Fragmentation knowledge processing and networked artificial intelligence}{Fragmentation knowledge processing and networked artificial intelligence

Jianji WANG$^†$; Yongqiang MA$^†$; Shitao CHEN; Ziyi LIU; Nanning ZHENG$^*$

doi:10.1360/N112016-00120

SCIENTIA SINICA Informationis

Download PDF

SCIENTIA SINICA Informationis, Volume 47 , Issue 2 : 171-192(2017) https://doi.org/10.1360/N112016-00120

Fragmentation knowledge processing and networked artificial intelligence}{Fragmentation knowledge processing and networked artificial intelligence

Jianji WANG$^†$ , Yongqiang MA$^†$ , Shitao CHEN , Ziyi LIU , Nanning ZHENG$^*$ ^*

More info

ReceivedMay 6, 2016
AcceptedSep 24, 2016
PublishedFeb 13, 2017

Previous Table of Contents Next

Rating

Abstract

The Internet is responsible for the explosive growth of information and its accelerated and diversified dissemination. Human society has entered an unprecedented era of fragmented knowledge. Fragmented knowledge on the Internet is the product of diverse human behavior, thinking, and interactions. Traditional artificial intelligence methods try to intellectualize machines through an expert system established by knowledge engineering, which cannot effectively process and utilize fragmented knowledge. The summarization, reorganization, and rediscovery of the huge amount of fragmented knowledge are thus crucial scientific challenges in information science and artificial intelligence research. We analyze the limitations of traditional artificial intelligence methods associated with the large-scale processing of fragmented knowledge, discuss the basics of processing, organizing, and obtaining fragmented knowledge and the concept of networking artificial intelligence, introduce a law discovery method that can reveal the evolution of big data, and explore the hot topics of future research.

Funded by

国家重点研发计划(2016YFB1000903)

国家高技术研究发展计划(863)

(2014AA01A301)

fragmented knowledge

law discovery

networking artificial intelligence

machine reasoning

collective intelligence network

knowledge discovery

References

[1] Kompella V P, Pasquale J C, Polyzos G C. Multicast routing for multimedia communication. IEEE/ACM Trans Netw, 2015, 1: 286-292. Google Scholar

[2] Akyildiz I F, Melodia T, Chowdury K R. Wireless multimedia sensor networks: a survey. IEEE Wirel Commun, 2008, 14: 32-39. Google Scholar

[3] Fargues J, Landau M C, Dugourd A, et al. Conceptual graphs for semantics and knowledge processing. IBM J Res Dev, 1986, 30: 70-79 CrossRef Google Scholar

[4] Duda R O, Shortliffe E H. Expert systems research. Science, 1983, 220: 261-268 CrossRef Google Scholar

[5] Liao S H. Expert system methodologies and applications--a decade review from 1995 to 2004. Expert Syst Appl, 2005, 28: 93-103 CrossRef Google Scholar

[6] Lennartson B, Sundberg K. Expert system. US Patent No. 8,064,901, 2011. Google Scholar

[7] Zheng N N. Information processing for cognition process and new artificial intelligent systems. China Basic Sci, 2000, 8: 9-18 [郑南宁. 认知过程的信息处理和新型人工智能系统. 中国基础科学, 2000, 8: 9-18]. Google Scholar

[8] Reagans R, Mcevily B. Network structure and knowledge transfer: the effects of cohesion and range. Admin Sci Quart, 2003, 48: 240-267 CrossRef Google Scholar

[9] Holland J H. Adaptation in Natural and Artificial Systems: an Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. Ann Arbor: University of Michigan Press, 1975. Google Scholar

[10] Marr D. Artificial intelligence--a personal view. Artif Intell, 1977, 9: 37-48 CrossRef Google Scholar

[11] Russell S J, Norvig P, Canny J F, et al. Artificial Intelligence: A Modern Approach. 2nd ed. Upper Saddle River: Prentice Hall, 2003. Google Scholar

