Visual knowledge representation of conceptual semantic networksSocial Network Analysis and Mining Journal
AbstractThis 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 University1. 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 HyperManyMedia2 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 WKU3.
Citation InformationLeyla Zhuhadar, Olfa Nasaoui, Robert Wyatt, and Rong Yang. Visual Knowledge Representation of Conceptual Semantic Networks, Social Network Analysis and Mining Journal, 219-229, Volume 1, 2011.