Organisational Knowledge Acquisition with Contested Collective Intelligence in the Web Environment
Volume 4, Issue 2, March 2016, Pages: 41-49
Received: May 11, 2016;
Published: May 12, 2016
Views 2916 Downloads 78
Gangmin Li, Department of Computer Science and Software Engineering, Xi’an Jiaotong-Liverpool University, Suzhou, Jiangsu, China
Knowledge acquisition (KA) is a hard problem in knowledge engineering. Big Data Analytics (BDA), aiming at derives value out of big data, sheds light on this problem. Advanced data analysing methods and computational platforms make it possible to imitate large members of communities and interactions among the community members. This paper reports the efforts on capturing organisational knowledge through a “Contested Collective Intelligence (CCI)” model in the web environment. We assume that web users are individual experts and the whole web community is a big organisation. The organizational knowledge on the web is emerged and revealed through the interactions where individual users freely express themselves and interact with others to clarify facts, argue about meaning and debate about truth through claim and counterclaims. It is a hope that by capturing those claims, the connections between claims and the final agreement on understanding of the meaning, the collective knowledge emerged on the web can be captured, stored and reused.
Organisational Knowledge Acquisition with Contested Collective Intelligence in the Web Environment, Software Engineering.
Vol. 4, No. 2,
2016, pp. 41-49.
William Kent. Data and Reality: “A Timeless Perspective on Perceiving and Managing Information in Our Imprecise World”, 3rd Edition, ISBN-10: 1935504215, 2012.
Potter, Steven. "A Survey of Knowledge Acquisition from Natural Language". Technology Maturity Assessment (TMA). Retrieved 9 July 2014.
Cambria, Erik. "Affective computing and sentiment analysis" (PDF). IEEE Intelligent Systems 31 (2): 102–107. 2016.
"A Twitter and web sentiment analysis tool". werfamous.com.
Sensemaking Workshops, ACM Computer-Human Interaction (CHI) Conferences, 2012, 2013, 2014, 2015.
Kiku Jones and Lori N. K. Leonard. , “From Tacit Knowledge to Organizational Knowledge for Successful”. KM. W.R. King (ed.), Knowledge Management and Organizational Learning, Annals of Information Systems 4, DOI 10.1007/978-1-4419-0011-1_3, 2009.
Ikujiro Nonaka, Georg von Krogh, “Perspective—Tacit Knowledge and Knowledge Conversion: Controversy and Advancement in Organizational Knowledge Creation Theory”. Organization Science 20(3):635-652. 2009
Xindong Wu. “Data Mining with Big Data”. International Conference on Big Data and Cloud Computing December 28-30, 2013 Xiamen, China, 2013.
V. Terziya,O. Shevchenko, M. Golovianko, “An Introduction to Knowledge Computing”, 2014.
De Liddo, Anna, G. Li and Buckingham Shum, Simon. “Cohere: A prototype for contested collective intelligence”. In: ACM Computer Supported Cooperative Work (CSCW 2010) - Workshop: Collective Intelligence In Organizations - Toward a Research Agenda, February 6-10, 2010, Savannah, Georgia, USA.
Buckingham Shum, S., Uren, V., Li, G., Domingue, J., Motta, E., “Visualising internetworked argumentation”. Visualizing Argumentation: Software Tools for Collaborative and Educational Sense-Making. P. A. Kirschner, Buckingham Shum, S. and Carr, C. London, Springer-Verlag, 185-204. 2003.
De Liddo, A., Sandor, A. & Buckingham Shum, S., “Contested Collective Intelligence: Rationale, Technologies, and a Human-Machine Annotation Study”, Computer Supported Cooperative Work: the journal of collaborative computing, vol. 21, no. 4-5, pp. 417-448, 2012.
Buckingham Shum and De Liddo, Anna. “Collective intelligence for OER sustainability”. Collective Intelligence in Organizations. February 6-10, Savannah, 2010.
De Liddo, A. & Buckingham Shum, S., “The Evidence Hub: Harnessing the Collective Intelligence of Communities to Build Evidence-Based Knowledge”, Large Scale Ideation and Deliberation Workshop, 6th International Conference on Communities & Technologies, 2013.
G. Li, “Contested Collective Intelligence for Knowledge Networks Construction and Services”, International conference on Computing and Technology Innovation (CTI 2015), 27th-28th May, 2015, best paper.
Klein, G., Moon, B. and Hoffman, R.F. (2006). Making sense of sensemaking 1: Alternative Perspectives. IEEE Intelligent Systems, 21(4), 70-73.
Knight, S., Buckingham Shum, S. & Littleton, K., “Collaborative sensemaking in learning analytics”, CSCW and Education Workshop (2013): Viewing education as a site of work practice, co-located with the 16th ACM Conference on Computer Support Cooperative Work and Social Computing, CSCW 2013.
De Liddo, A. & Buckingham Shum, S., “Improving online deliberation with argument network visualization”, Digital Cities 8. 2013.
Iandoli, L., Quinto, I., De Liddo, A. & Buckingham Shum, S., “A Debate Dashboard to Enhance Online Knowledge Sharing.”, VINE The Journal of Information and Knowledge Management Systems, vol. 42, no. 1, pp. 67-93, 2012.
Marcus Kracht, “Agreement Morphology, Argument Structure and Syntax” 3rd Ed, Los Angeles, September 2005.