SCIENTIA SINICA Informationis, Volume 46, Issue 8: 1016-1034(2016) https://doi.org/10.1360/N112016-00065

Knowledge automation and its industrial application

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  • ReceivedMar 26, 2016
  • AcceptedJun 1, 2016


The term knowledge work refers to the use and creation of knowledge from mastery of knowledge. In large part, machines have replaced human physical labor in modern industry, where the core of management, scheduling, and operation is knowledge work. Knowledge work automation is a disruptive technology that is transforming the future economy with extensive application value. Knowledge automation is automation of knowledge work. This paper reviews the research conducted on topics related to knowledge automation such as knowledge acquisition, knowledge representation, knowledge association, and knowledge inference, as well as the automation application technology based on knowledge. A knowledge automation case study involving the making of raw material purchasing decisions by a zinc producer is also presented to demonstrate the operation of knowledge automation. Considering the challenges confronting knowledge automation in the management, scheduling, and operation levels of industrial processes, the features of knowledge in industrial processes and several knowledge automation problems are discussed. Further, several strategies and suggestions for knowledge automation research are also proposed.

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[1] Manyika J, Chui M, Bughin J, et al. Disruptive Technologies: Advances That Will Transform Life, Business, and the Global Economy. New York: McKinsey Global Institute, 2013. Google Scholar

[2] Wang F Y. The destiny: towards knowledge au-tomation--preface of the special issue for the 50th anniver-sary of Acta Automatica Sinica. Acta Automatica Sinica, 2013, 39: 1741-1743 [王飞跃. 天命唯新:迈向知识自动化---《自动化学报》创刊50周年专刊序. 自动化学报, 2013, 39: 1741-1743]. Google Scholar

[3] Wang F Y. On future development of robotics: from industrial automation to knowledge automation. Sci Tech Rev, 2015, 33: 39-44 [王飞跃. 机器人的未来发展: 从工业自动化到知识自动化. 科技导报, 2015, 33: 39-45]. Google Scholar

[4] 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

[5] Manda P, Ozkan S, Wang H, et al. Cross-ontology multi-level association rule mining in the gene ontology. PloS one, 2012, 7: e47411-489 CrossRef Google Scholar

[6] Nayak A K, Sharma K C, Bhakar R, et al. ARIMA based statistical approach to predict wind power ramps. In: Proceedings of IEEE Power {&} Energy Society General Meeting, Denver, 2015. 1-5. Google Scholar

[7] Martinsen K, Downey J, Baturynska I. Human-machine interface for artificial neural network based machine tool process monitoring. Procedia CIRP, 2016, 41: 933-938 CrossRef Google Scholar

[8] Wu T C, Hsu M F. Credit risk assessment and decision making by a fusion approach. Knowledge-Based Syst, 2012, 35: 102-110 CrossRef Google Scholar

[9] Bi W, Dandy G C, Maier H R. Improved genetic algorithm optimization of water distribution system design by incorporating domain knowledge. Environ Model Softw, 2015, 69: 370-381 CrossRef Google Scholar

[10] Reyes E R, Negny S, Robles G C, et al. Improvement of online adaptation knowledge acquisition and reuse in case-based reasoning: application to process engineering design. Eng Appl Artif Intell, 2015, 41: 1-16 CrossRef Google Scholar

[11] Barros R C, Jaskowiak P A, Cerri R, et al. A framework for bottom-up induction of oblique decision trees. Neurocomputing, 2014, 135: 3-12 CrossRef Google Scholar

[12] Han M, Cao Z J. An improved case-based reasoning method and its application in endpoint prediction of basic oxygen furnace. Neurocomputing, 2015, 149: 1245-1252 CrossRef Google Scholar

[13] Kamsu-Foguem B, Rigal F, Mauget F. Mining association rules for the quality improvement of the production process. Expert Syst Appl, 2013, 40: 1034-1045 CrossRef Google Scholar