[12] Feigenbaum E A. The art of artificial intelligence: themes and case studies of knowledge engineering. In: Proceedings of the 5th International Joint Conference on Artificial Intelligence. Burlington: Morgan Kaufmann Publishers, 1977. 1014-1029. Google Scholar

[13] Studer R, Benjamins V R, Fensel D. Knowledge engineering: principles and methods. Data Knowl Eng, 1998, 25: 161-197 CrossRef Google Scholar

[14] Lindsay R K, Buchanan B G, Feigenbaum E A, et al. DENDRAL: a case study of the first expert system for scientific hypothesis formation. Artif Intell, 1993, 61: 209-261 CrossRef Google Scholar

[15] Lenat D B. CYC: a large-scale investment in knowledge infrastructure. Commun ACM, 1995, 38: 33-38. Google Scholar

[16] Ong T H, Chen H, Sung W K, et al. Newsmap: a knowledge map for online news. Decis Support Syst, 2005, 39: 583-597 CrossRef Google Scholar

[17] Nickel M, Murphy K, Tresp V, et al. A review of relational machine learning for knowledge graphs: from multi-relational link prediction to automated knowledge graph construction. ArXiv:1503.00759, 2015. Google Scholar

[18] Wagner C. Breaking the knowledge acquisition bottleneck through conversational knowledge management. Inf Resour Manage J, 2006, 19: 70-83 CrossRef Google Scholar

[19] Hoekstra R. The knowledge reengineering bottleneck. Semant Web, 2010, 1: 111-115. Google Scholar

[20] Gruber T R. A translation approach to portable ontology specifications. Knowl Acquis, 1993, 5: 199-220 CrossRef Google Scholar

[21] Armbrust M, Fox A, Griffith R, et al. A view of cloud computing. Commun ACM, 2010, 53: 50-58. Google Scholar

[22] Atzori L, Iera A, Morabito G. The internet of things: a survey. Comput Netw, 2010, 54: 2787-2805 CrossRef Google Scholar

[23] Wofford J. User-generated content. New Media Soc, 2012, 14: 1236-1239 CrossRef Google Scholar

[24] Inkpen A. Learning, knowledge acquisition, and strategic alliances. Eur Manage J, 1998, 16: 223-229 CrossRef Google Scholar

[25] Yamins D L K, Dicarlo J J. Using goal-driven deep learning models to understand sensory cortex. Nat Neurosci, 2016, 19: 356-365 CrossRef Google Scholar

[26] Lévy P. Collective Intelligence. New York: Plenum/Harper Collins, 1997. Google Scholar

[27] Bonabeau E. Decisions 2.0: the power of collective intelligence. MIT Sloan Manage Rev, 2009, 50: 45. Google Scholar

[28] Gray P H, Meister D B. Introduction: fragmentation and integration in knowledge management research. Inf Tech People, 2003, 16: 259-265 CrossRef Google Scholar

[29] Cox S A, Perkins J S. The role of E-collaboration systems in knowledge management. In: E-Collaboration: Concepts, Methodologies, Tools, and Applications. Hershey: IGI Global Publishes, 2009. 600-607. Google Scholar

[30] Li L, Wen D, Zheng N N, et al. Cognitive cars: a new frontier for ADAS research. IEEE T Intell Transp, 2012, 13: 395-407 CrossRef Google Scholar

[31] Zheng N N, Tang S, Cheng H, et al. Toward intelligent driver-assistance and safety warning system. IEEE Intell Syst, 2004, 19: 8-11. Google Scholar

[32] Knoblock C A, Minton S, Ambite J L, et al. Mixed-initiative, multi-source information assistants. In: Proceedings of the 10th International Conference on World Wide Web. Hong Kong: ACM, 2001. 697-707. Google Scholar

[33] Dasarathy B V. Multi-sensor, multi-source information fusion: architecture, algorithms, and applications--a panoramic overview. In: Proceedings of the 2nd IEEE International Conference on Computational Cybernetics, 2004, doi: 10.1109/ICCCYB.2004.1437643. Google Scholar

