科技创新2030—“新一代人工智能"重大项目(2018AAA0102100)
国家自然科学基金(61772525,61876183,U1636220)
[1] Thomas M. Siebel. Digital Transformation Survive and Thrive in an Era of Mass Extinction. America: RosettaBooks, 2019. 23--50. Google Scholar
[2] Tang J, Chen W G. Deep analytics and mining for big social data. Chin Sci Bull, 2015, 60: 509-519 CrossRef Google Scholar
[3] Ding X, Li Z Y, Liu T, Liao K. ELG: an event logic graph. 2019,. arXiv Google Scholar
[4] Mnih V, Kavukcuoglu K, Silver D. Human-level control through deep reinforcement learning. Nature, 2015, 518: 529-533 CrossRef ADS Google Scholar
[5] Wang J P, Shi Y K, Zhang W S. Multitask Policy Adversarial Learning for Human-Level Control With Large State Spaces. IEEE Trans Ind Inf, 2019, 15: 2395-2404 CrossRef Google Scholar
[6] LIU Q, LI Y, YANG D H, et al. Knowledge graph construction techniques. Journal of Computer Research and Development, 2016, 53(3): 582-600 doi: 10.7544/issn1000-1239.2016.20148228. Google Scholar
[7] LIU Z Y, SUN M S, LIN Y K, et al. Knowledge representation learning: a review. Journal of Computer Research and Development, 2016, 53(2): 1-16 doi: 10.7544/issn1000-1239.2016.20160020. Google Scholar
[8] Sharad Rawat, M.-H. Herman Shen. A Novel Topology Optimization Approach using Conditional Deep Learning,. arXiv Google Scholar
[9] Zhou Z H, Zhang M L, Huang S J. Multi-instance multi-label learning. Artificial Intelligence, 2012, 176: 2291-2320 CrossRef Google Scholar
[10] Socher R, Milind G, Christopher D M, Andrew Y. Ng. Zero-Shot Learning Through Cross-Modal Transfer. In: Proceedings of Advances in Neural Information Processing Systems, San Francisco, 2013.1-10. Google Scholar
[11] Zeng D J, Liu K, Lai S W, Zhou G Y, Zhao J. Relation classification via convolutional deep neural network. In: Proceedings of the 25th International Conference on Computational Linguistics, 2014. 2335--2344. Google Scholar
[12] Zhang D X, Wang D. Relation classification via recurrent neural network. 2015,. arXiv Google Scholar
[13] Miwa M, Bansa M. End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures. In: Proceedings of the 53th Annual Meeting of the Association for Computational Linguistics, Beijing, 2016. 1105--1116. Google Scholar
[14] Feng C, Kang L Q, Shi G, et al. Causality extraction with GAN. Acta Autom Sin, 2018, 44: 811--818. Google Scholar
[15] Perozzi B, Al-Rfou R, Skiena S. Deepwalk: online learning of social representations. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, 2014. 701--710. Google Scholar
[16] Petar V, Guillem C, Arantxa C, et al. Graph attention networks. 2017,. arXiv Google Scholar
[17] Wang D, Cui P, Zhu W. Structural deep network embedding. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016. 1225--1234. Google Scholar
[18] Chang S, Han W, Tang J, et al. Heterogeneous network embedding via deep architectures. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2015. 119--128. Google Scholar
[19] Gui H, Liu J L, Tao F B, et al. Large-scale embedding learning in heterogeneous event data. In: Proceedings of IEEE 16th International Conference on Data Mining (ICDM), Beijing, 2016. Google Scholar
[20] Xu L C, Wei X K, Cao J N, et al. Embedding of embedding: joint embedding for coupled heterogeneous networks. In: Proceedings of the 10th ACM International Conference on Web Search and Data Mining, 2017. 741--749. Google Scholar
[21] Mehrkanoon S, Suykens J A K. Regularized Semipaired Kernel CCA for Domain Adaptation. IEEE Trans Neural Netw Learning Syst, 2017, : 1-15 CrossRef Google Scholar
