Knowledge Schematization Method Based on Link and Semantic Relationship

How to present knowledge in a more acceptable form has been a difficult problem. In most traditional conceptualization methods, educators always summarize and describe knowledge directly. Some education experiences have demonstrated schematization, which depicts knowledge by its adjacent knowledge units, is more comprehensible to learners. In conventional knowledge representation methods, knowledge schematization must be artificially completed. In this paper, a possible approach is proposed to finish knowledge schematization automatically. We explore the relationship between the given concept and its adjacent concepts on the basis of Wikipedia concept topology (WCT) and then present an innovative algorithm to select the most related concepts. In addition, the state-of-the-art neural embedding model Word2Vec is utilized to measure the semantic correlation between concepts, aiming to further enhance the effectiveness of knowledge schematization. Experimental results show that the use of Word2Vec is able to improve the effectiveness of selecting the most correlated concepts. Moreover, our approach is able to effectively and efficiently extract knowledge structure from WCT and provide available suggestions for students and researchers.