[34] Atrey P K, Hossain M A, El Saddik A, et al. Multimodal fusion for multimedia analysis: a survey. Multimedia Syst, 2010, 16: 345-379 CrossRef Google Scholar

[35] Chong C Y, Kumar S P. Sensor networks: evolution, opportunities, and challenges. Proc IEEE, 2003, 91: 1247-1256 CrossRef Google Scholar

[36] Hall D L, Llinas J. An introduction to multisensor data fusion. Proc IEEE, 1997, 85: 6-23 CrossRef Google Scholar

[37] Greenberg J, Pyszczynski T. The effect of an overheard ethnic slur on evaluations of the target: how to spread a social disease. J Exp Soc Psychol, 1985, 21: 61-72 CrossRef Google Scholar

[38] Wu X, Liu Z. How community structure influences epidemic spread in social networks. Phys A Stat Mech Its Appl, 2008, 387: 623-630 CrossRef Google Scholar

[39] Song S, Li Q, Bao H. Detecting dynamic association among twitter topic. In: Proceedings of the 21st International Conference Companion on World Wide Web. Lyon: ACM, 2012. 605-606. Google Scholar

[40] Zhu J, Lu J, Yu X. Flocking of multi-agent non-holonomic systems with proximity graphs. IEEE Trans Circuit Syst I-Regular Paper, 2013, 60: 199-210 CrossRef Google Scholar

[41] Axley S R. Managerial and organizational communication in terms of the conduit metaphor. Acad Manage Rev, 1984, 9: 428-437. Google Scholar

[42] Ginsberg J, Mohebbi M H, Patel R S, et al. Detecting influenza epidemics using search engine query data. Nature, 2008, 457: 1012-1014. Google Scholar

[43] Lazer D, Kennedy R, King G, et al. The parable of google flu: traps in big data analysis. Science, 2014, 343: 1203-1205 CrossRef Google Scholar

[44] Wu X, Zhu X, Wu G Q, et al. Data mining with big data. IEEE Trans Knowl Data Eng, 2014, 26: 97-107 CrossRef Google Scholar

[45] Cheeseman P, Stutz J. Bayesian classification (AutoClass): theory and results. In: Advances in Knowledge Discovery and Data Mining. Menlo Park: AAAI Press, 1996. Google Scholar

[46] Jeffrey R C. The Logic of Decision. New York: Mcgraw-Hill Book Co. Inc., 1965. Google Scholar

[47] Dreiseitl S, Ohno-Machado L. Logistic regression and artificial neural network classification models: a methodology review. J Biomed Inf, 2002, 35: 352-359 CrossRef Google Scholar

[48] LeCun Y, Bengio Y, Hinton G. Deep learning. Nature, 2015, 521: 436-444 CrossRef Google Scholar

[49] Rowley H A, Baluja S, Kanade T. Neural network-based face detection. IEEE Trans Pattern Anal Mach Intell, 1998, 20: 23-38 CrossRef Google Scholar

[50] Hecht-Nielsen R. Theory of the backpropagation neural network. In: Proceedings of International 1989 Joint Conference on Neural Networks. Piscataway: IEEE, 1989. 593-605. Google Scholar

[51] Kawato M, Furukawa K, Suzuki R. A hierarchical neural-network model for control and learning of voluntary movement. Biol Cybern, 1987, 57: 169-185 CrossRef Google Scholar

[52] Piater J H, Grupen R A. Feature learning for recognition with Bayesian networks. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Los Alamitos: IEEE Computer Society, 2000. 17-20. Google Scholar

[53] Lee H, Pham P, Largman Y, et al. Unsupervised feature learning for audio classification using convolutional deep belief networks. In: Proceedings of the 23rd Annual Conference on Neural Information Processing Systems, Vancouver, 2009. 1096-1104. Google Scholar

[54] Wang Y X. The theoretical framework and cognitive process of learning. In: Proceedings of the 6th IEEE International Conference on Cognitive Informatics, California, 2007. 470-479. Google Scholar

[55] Luger G F. Artificial Intelligence: Structures and Strategies for Complex Problem Solving. New York: Pearson Education, 2005. Google Scholar

