–8763
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
Later among the works it cites.
Learning to ask appropriate questions in conversational recommendation. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval . 808–817
Xuhui Ren, Hongzhi Yin, Tong Chen, Hao Wang, Zi Huang, and Kai Zheng. 2021 · 2021
Later among the works it cites.
Understanding the behaviour of contrastive loss. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition . 2495–2504
Feng Wang and Huaping Liu. 2021 · 2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Kaidi Cao, Colin Wei, Adrien Gaidon, Nikos Arechiga, and Tengyu Ma. 2019b · 2019
Cited alongside, same era.
PyTorch Lightning
William Falcon and The PyTorch Lightning team. 2019 · 2019
Cited alongside, same era.
Fast graph representation learning with PyTorch Geometric
Matthias Fey and Jan Eric Lenssen. 2019 · 2019
Cited alongside, same era.
I know the relationships: Zero-shot action recognition via two-stream graph convolutional networks and knowledge graphs. In Proceedings of the AAAI conference on artificial intelligence , Vol. 33. 8303–8311
Junyu Gao, Tianzhu Zhang, and Changsheng Xu. 2019 · 2019
Cited alongside, same era.
Semi-Supervised Classification with Graph Convolutional Networks
Thomas N Kipf and Max Welling. 2019 · 2019
Cited alongside, same era.
Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics . 4710–4723
Deepak Nathani, Jatin Chauhan, Charu Sharma, and Manohar Kaul. 2019 · 2019
Cited alongside, same era.
End-to-end structure-aware convolutional networks for knowledge base completion. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 33. 3060–3067
Chao Shang, Yun Tang, Jing Huang, Jinbo Bi, Xiaodong He, and Bowen Zhou. 2019 · 2019
Cited alongside, same era.
Composition-based Multi-Relational Graph Convolutional Networks. In International Conference on Learning Representations
Shikhar Vashishth, Soumya Sanyal, Vikram Nitin, and Partha Talukdar. 2019 · 2019
Cited alongside, same era.
DisenKGAT: knowledge graph embedding with disentangled graph attention network. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management . 2140–2149
Junkang Wu, Wentao Shi, Xuezhi Cao, Jiawei Chen, Wenqiang Lei, Fuzheng Zhang, Wei Wu, and Xiangnan He. 2021 · 2021
Later among the works it cites.
Knowledge-enhanced hierarchical graph transformer network for multi-behavior recommendation. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 35. 4486–4493
Lianghao Xia, Chao Huang, Yong Xu, Peng Dai, Xiyue Zhang, Hongsheng Yang, Jian Pei, and Liefeng Bo. 2021 · 2021
Later among the works it cites.
Neural bellman-ford networks: A general graph neural network framework for link prediction
Zhaocheng Zhu, Zuobai Zhang, Louis-Pascal Xhonneux, and Jian Tang. 2021b · 2021
Later among the works it cites.
Geometry Interaction Knowledge Graph Embeddings. In AAAI Conference on Artificial Intelligence
Zongsheng Cao, Qianqian Xu, Zhiyong Yang, Xiaochun Cao, and Qingming Huang. 2022 · 2022
Closest in time.
CONTaiNER: Few-Shot Named Entity Recognition via Contrastive Learning. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) . 6338–6353
Sarkar Snigdha Sarathi Das, Arzoo Katiyar, Rebecca J Passonneau, and Rui Zhang. 2022 · 2022
Closest in time.
Molecular contrastive learning with chemical element knowledge graph. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 36. 3968–3976
Yin Fang, Qiang Zhang, Haihong Yang, Xiang Zhuang, Shumin Deng, Wen Zhang, Ming Qin, Zhuo Chen, Xiaohui Fan, and Huajun Chen. 2022 · 2022
Closest in time.
Path Language Modeling over Knowledge Graphsfor Explainable Recommendation. In Proceedings of the ACM Web Conference 2022 . 946–955
Shijie Geng, Zuohui Fu, Juntao Tan, Yingqiang Ge, Gerard De Melo, and Yongfeng Zhang. 2022 · 2022
Closest in time.
Graph Communal Contrastive Learning. In Proceedings of the ACM Web Conference 2022 . 1203–1213
Bolian Li, Baoyu Jing, and Hanghang Tong. 2022 · 2022
Closest in time.