[22] Mihalkova L, Huynh T N, Raymond J M. Mapping and revising Markov logic networks for transfer learning. In: Proceedings of the 22nd Conference on Artificial Intelligence, Canada, 2007. 608--614. Google Scholar
[23] Parisotto E, Ba J L, Salakhutdinov R. Actor-mimic: deep multitask and transfer reinforcement learning. In: Proceedings of the International Conference on Learning Representations, Puerto Rico, 2016. 156--171. Google Scholar
[24] Rusu A A, Colmenarejo S G, Gulcehre C, et al. Policy distillation. 2016,. arXiv Google Scholar
[25] Silver D, Huang A, Maddison C J. Mastering the game of Go with deep neural networks and tree search. Nature, 2016, 529: 484-489 CrossRef ADS Google Scholar
[26] Wang J P, Sun Y C, Zhang W S. Large-Scale Online Multitask Learning and Decision Making for Flexible Manufacturing. IEEE Trans Ind Inf, 2016, 12: 2139-2147 CrossRef Google Scholar
[27] Xu K, Ba J, et al. Show, attend and tell: neural image caption generation with visual attention. In: Proceedings of the 32nd International Conference on Machine Learning, 2015. 2048--2057. Google Scholar
[28] Karpathy A, Fei-Fei L. Deep Visual-Semantic Alignments for Generating Image Descriptions. IEEE Trans Pattern Anal Mach Intell, 2017, 39: 664-676 CrossRef Google Scholar
[29] Zhou J B, Gou S H, Hu R J, et al. A collaborative learning framework to tag refinement for points of interest. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2019. 1752--1761. Google Scholar
[30] David M B, John D L. Correlated topic models. In: Proceedings of the Advances in Neural Information Processing Systems, 2006, 18:147-154. Google Scholar
[31] Ranganath R, Gerrish S, Blei D M. Black box variational inference. In: Proceedings of the 17th International Conference on Artificial Intelligence and Statistics, Reykjavik, 2014. 814--822. Google Scholar
[32] Sun T, Sheldon D, Kumar A. Message passing for collective graphical models. In: Proceedings of the 32nd International Conference on Machine Learning, France, 2015. 853--861. Google Scholar
[33] Bernhard P. Disjunctive normal form. In: Encyclopedia of Machine Learning. Boston: Springer, 2017. 371--372. Google Scholar
[34] Wu Z H, Pan S R, Chen F W, et al. A comprehensive survey on graph neural networks. 2019,. arXiv Google Scholar
[35] Levie R, Monti F, Bresson X. CayleyNets: Graph Convolutional Neural Networks With Complex Rational Spectral Filters. IEEE Trans Signal Process, 2019, 67: 97-109 CrossRef Google Scholar
[36] Silver D, Huang A, Maddison C J. Mastering the game of Go with deep neural networks and tree search. Nature, 2016, 529: 484-489 CrossRef ADS Google Scholar
[37] Meng Q, Yoshua B, Jian T, et al. GMNN: graph markov neural networks. In: Proceedings of the 36th International Conference on Machine Learning, 2019. 5241--5250. Google Scholar
[38] Michelle A L, Yuke Z, Peter Z, et al. Making sense of vision and touch: learning multimodal representations for contact-rich tasks. 2019,. arXiv Google Scholar
[39] Zhu S Y, Ng I, Chen Z T. Causal discovery with reinforcement learning. 2019,. arXiv Google Scholar
Figure 1
(Color online) The event cognitive vault concepts, key techniques and applications
Figure 2
(Color online) The hierarchical structure of event cognitive vault
Figure 3
(Color online) The multi-mode joint network synthesis representation technology map
Figure 4
(Color online) The event logical situational evolutionary cognition technology map
Figure 5
(Color online) The event cognitive vault-based prevent network fraud system
Figure 6
(Color online) The event cognitive vault-based active warning system for all production risks
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