[56] Brachman R J. What's in a concept: structural foundations for semantic networks. Int J Man-Mach Stud, 1977, 9: 127-152 CrossRef Google Scholar

[57] Lu C Y. Knowledge map: an approach to knowledge acquisition in developing engineering expert systems. Eng Comput, 1987, 3: 59-68 CrossRef Google Scholar

[58] Shiffrin R M, Börner K. Mapping knowledge domains. Proc Natl Acad Sci, 2004, 101: 5183-5185 CrossRef Google Scholar

[59] Chen Y, Liu Z Y. The rise of mapping knowledge domain. Stud Sci Sci, 2005, 23: 149-154. Google Scholar

[60] Zhou D, Bousquet O, Lal T N, et al. Learning with local and global consistency. Adv Neural Inf Proc Syst, 2004, 16: 321-328. Google Scholar

[61] Ahn H S, Chen Y Q, Moore K L. Iterative learning control: brief survey and categorization. IEEE Trans Syst Man Cy C, 2007, 37: 1099-1149. Google Scholar

[62] Nguyen-Tuong D, Peters J. Model learning for robot control: a survey. Cogn Process, 2011, 12: 319-340 CrossRef Google Scholar

[63] Fayyad U. From data mining to knowledge discovery in databases. AI Mag, 1996, 17: 37-54. Google Scholar

[64] Ishizaka H, Arikawa S. Model inference. Ipsj Magazine, 1991, 32: 236-245. Google Scholar

[65] Yang C, Wang G, Li Y, et al. Study on knowledge reasoning based on extended formulas. IFIP Intl Federat Inf Proc, 2005, 187: 797-805 CrossRef Google Scholar

[66] Kirsh D. A few thoughts on cognitive overload. Intellectica, 2000, 30: 41-42. Google Scholar

[67] Tene O, Polonetsky J. Big data for all: privacy and user control in the age of analytics. Nw J Tech Intell Prop, 2012, 11: 1-27. Google Scholar

[68] Townsend A M. Smart Cities: Big Data, Civic Hackers, and the Quest for A New Utopia. New York: WW Norton & Company, 2013. Google Scholar

[69] Langley P, Zytkow J M. Data-driven approaches to empirical discovery. Artif Intell, 1989, 40: 283-312 CrossRef Google Scholar

[70] Chen W, Zhang S. Experience formula discovery system FDD. Mini-Micro Syst, 1999, 6: 410-413. Google Scholar

[71] Dempster A P, Laird N M, Rubin D B. Maximum likelihood from incomplete data via the EM algorithm. J R Stat Soc B (Methodological), 1977, 39: 1-38. Google Scholar

[72] Bach F R, Lanckriet G R G, Jordan M I. Multiple kernel learning, conic duality, and the SMO algorithm. In: Proceedings of the 21st International Conference on Machine Learning. New York: ACM, 2004. Google Scholar

[73] Wang J, Zheng N. Multivariate linear correlation analysis. ArXiv:1401.4827, 2014. Google Scholar

[74] LeCun Y, Boser B, Denker J S, et al. Backpropagation applied to handwritten zip code recognition. Neural Comput, 1989, 1: 541-551 CrossRef Google Scholar

[75] Deng J, Dong W, Socher R, et al. Imagenet: a large-scale hierarchical image database. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Miami Beach: IEEE, 2009. 248-255. Google Scholar

[76] Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks. Adv Neural Inf Proc Syst, 2012, 25: 2012. Google Scholar

[77] He K, Zhang X, Ren S, et al. Delving deep into rectifiers: surpassing human-level performance on imagenet classification. In: Proceedings of 2015 IEEE International Conference on Computer Vision (ICCV). Chile: IEEE Computer Society, 2015. 1026-1034. Google Scholar

[78] Ren S, He K, Girshick R, et al. Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems. Quebec: Neural Information Processing Systems Foundation Inc., 2015. 91-99. Google Scholar

