Visual knowledge representation of conceptual semantic networks

摘要

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 sub-concepts. 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).