Learning Resource Correlation Mining Based on the Wisdom of Crowds

Abstract

Mining and setting up semantic relations between learning resources is an important premise to achieve effective learning resource organization, and is also an important basis for deep integration and sharing. To work out the knowledge correlations among the massive volume of learning resources, it is not enough and difficult to implement that only base on machines, because it requires full understanding of the relevant concepts, knowledge relationships, as well as specific domain knowledge, which is hard for machines. This paper studies the common methods of semantic correlation of learning resources, and proposes a knowledge correlation model based on the wisdom of crowds and design a learning Resource Correlation system. The system makes full use of human subjective initiative and elaborately solves the semantic problem of knowledge which is hard to understand for machines. And more, it can effectively aggregate all relation marking and comments submitted by the users to reasonable results.