段玉聪
DIKWP数字经济学12链之机器学习链:数据学习-信息学习-知识学习-智慧学习-意图学习
2022-12-6 16:06
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DIKWP Machine Learning Chain::= Data Learning -> Information Learning -> Knowledge Learning->Wisdom Learning->Purpose Learning


DIKWP数字经济12链之机器学习:数据学习-信息学习-知识学习-智慧学习-意图学习

Chains of DIKWP Digital Economics: 

DIKWP Learning Chain::= Data Learning -> Information Learning -> Knowledge Learning->Wisdom Learning->Purpose Learning 

Yucong Duan(段玉聪)

DIKWP research group, 海南大学(Hainan University)

Email: duanyucong@hotmail.com

DIKWP数字经济12链之机器学习:数据学习-信息学习-知识学习-智慧学习-意图学习

从传统供应链-产业链-价值链的承载基础实体对象转化为更有效与密切的融合客观观察或交流对象与主观认知内容的数字实体对象后的主观认知和客观内容的融合,对当前机器学习由数据学习知识学习的转化提出了更广泛的DIKW类型内及DIKW类型间转化需求。这些转化将支撑DIKWP资产数字化后的数字资:数据资产-信息资产-知识资产-智慧资产-意图资产的资产间数字转换、语义计算与推理需求,帮助落实DIKWP 数字经济链::=数据经济->信息经济->知识经济->智慧经济->意图经济的整体性调整。通过将当前人工智能机器学习的数据标注和数据训练扩展到数据标注[1],信息标注[2],知识标注[3],智慧标注[4],意图标注[5]DIKWP训练的横切DIKWP标注 数据训练[6],信息训练[7],知识训练[8],智慧训练[9],意图训练[10],DIKWP机器学习启动整合了数据学习[11],信息学习[12],知识学习[ 13],智慧学习[14],意图学习[15]形成DIKWP机器学习链的主客观融合的语义学习[16]

 

DIKWP Learning Chain

::= Data Learning -> Information Learning -> Knowledge Learning->Wisdom Learning->Purpose Learning

 

DIKWP 机器学习

::=数据学习->信息学习->知识学习->智慧学习->意图学习

 

参考文献:

[1] Wang, Meng, et al. "Assistive tagging: A survey of multimedia tagging with human-computer joint exploration." ACM Computing Surveys (CSUR) 44.4 (2012): 1-24.

[2] Blaze, Matt, et al. "Managing trust in an information‐labeling system." European Transactions on Telecommunications 8.5 (1997): 491-501.

[3] Heymann, Paul, Andreas Paepcke, and Hector Garcia-Molina. "Tagging human knowledge." Proceedings of the third ACM international conference on Web search and data mining. 2010.

[4] Agarwal, Nitin, et al. "WisColl: Collective wisdom based blog clustering." Information Sciences 180.1 (2010): 39-61.

[5] Strohmaier, Markus. "Purpose tagging: capturing user intent to assist goal-oriented social search." Proceedings of the 2008 ACM workshop on Search in social media. 2008.

[6] Batista, Gustavo EAPA, Ronaldo C. Prati, and Maria Carolina Monard. "A study of the behavior of several methods for balancing machine learning training data." ACM SIGKDD explorations newsletter 6.1 (2004): 20-29.

[7] Rodriguez, M., J. Hidalgo, and B. Agudo. "Using WordNet to complement training information in text categorization." Proceedings of 2nd International Conference on Recent Advances in Natural Language Processing II: Selected Papers from RANLP. Vol. 97. 2000.

[8] Noordewier, Michiel, Geoffrey Towell, and Jude Shavlik. "Training knowledge-based neural networks to recognize genes in DNA sequences." Advances in neural information processing systems 3 (1990).

[9] Grossmann, Igor, et al. "The science of wisdom in a polarized world: Knowns and unknowns." Psychological Inquiry 31.2 (2020): 103-133.

[10] Lawson, M. J., and Sandra Fueloep. "Understanding the purpose of strategy training." British Journal of Educational Psychology 50.2 (1980): 175-180.\

[11] Cherkassky, Vladimir, and Filip M. Mulier. Learning from data: concepts, theory, and methods. John Wiley & Sons, 2007.

