A Generic Framework for Extraction of Knowledge from Social Web Sources (Social Networking Websites) for an Online Recommendation System
IRRODL Volume 16, Number 2, ISSN 1492-3831 Publisher: Athabasca University Press
Mining social web data is a challenging task and finding user interest for personalized and non-personalized recommendation systems is another important task. Knowledge sharing among web users has become crucial in determining usage of web data and personalizing content in various social websites as per the user's wish. This paper aims to design a framework for extracting knowledge from web sources for the end users to take a right decision at a crucial juncture. The web data is collected from various web sources and structured appropriately and stored as an ontology based data repository. The proposed framework implements an online recommender application for the learners online who pursue their graduation in an open and distance learning environment. This framework possesses three phases: data repository, knowledge engine, and online recommendation system. The data repository possesses common data which is attained by the process of acquiring data from various web sources. The knowledge engine collects the semantic data from the ontology based data repository and maps it to the user through the query processor component. Establishment of an online recommendation system is used to make recommendations to the user for a decision making process. This research work is implemented with the help of an experimental case study which deals with an online recommendation system for the career guidance of a learner. The online recommendation application is implemented with the help of R-tool, NLP parser and clustering algorithm.This research study will help users to attain semantic knowledge from heterogeneous web sources and to make decisions.
Sathick, J. & Venkat, J. (2015). A Generic Framework for Extraction of Knowledge from Social Web Sources (Social Networking Websites) for an Online Recommendation System. The International Review of Research in Open and Distributed Learning, 16(2), 247-271. Athabasca University Press.