[79] Sun Y, Wang X, Tang X. Deep learning face representation from predicting 10000 classes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Guangzhou: Computer Vision Foundation, 2014. 1891-1898. Google Scholar

[80] Wang L, Qiao Y, Tang X. Action recognition with trajectory-pooled deep-convolutional descriptors. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, 2015. 4305-4314. Google Scholar

[81] Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. ArXiv:1409.1556, 2014. Google Scholar

[82] Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, 2015: 3431-3440. Google Scholar

[83] Vapnik V, Izmailov R. Learning using privileged information: similarity control and knowledge transfer. J Mach Learn Res, 2015, 16: 2023-2049. Google Scholar

[84] Chris E, Stewart T C, Xuan C, et al. A large-scale model of the functioning brain. Science, 2012, 338: 1202-1205 CrossRef Google Scholar

[85] Lake B M, Salakhutdinov R B, Tnenbaum J. Human-level concept learning through probabilistic program induction. Science, 2015, 350: 1332-1338 CrossRef Google Scholar

[86] Kohonen T. Self-Organizing Maps. New York: Springer, 1995. Google Scholar

[87] Kohonen T, Somervuo P. Self-organizing maps of symbol strings. Neurocomputing, 1998, 21: 19-30 CrossRef Google Scholar

[88] Patel V L, Shortliffe E H, Stefanelli M, et al. The coming of age of artificial intelligence in medicine. Artif Intell Med, 2009, 46: 5-17 CrossRef Google Scholar

[89] Gerola H. IBM-Watson. Bull Amer Astron Soc, 1980, 12: 131. Google Scholar

[90] Shader R I. Some reflections on IBM watson and on women's health. Clin Ther, 2016, 38: 1-2 CrossRef Google Scholar

[91] Tian Y, Zhang S Y, Luo B, et al. Research progress in impact of heat wave on human health. Adv Meteorol Sci Tech, 2013, 2: 49-54 [田颖, 张书余, 罗斌, 等. 热浪对人体健康影响的研究进展. 气象科技进展, 2013, 2: 49-54]. Google Scholar

[92] Garabedian V D, Pawlotsky J M, Authier F J, et al. Myasthenia gravis and hepatitis C virus infection. J Viral Hepatitis, 1996, 3: 329-332 CrossRef Google Scholar

[93] Silver D, Huang A, Maddison C J, et al. Mastering the game of Go with deep neural networks and tree search. Nature, 2016, 529: 484-489 CrossRef Google Scholar

[94] Ghahramani Z. Probabilistic machine learning and artificial intelligence. Nature, 2015, 521: 452-459 CrossRef Google Scholar

[95] Ferrenberg A M, Swendsen R H. Optimized monte carlo data analysis. Phys Rev Lett, 1989, 63: 1195-1198 CrossRef Google Scholar

[96] Baheti R, Gill H. Cyber-physical systems. Impact Control Tech, 2011, 13: 1-6. Google Scholar

[97] Liu Z, Yang D S, Wen D, et al. Cyber-physical-social systems for command and control. IEEE Intell Syst, 2011, 26: 92-96 CrossRef Google Scholar

[98] Murase H. Artificial intelligence in agriculture. Comput Electron Agric, 2000, 29: 1-2 CrossRef Google Scholar

[99] Barrett J R, Jones D D. Knowledge engineering in agriculture. Knowl Eng Agric, 1989, 8: 214. Google Scholar

[100] Aitkenhead M J, Dalgetty I A, Mullins C E, et al. Weed and crop discrimination using image analysis and artificial intelligence methods. Comput Electron Agric, 2003, 39: 157-171 CrossRef Google Scholar

[101] Rykiel E J. Artificial intelligence and expert systems in ecology and natural resource management. Ecol Model, 1989, 46: 3-8 CrossRef Google Scholar

2

Citations
0

Altmetric
Article metrics

Email
Export

Fragmentation knowledge processing and networked artificial intelligence}{Fragmentation knowledge processing and networked artificial intelligence

Abstract

Funded by

References

2

0

Interesting search

Top 5Related Articles