[14] Ghanbari A, Kazemi S M R, Mehmanpazir F, et al. A cooperative ant colony optimization-genetic algorithm approach for construction of energy demand forecasting knowledge-based expert systems. Knowledge-Based Syst, 2013, 39: 194-206 CrossRef Google Scholar

[15] Bai C, Dhavale D, Sarkis J. Complex investment decisions using rough set and fuzzy c-means: an example of investment in green supply chains. Euro J Oper Res, 2016, 248: 507-521 CrossRef Google Scholar

[16] Wang H J, Lin N, Jing S K. Supporting knowledge uncertainty engineering-knowledge acquisition. Comput Integr Manuf Syst, 2015, 21: 2558-2563 [王宏君, 蔺娜, 敬石开. 支持知识不确定性的工程知识获取. 计算机集成制造系统, 2015, 21: 2558-2563]. Google Scholar

[17] Singhal A. Introducing the Knowledge Graph: Things, Not Strings. Official Blog (of Google). https://googleblog. blogspot.com/2012/05/introducing-knowledge-graph-things-not.html. 2012. Google Scholar

[18] Wu W, Li H, Wang H, et al. Probase: a probabilistic taxonomy for text understanding. In: Proceedings of ACM SIGMOD International Conference on Management of Data. New York: ACM, 2012. 481-492. Google Scholar

[19] Zhong X, Fu H, Xia H, et al. A hybrid cognitive assessment based on ontology knowledge map and skills. Knowledge-Based Syst, 2015, 73: 52-60 CrossRef Google Scholar

[20] Hinton G E, Salakhutdinov R R. Reducing the dimensionality of data with neural networks. Science, 2006, 313: 504-507 CrossRef Google Scholar

[21] Schmidhuber J. Deep learning in neural networks: an overview. Neural Netw, 2015, 61: 85-117 CrossRef Google Scholar

[22] Hinton G, Vinyals O, Dean J. Distilling the knowledge in a neural network. arXiv:1503.02531. Google Scholar

[23] Mnih V, Kavukcuoglu K, Silver D, et al. Playing atari with deep reinforcement learning. arXiv:1312.5602. Google Scholar

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

[25] Nonaka I. A dynamic theory of organizational knowledge creation. Organ Sci: J Inst Manage Sci, 1994, 5: 14-37. Google Scholar

[26] Nonaka I. Toward middle-up-down management: accelerating information creation. Sloan Manage Rev, 1988, 29: 9-18. Google Scholar

[27] do Rosário C R, Kipper L M, Frozza R, et al. Modeling of tacit knowledge in industry: simulations on the variables of industrial processes. Expert Syst Appl, 2015, 42: 1613-1625 CrossRef Google Scholar

[28] Simon H A. Complexity and the representation of patterned sequences of symbols. Psychol Rev, 1972, 79: 369-1625 CrossRef Google Scholar

[29] Yin J S, Liu L, Chen K, et al. Intelligent decision making model for traffic congestion control based on fuzzy petri net. J Comput Methods Sci Eng, 2015, 15: 91-98. Google Scholar

[30] Jin L, Liu J, Xu Y, et al. A novel rule base representation and its inference method using the evidential reasoning approach. Knowledge-Based Syst, 2015, 87: 80-91 CrossRef Google Scholar

[31] Minsky M. A framework for representing knowledge. Readings Cogn Sci, 1974, 76: 156-189. Google Scholar

[32] Terletskyi D. Inheritance in object-oriented knowledge representation. In: Information and Software Technologies. Berlin: Springer International Publishing, 2015. 293-305. Google Scholar

[33] An G. Introduction of a framework for dynamic knowledge representation of the control structure of transplant immunology: employing the power of abstraction with a solid organ transplant agent-based model. Front Immunol, 2015, 6: 561. Google Scholar

[34] Kendall E F, Dutra M E. Method and apparatus for frame-based knowledge representation in the unified modeling language (UML). US Patent 7, 424, 701, 2008-9-9. Google Scholar

[35] Mohan L, Kashyap R L. An object-oriented knowledge representation for spatial information. IEEE Trans Softw Eng, 1988, 14: 675-681 CrossRef Google Scholar

