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Visual knowledge representation of conceptual semantic networks

Visual knowledge representation of conceptual semantic networks This article presents methods of using visual analysis to visually represent large amounts of massive, dynamic, ambiguous data allocated in a repository of learning objects. These methods are based on the semantic representation of these resources. We use a graphical model represented as a semantic graph. The formalization of the semantic graph has been intuitively built to solve a real problem which is browsing and searching for lectures in a vast repository of colleges/courses located at Western Kentucky University ( http://HyperManyMedia.wku.edu ). This study combines Formal Concept Analysis (FCA) with Semantic Factoring to decompose complex, vast concepts into their primitives in order to develop knowledge representation for the HyperManyMedia [we proposed this term to refer to any educational material on the web (hyper) in a format that could be a multimedia format (image, audio, video, podcast, vodcast) or a text format (HTML webpages, PHP webpages, PDF, PowerPoint)] platform. Also, we argue that the most important factor in building the semantic representation is defining the hierarchical structure and the relationships among concepts and subconcepts. In addition, we investigate the association between concepts using Concept Analysis to generate a lattice graph. Our domain is considered as a graph, which represents the integrated ontology of the HyperManyMedia platform. This approach has been implemented and used by online students at WKU ( http://www.wku.edu ). http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Social Network Analysis and Mining Springer Journals

Visual knowledge representation of conceptual semantic networks

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Publisher
Springer Journals
Copyright
Copyright © 2010 by Springer-Verlag
Subject
Computer Science; Data Mining and Knowledge Discovery; Applications of Graph Theory and Complex Networks; Game Theory, Economics, Social and Behav. Sciences; Statistics for Social Science, Behavorial Science, Education, Public Policy, and Law; Methodology of the Social Sciences
ISSN
1869-5450
eISSN
1869-5469
DOI
10.1007/s13278-010-0008-2
Publisher site
See Article on Publisher Site

Abstract

This article presents methods of using visual analysis to visually represent large amounts of massive, dynamic, ambiguous data allocated in a repository of learning objects. These methods are based on the semantic representation of these resources. We use a graphical model represented as a semantic graph. The formalization of the semantic graph has been intuitively built to solve a real problem which is browsing and searching for lectures in a vast repository of colleges/courses located at Western Kentucky University ( http://HyperManyMedia.wku.edu ). This study combines Formal Concept Analysis (FCA) with Semantic Factoring to decompose complex, vast concepts into their primitives in order to develop knowledge representation for the HyperManyMedia [we proposed this term to refer to any educational material on the web (hyper) in a format that could be a multimedia format (image, audio, video, podcast, vodcast) or a text format (HTML webpages, PHP webpages, PDF, PowerPoint)] platform. Also, we argue that the most important factor in building the semantic representation is defining the hierarchical structure and the relationships among concepts and subconcepts. In addition, we investigate the association between concepts using Concept Analysis to generate a lattice graph. Our domain is considered as a graph, which represents the integrated ontology of the HyperManyMedia platform. This approach has been implemented and used by online students at WKU ( http://www.wku.edu ).

Journal

Social Network Analysis and MiningSpringer Journals

Published: Nov 25, 2010

References