Architecture to Organize Social Semantic Relevant Web Resources in a Knowledgebase

Abstract

Abstract-Learners who believe in self-directed learning usually refer all possible sources to gather information. They also explore social sites to understand the community's view about a topic. At times, this whole process of searching for the required information becomes confusing, time consuming and tedious. Instead of focusing on learning, they tend to spend more time on searching and storing relevant resources suitably in an organized way so that the resources relevant to a concept can be retrieved for later reference. In literature many models have been proposed that have used metadata to organize and store learning content. However they work on a specific customized learning structure intended for a specific institutional or organizational needs. This paper presents an architecture to search relevant resources using a social semantic focused crawler, to organize them in fragment ontology with the term specific relevance and to link each resource to semantically relevant concept in domain ontology, for generic educational use. A prototype of the proposed architecture has been implemented using java, MySql, Protégé and OWLAPI. The experiment study has been conducted on 'database' domain ontology and fragment ontology. The proposed architecture based content organization provides an easy retrieval of socially semantic relevant resources on the posted query topics.

Ranking Tagged Resources Using Social Semantic Relevance

“…Most of the coding on the proposed approach has been written using java programming language, and ProtégéOWL APIs to access ontologies. The knowledgebase consisting of ontologies has been developed using Protégé (Thukral, Bedi, & Banati, 2011a, 2011b. The SBS crawler was coded using java.…”