[36] Zhang Y, Luo X, Buis J J, et al. LCA-oriented semantic representation for the product life cycle. J Clean Prod, 2015, 86: 146-162 CrossRef Google Scholar

[37] Thanh N T. Reasoning With UML Statechart Diagrams Using XML Declarative Description Theory. Bangkok: Asian Institute of Technology, 2015. Google Scholar

[38] Petri C A. Introduction to general net theory. In: Net theory and applications. Berlin: Springer, 1980. 1-19. Google Scholar

[39] Li X, Lara-Rosano F. Adaptive fuzzy Petri nets for dynamic knowledge representation and inference. Expert Syst Appl, 2000, 19: 235-241 CrossRef Google Scholar

[40] Mohite P V, Dharaskar R V, Thakare V M. Adaptive fuzzy higher order petri nets for knowledge representation a reasoning. In: Proceedings of National Conference on Advanced Technologies in Computing and Networking, Special Issue of International Journal of Electronics, Communication & Soft Computing Science and Engineering (IJECSCSE), Amravati, 2015. 99-102. Google Scholar

[41] Liu H C, Lin Q L, Mao L X, et al. Dynamic adaptive fuzzy Petri nets for knowledge representation and reasoning. IEEE Trans Syst Man Cyb Syst, 2013, 43: 1399-1410 CrossRef Google Scholar

[42] Zhang F P, Zhang T H, Yan Y. Intelligent fixture structure design based on knowledge ontology. In: Proceedings of IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), Singapore, 2015 1820-1824. Google Scholar

[43] Chen Z H, Xie G, Yan G W. Granular matrix-based knowledge representation for tolerance relation. Comput Sci, 2012, 39: 211-213 [陈泽华, 谢刚, 阎高伟. 基于粒矩阵的相容关系的知识表示. 计算机科学, 2012, 39: 211-213]. Google Scholar

[44] Omri M N, Tijus C A. Uncertain and approximative knowledge representation in fuzzy semantic networks. In: Proceedings of the 12th International Conference on Industrial {&} Engineering Applications of Artificial Intelligence {&} Expert Systems IEA/AIE-99, Cairo, 1999. 233-238. Google Scholar

[45] Shadbolt N, Berners-Lee T, Hall W. The semantic web revisited. Intell Syst, 2006, 21: 96-101. Google Scholar

[46] Wang Q, Rong L, Yu K. A pushouts based knowledge merging method: knowledge reorganization in emergency decision-making support. In: Proceedings of the 4th International Conference on Wireless Communications, Networking and Mobile Computing, Dalian, 2008. 1-4. Google Scholar

[47] Feng D. Research and implementation of knowledge extraction and knowledge restructuring based on content. Inst Sci Tech Inf China, 2014 [冯丹. 基于内容的知识抽取与知识重组研究与实现. 中国科学技术信息研究所, 2014]. Google Scholar

[48] Maleszka M, Mianowska B, Nguyen N T. A method for collaborative recommendation using knowledge integration tools and hierarchical structure of user profiles. Knowledge-Based Syst, 2013, 47: 1-13 CrossRef Google Scholar

[49] Biswas G, Lee G. Knowledge reorganization. A rule model scheme for efficient reasoning. In: Proceedings of the 10th Conference on Artificial Intelligence for Applications, San Antonia, 1994. 312-318. Google Scholar

[50] Ruiz P P, Foguem B K, Grabot B. Generating knowledge in maintenance from experience feedback. Knowledge-Based Syst, 2014, 68: 4-20 CrossRef Google Scholar

[51] Namioka Y, Yamashita T, Hoshino T, et al. Knowledge-association networks for system design. In: Proceedings of Workshop on Knowledge and Data Engineering Exchange, Chicago, 1999. 131-138. Google Scholar

[52] Hu J, Zhang L, Cai Z, et al. Fault propagation behavior study and root cause reasoning with dynamic Bayesian network based framework. Process Safe Environ Prot, 2015, 97: 25-36 CrossRef Google Scholar