[12] Chen, Xi, et al. "Infogan: Interpretable representation learning by information maximizing generative adversarial nets." Advances in neural information processing systems 29 (2016).

[13] Goldie, John Gerard Scott. "Connectivism: A knowledge learning theory for the digital age?." Medical teacher 38.10 (2016): 1064-1069.

[14] Kuepers, Wendelin M., and David Pauleen. "Learning wisdom: Embodied and artful approaches to management education." Scandinavian Journal of Management 31.4 (2015): 493-500.

[15] Milner IV, H. Richard. "Culturally relevant, purpose-driven learning & teaching in a middle school social studies classroom." Multicultural Education 21.2 (2014): 9-17.

[16] Lavrenko, Victor, Raghavan Manmatha, and Jiwoon Jeon. "A model for learning the semantics of pictures." Advances in neural information processing systems 16 (2003).

[17] Yucong Duan. 面向数据,信息,知识过载的跨模态,跨尺度,介尺度主观认知语义建模与分析https://blog.Culturenet.cn/blog-3429562-1228985.html

[18] Yucong Duan, DIKWP based Digital Governance: Current Trends from "Data Governance","Information Governance", "Knowledge Governance" to "Wisdom Governance", October 2022, DOI: 10.13140/RG.2.2.15074.40645

[19] Yucong Duan, A Holism Trusted Semantic Computing based on DIKWP Integrating Trusted Data *Trusted Information*Trusted Knowledge*Trusted Wisdom* Trusted Purpose, October 2022, DOI: 10.13140/RG.2.2.22073.65128

[9] Yucong Duan, DIKWP Ultimate of Digital Economy: From Asymmetric Data Economy, Asymmetric Information Economy to Symmetric Knowledge Economy and Symmetric Wisdom Economy, November 2022DOI: 10.13140/RG.2.2.32178.48328

英文原文章阅读链接:

https://www.researchgate.net/publication/365635800_DIKWP_Ultimate_of_Digital_Economy_From_Asymmetric_Data_Economy_Asymmetric_Information_Economy_to_Symmetric_Knowledge_Economy_and_Symmetric_Wisdom_Economy

中文全文章阅读链接:

https://blog.sciencenet.cn/home.php?mod=space&uid=3429562&do=blog&id=1365170

 

DIKWP Digital Economy 12 Chains of Machine Learning: Data Learning-Information Learning-Knowledge Learning-Smart Learning-Intent Learning

Chains of DIKWP Digital Economics: 

DIKWP Learning Chain::= Data Learning -> Information Learning -> Knowledge Learning->Wisdom Learning->Purpose Learning 

Yucong Duan

DIKWP research group, Hainan University

Email: duanyucong@hotmail.com

DIKWP Digital Economy 12 Chains of Machine Learning: Data Learning-Information Learning-Knowledge Learning-Smart Learning-Intent Learning

The fusion of subjective cognition and objective content after transforming from the traditional supply chain-industrial chain-value chain carrying basic entity objects to digital entity objects that more effectively and closely integrate objective observation or communication objects and subjective cognition content, has a great impact on the current situation. The transformation of machine learning from data learning to knowledge learning puts forward a wider demand for transformation within DIKW types and between DIKW types. These transformations will support the digital asset chain after the digitization of DIKWP assets: data assets-information assets-knowledge assets-smart assets-intent assets, digital conversion between assets, semantic calculation and reasoning requirements, and help implement the DIKWP digital economic chain::=data economy ->Information economy->knowledge economy->smart economy->overall adjustment of intention economy. By extending the current data labeling and data training of artificial intelligence machine learning to data labeling [1], information labeling [2], knowledge labeling [3], smart labeling [4], intent labeling [5] and crosscutting of DIKWP training DIKWP labeled data training [6], information training [7], knowledge training [8], wisdom training [9], intention training [10], DIKWP machine learning startup integrated data learning [11], information learning [12], Knowledge learning [13], wisdom learning [14], intention learning [15] form the semantic learning of subjective and objective fusion of DIKWP machine learning chain [16]:

 

DIKWP Learning Chain

::= Data Learning -> Information Learning -> Knowledge Learning->Wisdom Learning->Purpose Learning

 

DIKWP Machine Learning Chain

::= Data Learning -> Information Learning -> Knowledge Learning -> Wisdom Learning -> Intent Learning

 

References

[1] Wang, Meng, et al. "Assistive tagging: A survey of multimedia tagging with human-computer joint exploration." ACM Computing Surveys (CSUR) 44.4 (2012): 1-24.

