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SCIENTIA SINICA Informationis, Volume 50 , Issue 7 : 988-1002(2020) https://doi.org/10.1360/SSI-2019-0273

Constructing and inferring event logic cognitive graph in the field of big data

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  • ReceivedDec 4, 2019
  • AcceptedApr 28, 2020
  • PublishedJul 13, 2020

Abstract

Along with the increasing impact of big data on the economic operation mechanism, including global production, circulation, distribution and consumption, event logic (EL) is becoming an important concept in mining and releasing the potential value of big data. Besides its critical role in exploring the clues and rules of digital economy, EL will also help people better understand the future development of the ternary human-cyber-physical universe. Considering the dynamic evolution of the EL is the essential difference between industrial big data and other big data. Therefore, we propose a constructing and inferring EL cognitive graph in the field of big data. Firstly, we introduce the great opportunities offered by the knowledge graph in promoting the high-quality development of the manufacturing industry, leading the economic development of the new digital manufacturing industry, and promoting the cross-development of sociology and information science. Secondly, we present the construction technology of EL cognitive graph and expound the progress and critical technical challenges in the research of the EL cognitive graph at home and abroad, in the aspects of multi-granularity acquisition and representation of EL, multimodal association and fusion, evolution analysis and mining of EL behavior. Finally, combined with the research progress of our works in EL cognitive graph, we summarize the effect of EL cognitive graph in the prevention of network fraud and the control of safe production as well as summarize and anticipate the future research direction and development prospect of the industry.


Funded by

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)


References

[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

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