Zero-Shot Stance Detection via Contrastive Learning. In Proceedings of the ACM Web Conference 2022 . 2738–2747
Bin Liang, Zixiao Chen, Lin Gui, Yulan He, Min Yang, and Ruifeng Xu. 2022 · 2022
Closest in time.
CAKE: A Scalable Commonsense-Aware Framework For Multi-View Knowledge Graph Completion. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) . 2867–2877
Guanglin Niu, Bo Li, Yongfei Zhang, and Shiliang Pu. 2022 · 2022
Closest in time.
CGC: Contrastive Graph Clustering forCommunity Detection and Tracking. In Proceedings of the ACM Web Conference 2022 . 1115–1126
Namyong Park, Ryan Rossi, Eunyee Koh, Iftikhar Ahamath Burhanuddin, Sungchul Kim, Fan Du, Nesreen Ahmed, and Christos Faloutsos. 2022 · 2022
Closest in time.
Sequence-to-Sequence Knowledge Graph Completion and Question Answering. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) . 2814–2828
Apoorv Saxena, Adrian Kochsiek, and Rainer Gemulla. 2022 · 2022
Closest in time.
Swift and Sure: Hardness-aware Contrastive Learning for Low-dimensional Knowledge Graph Embeddings. In Proceedings of the ACM Web Conference 2022 . 838–849
Kai Wang, Yu Liu, and Quan Z Sheng. 2022a · 2022
Closest in time.
ClusterSCL: Cluster-Aware Supervised Contrastive Learning on Graphs. In Proceedings of the ACM Web Conference 2022 . 1611–1621
Yanling Wang, Jing Zhang, Haoyang Li, Yuxiao Dong, Hongzhi Yin, Cuiping Li, and Hong Chen. 2022b · 2022
Closest in time.
SimGRACE: A Simple Framework for Graph Contrastive Learning without Data Augmentation. In Proceedings of the ACM Web Conference 2022 . 1070–1079
Jun Xia, Lirong Wu, Jintao Chen, Bozhen Hu, and Stan Z Li. 2022 · 2022
Closest in time.
Sequence level contrastive learning for text summarization. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 36. 11556–11565
Shusheng Xu, Xingxing Zhang, Yi Wu, and Furu Wei. 2022 · 2022
Closest in time.
Dual Space Graph Contrastive Learning. In Proceedings of the ACM Web Conference 2022 . 1238–1247
Haoran Yang, Hongxu Chen, Shirui Pan, Lin Li, Philip S Yu, and Guandong Xu. 2022a · 2022
Closest in time.
Knowledge Graph Contrastive Learning for Recommendation. In SIGIR ’22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, Madrid, Spain, July 11 - 15, 2022 , Enrique Amigó, Pablo Castells, Julio Gonzalo, Ben Carterette, J. Shane Culpepper, and Gabriella Kazai (Eds.). ACM, 1434–1443
Yuhao Yang, Chao Huang, Lianghao Xia, and Chenliang Li. 2022b · 2022
Closest in time.
KCD: Knowledge Walks and Textual Cues Enhanced Political Perspective Detection in News Media. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies . Association for Computational Linguistics, Seattle, United States, 4129–4140
Wenqian Zhang, Shangbin Feng, Zilong Chen, Zhenyu Lei, Jundong Li, and Minnan Luo. 2022 · 2022
Closest in time.
Knowledge graph reasoning with relational digraph. In Proceedings of the ACM Web Conference 2022 . 912–924
Yongqi Zhang and Quanming Yao. 2022 · 2022
Closest in time.
Eventbert: A pre-trained model for event correlation reasoning. In Proceedings of the ACM Web Conference 2022 . 850–859
Yucheng Zhou, Xiubo Geng, Tao Shen, Guodong Long, and Daxin Jiang. 2022 · 2022
Closest in time.
Complex embeddings for simple link prediction. In International conference on machine learning . PMLR, 2071–2080
Théo Trouillon, Johannes Welbl, Sebastian Riedel, Éric Gaussier, and Guillaume Bouchard. 2016 · 2080
Closest in time.
Graph contrastive learning with adaptive augmentation. In Proceedings of the Web Conference 2021 . 2069–2080
Yanqiao Zhu, Yichen Xu, Feng Yu, Qiang Liu, Shu Wu, and Liang Wang. 2021a · 2080
Closest in time.