[2] Blaze, Matt, et al. "Managing trust in an information‐labeling system." European Transactions on Telecommunications 8.5 (1997): 491-501.

[3] Heymann, Paul, Andreas Paepcke, and Hector Garcia-Molina. "Tagging human knowledge." Proceedings of the third ACM international conference on Web search and data mining. 2010.

[4] Agarwal, Nitin, et al. "WisColl: Collective wisdom based blog clustering." Information Sciences 180.1 (2010): 39-61.

[5] Strohmaier, Markus. "Purpose tagging: capturing user intent to assist goal-oriented social search." Proceedings of the 2008 ACM workshop on Search in social media. 2008.

[6] Batista, Gustavo EAPA, Ronaldo C. Prati, and Maria Carolina Monard. "A study of the behavior of several methods for balancing machine learning training data." ACM SIGKDD explorations newsletter 6.1 (2004): 20-29.

[7] Rodriguez, M., J. Hidalgo, and B. Agudo. "Using WordNet to complement training information in text categorization." Proceedings of 2nd International Conference on Recent Advances in Natural Language Processing II: Selected Papers from RANLP. Vol. 97. 2000.

[8] Noordewier, Michiel, Geoffrey Towell, and Jude Shavlik. "Training knowledge-based neural networks to recognize genes in DNA sequences." Advances in neural information processing systems 3 (1990).

[9] Grossmann, Igor, et al. "The science of wisdom in a polarized world: Knowns and unknowns." Psychological Inquiry 31.2 (2020): 103-133.

[10] Lawson, M. J., and Sandra Fueloep. "Understanding the purpose of strategy training." British Journal of Educational Psychology 50.2 (1980): 175-180.\

[11] Cherkassky, Vladimir, and Filip M. Mulier. Learning from data: concepts, theory, and methods. John Wiley & Sons, 2007.

[12] Chen, Xi, et al. "Infogan: Interpretable representation learning by information maximizing generative adversarial nets." Advances in neural information processing systems 29 (2016).

[13] Goldie, John Gerard Scott. "Connectivism: A knowledge learning theory for the digital age?." Medical teacher 38.10 (2016): 1064-1069.

[14] Kuepers, Wendelin M., and David Pauleen. "Learning wisdom: Embodied and artful approaches to management education." Scandinavian Journal of Management 31.4 (2015): 493-500.

[15] Milner IV, H. Richard. "Culturally relevant, purpose-driven learning & teaching in a middle school social studies classroom." Multicultural Education 21.2 (2014): 9-17.

[16] Lavrenko, Victor, Raghavan Manmatha, and Jiwoon Jeon. "A model for learning the semantics of pictures." Advances in neural information processing systems 16 (2003).

[17] Yucong Duan. 面向数据,信息,知识过载的跨模态,跨尺度,介尺度主观认知语义建模与分析https://blog.Culturenet.cn/blog-3429562-1228985.html

[18] Yucong Duan, DIKWP based Digital Governance: Current Trends from "Data Governance","Information Governance", "Knowledge Governance" to "Wisdom Governance", October 2022, DOI: 10.13140/RG.2.2.15074.40645

[19] Yucong Duan, A Holism Trusted Semantic Computing based on DIKWP Integrating Trusted Data *Trusted Information*Trusted Knowledge*Trusted Wisdom* Trusted Purpose, October 2022, DOI: 10.13140/RG.2.2.22073.65128

[9] Yucong Duan, DIKWP Ultimate of Digital Economy: From Asymmetric Data Economy, Asymmetric Information Economy to Symmetric Knowledge Economy and Symmetric Wisdom Economy, November 2022DOI: 10.13140/RG.2.2.32178.48328

英文原文章阅读链接:

https://www.researchgate.net/publication/365635800_DIKWP_Ultimate_of_Digital_Economy_From_Asymmetric_Data_Economy_Asymmetric_Information_Economy_to_Symmetric_Knowledge_Economy_and_Symmetric_Wisdom_Economy

中文全文章阅读链接:

https://blog.sciencenet.cn/home.php?mod=space&uid=3429562&do=blog&id=1365170

 


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