Abstract
<p class="BODYTEXT">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.</p>
Highlights
Nowadays the amount of web data stored in web servers is increasing rapidly
In the field of open and distance learning the knowledge transferred online is huge.The social networking sites Facebook, Orkut, LinkedIn and Whatsapp are examples of widely used popular networks to share the enormous amount of knowledge among the various users from which the users take the crucial decision in various domains, for example in educational domain, choosing the best institute to pursue higher studies, finding the premier and special institute to pursue research work, identifying the current requirement of corporations for the recruitment of learners, deciding on best online material available for the different class of academics and getting career guidance information
The knowledge extraction from the social web source for online recommendation system in open and distance learning environment is carried out with the help of a step by step procedure which is incorporated .The basic architectural layout of knowledge based decision support system shown in Figure 1.0 is proposed in this research work which provides the generic framework from which the knowledge extraction is initiated from various social web sources
Summary
Nowadays the amount of web data stored in web servers is increasing rapidly. “Online social networks” have exploded in popularity and rival the traditional web in terms of usage. In the field of open and distance learning the knowledge transferred online is huge.The social networking sites Facebook, Orkut, LinkedIn and Whatsapp are examples of widely used popular networks to share the enormous amount of knowledge among the various users from which the users take the crucial decision in various domains, for example in educational domain, choosing the best institute to pursue higher studies, finding the premier and special institute to pursue research work, identifying the current requirement of corporations for the recruitment of learners, deciding on best online material available for the different class of academics and getting career guidance information In this era, the web source acts as a resource pool which provides an infinite set of solutions to the users who are looking for useful information on the web. Some of the information like historical data, current data, feedback data and dynamic data were analysed and stored to reduce the complexity of web content for decision making process
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
