Collection Title:	SIU Thesis
Title :	A Collective Intelligence Framework for Collaborative Knowledge Creation and Sharing Systems
Material Type:	printed text
Authors:	Krissada Maleewong, Author ; Chutiporn Anutariya, Associated Name ; Vilas Wuwongse, Associated Name
Publisher:	Bangkok: Shinawatra University
Publication Date:	2013
Pagination:	xi, 135 p.
Layout:	ill, Tables.
Size:	30 cm.
Price:	500.00
General note:	SIU THE: SOIT-PhD-IT-2013-01 Thesis. [Ph.D. [Information Technology]]. - Shinawatra University, 2013.
Languages :	English (eng)
Descriptors:	[LCSH]Semantic Web
Keywords:	Collaborative Knowledge Creation and Sharing Systems, Collective Intelligence, Argumentation, Semantic Web, Social Web
Abstract:	Collaborative knowledge creation and sharing systems have been continuing to grow with increasing number of contributors and covering a wide range of disciplines. Many of such systems are successful in encouraging people to participate actively in content creation and knowledge sharing, and leading to the unanticipated explosion of innovative ideas. However, they still lack an effective mechanism to enhance collective intelligence and enable human-machine synergy. To tackle such problem, this thesis performs a literature survey, identifies important problems, and gathers key requirements of collaborative knowledge creation and sharing systems, as well as compares the requirements with existing solutions. It was found that the available systems are inefficient and insufficient to foster and facilitate community deliberation, while intelligent services are demanded. This research, therefore, aims to fill this need by developing a collective intelligence framework, namely Semantic Argumentation-based Model (SAM), which enhances a collaborative knowledge creation and sharing process and can serve as a solid foundation for such a system. SAM harnesses community deliberation by encouraging multiple users to cooperatively express ideas or positions on a complex issue, and to submit arguments which support or oppose other members’ ideas. In principle, an idea with high content quality and achieves a high degree of community agreement is considered as a potential solution to solve the issue. The community deliberation are structurally and semantically captured and encoded in RDF/OWL language conforming to the developed SAM Schema (SAMS) which enables automated analysis across the community knowledge base. In order to achieve quality-assured consensual knowledge, a number of useful measures are formally defined for assessing the quality of a user-generated content and determining a group preference on a certain position. A high-quality position supported by most members is considered as a potential position to solve the issue. Founded on SAM, a Web-based prototype system, namely ciSAM, has been developed in order to validate the practicality and usability of the proposed approach in real-world scenarios. The experimental results from various usage scenarios in this research verify that SAM can enhance collaborative knowledge creation and sharing more efficiently.
Curricular :	BSCS/MSIT/PhDT
Record link:	http://libsearch.siu.ac.th/siu/opac_css/index.php?lvl=notice_display&id=27647

SIU Thesis. A Collective Intelligence Framework for Collaborative Knowledge Creation and Sharing Systems [printed text] / Krissada Maleewong, Author ; Chutiporn Anutariya, Associated Name ; Vilas Wuwongse, Associated Name . - [S.l.] : Bangkok: Shinawatra University, 2013 . - xi, 135 p. : ill, Tables. ; 30 cm.
500.00
SIU THE: SOIT-PhD-IT-2013-01
Thesis. [Ph.D. [Information Technology]]. - Shinawatra University, 2013.
Languages : English (eng)

Descriptors:	[LCSH]Semantic Web
Keywords:	Collaborative Knowledge Creation and Sharing Systems, Collective Intelligence, Argumentation, Semantic Web, Social Web
Abstract:	Collaborative knowledge creation and sharing systems have been continuing to grow with increasing number of contributors and covering a wide range of disciplines. Many of such systems are successful in encouraging people to participate actively in content creation and knowledge sharing, and leading to the unanticipated explosion of innovative ideas. However, they still lack an effective mechanism to enhance collective intelligence and enable human-machine synergy. To tackle such problem, this thesis performs a literature survey, identifies important problems, and gathers key requirements of collaborative knowledge creation and sharing systems, as well as compares the requirements with existing solutions. It was found that the available systems are inefficient and insufficient to foster and facilitate community deliberation, while intelligent services are demanded. This research, therefore, aims to fill this need by developing a collective intelligence framework, namely Semantic Argumentation-based Model (SAM), which enhances a collaborative knowledge creation and sharing process and can serve as a solid foundation for such a system. SAM harnesses community deliberation by encouraging multiple users to cooperatively express ideas or positions on a complex issue, and to submit arguments which support or oppose other members’ ideas. In principle, an idea with high content quality and achieves a high degree of community agreement is considered as a potential solution to solve the issue. The community deliberation are structurally and semantically captured and encoded in RDF/OWL language conforming to the developed SAM Schema (SAMS) which enables automated analysis across the community knowledge base. In order to achieve quality-assured consensual knowledge, a number of useful measures are formally defined for assessing the quality of a user-generated content and determining a group preference on a certain position. A high-quality position supported by most members is considered as a potential position to solve the issue. Founded on SAM, a Web-based prototype system, namely ciSAM, has been developed in order to validate the practicality and usability of the proposed approach in real-world scenarios. The experimental results from various usage scenarios in this research verify that SAM can enhance collaborative knowledge creation and sharing more efficiently.
Curricular :	BSCS/MSIT/PhDT
Record link:	http://libsearch.siu.ac.th/siu/opac_css/index.php?lvl=notice_display&id=27647