research-article

The emergence of Community Knowledge in a Complex network-based Folksonomy Self-organization mode

Authors:

Yuanyuan WangAuthors Info & Claims

ICSIE '22: Proceedings of the 11th International Conference on Software and Information Engineering

Pages 62 - 65

https://doi.org/10.1145/3571513.3571524

Published: 14 January 2023 Publication History

Abstract

The paper describes the internal mechanism of community knowledge emergence and optimizes the management model of innovation outcomes within labeled communities. Drawing on the triple helix model proposed the Folksonomy community knowledge emergence mechanism and visualized using Citespace software to analyze the authors’ collaboration network. According to the study, the mechanism of the community self-organizing emergence system is composed of three subjects: labeling behavior, Hub emergence, and structural pluralism, and there has the clear Matthew effect and giant branching structure within it. The conclusion has an important significance to the study of the application of community knowledge emergence networks.

References

[1]

[1] Kumari P and Pandit A A. 2018. A Brief Overview of Folksonomy and its applications. 2018 10th International Conference on Computational Intelligence and Communication Networks. IEEE, Ghaziabad, 82-87. https://doi.org/10.1109/CICN.2018.8864955

[2]

[2] Alruqimi M and Aknin N. 2019. Bridging the Gap between the Social and Semantic Web: Extracting domain-specific ontology from folksonomy. Journal of King Saud University-Computer and Information Sciences, 31(1), 15-21.

[3]

[3] Qi G, Hou L, and Chen J, et al. 2021. How does user social network improve innovation outcomes on a virtual innovation platform?: evidence from LEGO ideas platform. Journal of Global Information Management (JGIM), 29(3), 188-211.

[4]

[4] Leonardi P M. 2017. The social media revolution: Sharing and learning in the age of leaky knowledge. Information and Organization, 27(1), 47-59.

Digital Library

[5]

[5] Weng L and Menczer F. 2015. Topicality and impact in social media: diverse messages, focused messengers. PloS One, 10(2), 1-17.

[6]

[6] Li J. 2009. Research and application of the network nature of Folksonomy. Intelligence Science, 27(10), 1483-1486. (In Chinese)

[7]

[7] McLinden D, Myers S, and Seid M, et al. 2019. The learning exchange, a community knowledge commons for learning networks: qualitative evaluation to test acceptability, feasibility, and utility. JMIR Formative Research, 3(1), 1-11.

[8]

[8] Jin F and Jin J. 2017. Characteristics of knowledge emergence and its management. Research on the characteristics of knowledge emergence and its management. Enterprise Reform and Management, (15), 3-4. (In Chinese)

[9]

[9] An N, Teng G Q, and Bai S C, et al. 2017. A study on the emergence of domain core knowledge based on Web Hub. Book Intelligence Work, 61(18), 98-106. (In Chinese)

[10]

[10] Xie M, Lu J, and Chen G, et al. 2017. Folksonomy-based internet object profiling and relation extracting. GLOBECOM 2017-2017 IEEE Global Communications Conference. IEEE, Singapore, 1-6. https://doi.org/10.1109/GLOCOM.2017.8255084

Digital Library

[11]

[11] Sarpong D, AbdRazak A, and Alexander E, et al. 2017. Organizing practices of university, industry and government that facilitate (or impede) the transition to a hybrid triple helix model of innovation. Technological Forecasting and Social Change, 123(1), 142-152.

[12]

[12] [12] Peters I and Stock G W. 2007. Folksonomy and information retrieval. Proceedings of the American Society for Information Science and Technology, 44(1), 1-28.

[13]

[13] Suran S, Pattanaik V, and Draheim D. 2020. Frameworks for collective intelligence: A systematic literature review. ACM Computing Surveys (CSUR), 53(1), 1-36.

Digital Library

[14]

[14] BURT R S. 1976. Positions in networks. Journals of Social Forces, 55(1), 93-122.

[15]

[15] Behfar S K, Turkina E, and Burger-Helmchen T. 2018. Knowledge management in OSS communities: Relationship between dense and sparse network structures. International Journal of Information Management, 38(1), 167-174.

Digital Library

[16]

[16] BarMatho A, de Vasconcelos J B, Rocha Math, et al. 2017. A knowledge management approach to capture organizational learning networks. International Journal of Information Management, 37(6), 735-740.

Digital Library

[17]

[17] Zhang S, Wen Y, and Liu Q. 2022. Exploring student teachers’ social knowledge construction behaviors and collective agency in an online collaborative learning environment. Interactive Learning Environments, 30(3), 539-551.

[18]

[18] Jin F, Jin J, and Hou J L, et al. 2018. Simulation of Knowledge Emergence Based on Complexity. DEStech Transaction on Computer Science and Engineering, Wuhan, China, 73-79. https://doi.org/10.12783/dtcse/cmsam2018/26521

Cited By

Yoo JKim M(2023)Using natural language processing to analyze elementary teachers’ mathematical pedagogical content knowledge in online community of practiceContemporary Educational Technology10.30935/cedtech/1327815:3(ep438)Online publication date: 2023
https://doi.org/10.30935/cedtech/13278

Index Terms

The emergence of Community Knowledge in a Complex network-based Folksonomy Self-organization mode
1. Human-centered computing
  1. Collaborative and social computing
    1. Collaborative and social computing systems and tools
      1. Social tagging systems
    2. Collaborative and social computing theory, concepts and paradigms
      1. Computer supported cooperative work

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ICSIE '22: Proceedings of the 11th International Conference on Software and Information Engineering

October 2022

111 pages

ISBN:9781450397063

DOI:10.1145/3571513

Copyright © 2022 ACM.

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Publication History

Published: 14 January 2023

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The Education Department Fund Project of Liaoning Province

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ICSIE 2022

ICSIE 2022: 2022 11th International Conference on Software and Information Engineering

October 21 - 23, 2022

Beijing, China

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Cited By

Yoo JKim M(2023)Using natural language processing to analyze elementary teachers’ mathematical pedagogical content knowledge in online community of practiceContemporary Educational Technology10.30935/cedtech/1327815:3(ep438)Online publication date: 2023
https://doi.org/10.30935/cedtech/13278

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