[53] Diallo T M L, Henry S, Ouzrout Y. Bayesian network building for diagnosis in industrial domain based on expert knowledge and unitary traceability data. Ifac Papersonline, 2015, 48: 2411-2416 CrossRef Google Scholar

[54] Ebrahimipour V, Yacout S. Ontology-based schema to support maintenance knowledge representation with a case study of a pneumatic valve. IEEE Trans Syst Man Cybernetics Syst, 2015, 45: 702-712 CrossRef Google Scholar

[55] Samwald M, Giménez J A M, Boyce R D, et al. Pharmacogenomic knowledge representation, reasoning and genome-based clinical decision support based on OWL 2 DL ontologies. BMC Medical Inf Decis Making, 2015, 15: 12-712 CrossRef Google Scholar

[56] Roda F, Musulin E. An ontology-based framework to support intelligent data analysis of sensor measurements. Expert Syst Appl, 2014, 41: 7914-7926 CrossRef Google Scholar

[57] Chattopadhyay S, Banerjee S, Rabhi F A, et al. A case-based reasoning system for complex medical diagnosis. Expert Syst, 2013, 30: 12-20 CrossRef Google Scholar

[58] Han M, Cao Z J. An improved case-based reasoning method and its application in endpoint prediction of basic oxygen furnace. Neurocomputing, 2015, 149: 1245-1252 CrossRef Google Scholar

[59] Yan A, Wang W, Zhang C, et al. A fault prediction method that uses improved case-based reasoning to continuously predict the status of a shaft furnace. Inf Sci, 2014, 259: 269-281 CrossRef Google Scholar

[60] Gui W H, Liu J H, Xie Y F. Operational pattern hierarchical matching and evolution strategy for copper flash smelting process. Syst Eng-Theory Pract, 2013, 33: 2714-2720 [桂卫华, 刘建华, 谢永芳. 铜闪速熔炼过程操作模式分级匹配技术与演化策略. 系统工程理论与实践, 2013, 33: 2714-2720]. Google Scholar

[61] Peng H, Wang J, PéRez-JiméNez M J, et al. Fuzzy reasoning spiking neural P system for fault diagnosis. Inf Sci, 2013, 235: 106-116 CrossRef Google Scholar

[62] Jian D, Yan C, Mpofu E, et al. A hybrid support vector machine and fuzzy reasoning based fault diagnosis and rescue system for stable glutamate fermentation. Chem Eng Res Design, 2012, 90: 1197-1207 CrossRef Google Scholar

[63] Maji P, Garai P. IT2 fuzzy-rough sets and max relevance-max significance criterion for attribute selection. IEEE Trans Cybernetics, 2015, 45: 1657-1668 CrossRef Google Scholar

[64] Chen D, Yang Y. Attribute reduction for heterogeneous data based on the combination of classical and fuzzy rough set models. IEEE Trans Fuzzy Syst, 2014, 22: 1325-1334 CrossRef Google Scholar

[65] Salamó M, López-Sánchez M. Rough set based approaches to feature selection for case-based reasoning classifiers. Pattern Recogn Lett, 2011, 32: 280-292 CrossRef Google Scholar

[66] Milinković S, Marković M, Vesković S, et al. A fuzzy Petri net model to estimate train delays. Simulat Model Pract Theory, 2013, 33: 144-157 CrossRef Google Scholar

[67] Amaral R R. A new SPC tool in the steelshop at ArcelorMittal Gent designed to increase productivity. Dissertation for Ph.D. Degree. Brussels: Ghent University, 2012. Google Scholar

[68] 钱锋. 以流程工业智能化助推``智造强国". 联合时报. Google Scholar

[69] Gui W H, Wang C H, Xie Y F, et al. The necessary way to realize great-leap-forward development of process industries. Sci Found China, 2015, 29: 337-342 [桂卫华, 王成红, 谢永芳, 等. 流程工业实现跨越式发展的必由之路. 中国科学基金, 2015, 29: 337-342]. Google Scholar

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