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Social Learning Systems : = The Design of Evolutionary, Highly Scalable, Socially Curated Knowledge Systems.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Social Learning Systems :/
Reminder of title:	The Design of Evolutionary, Highly Scalable, Socially Curated Knowledge Systems.
Author:	Hemmatazad, Nolan.
Description:	1 online resource (111 pages)
Notes:	Source: Dissertation Abstracts International, Volume: 77-04(E), Section: B.
Subject:	Computer science. -
Online resource:	click for full text (PQDT)
ISBN:	9781339286075

Social Learning Systems : = The Design of Evolutionary, Highly Scalable, Socially Curated Knowledge Systems.
Hemmatazad, Nolan.

Social Learning Systems :The Design of Evolutionary, Highly Scalable, Socially Curated Knowledge Systems. - 1 online resource (111 pages)

Source: Dissertation Abstracts International, Volume: 77-04(E), Section: B.

Thesis (Ph.D.)--University of Nebraska at Omaha, 2015.

Includes bibliographical references

In recent times, great strides have been made towards the advancement of automated reasoning and knowledge management applications, along with their associated methodologies. The introduction of the World Wide Web peaked academicians' interest in harnessing the power of linked, online documents for the purpose of developing machine learning corpora, providing dynamical knowledge bases for question answering systems, fueling automated entity extraction applications, and performing graph analytic evaluations, such as uncovering the inherent structural semantics of linked pages. Even more recently, substantial attention in the wider computer science and information systems disciplines has been focused on the evolving study of social computing phenomena, primarily those associated with the use, development, and analysis of online social networks (OSN's).

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9781339286075Subjects--Topical Terms:

573171
Computer science.
Index Terms--Genre/Form:

554714
Electronic books.

Social Learning Systems : = The Design of Evolutionary, Highly Scalable, Socially Curated Knowledge Systems.
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245 1 0 $a Social Learning Systems : $b The Design of Evolutionary, Highly Scalable, Socially Curated Knowledge Systems.
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300 $a 1 online resource (111 pages)
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500 $a Source: Dissertation Abstracts International, Volume: 77-04(E), Section: B.
500 $a Adviser: Qiuming Zhu.
502 $a Thesis (Ph.D.)--University of Nebraska at Omaha, 2015.
504 $a Includes bibliographical references
520 $a In recent times, great strides have been made towards the advancement of automated reasoning and knowledge management applications, along with their associated methodologies. The introduction of the World Wide Web peaked academicians' interest in harnessing the power of linked, online documents for the purpose of developing machine learning corpora, providing dynamical knowledge bases for question answering systems, fueling automated entity extraction applications, and performing graph analytic evaluations, such as uncovering the inherent structural semantics of linked pages. Even more recently, substantial attention in the wider computer science and information systems disciplines has been focused on the evolving study of social computing phenomena, primarily those associated with the use, development, and analysis of online social networks (OSN's).
520 $a This work followed an independent effort to develop an evolutionary knowledge management system, and outlines a model for integrating the wisdom of the crowd into the process of collecting, analyzing, and curating data for dynamical knowledge systems. Throughout, we examine how relational data modeling, automated reasoning, crowdsourcing, and social curation techniques have been exploited to extend the utility of web-based, transactional knowledge management systems, creating a new breed of knowledge-based system in the process: the Social Learning System (SLS).
520 $a The key questions this work has explored by way of elucidating the SLS model include considerations for 1) how it is possible to unify Web and OSN mining techniques to conform to a versatile, structured, and computationally-efficient ontological framework, and 2) how large-scale knowledge projects may incorporate tiered collaborative editing systems in an effort to elicit knowledge contributions and curation activities from a diverse, participatory audience.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Computer science. $3 573171
650 4 $a Information technology. $3 559429
655 7 $a Electronic books. $2 local $3 554714
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690 $a 0489
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a University of Nebraska at Omaha. $b Information Technology. $3 1182084
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