A Knowledge Mining Algorithm for E-Courseware Based on Query Likelihood Model

With the rapid development of Internet, various forms of online learning platforms have emerged. As a major form of knowledge presentation, a number of electronic courseware (e-courseware) have been uploaded to these platforms for users to learn and share. Extracting main contents of an e-courseware document to form a knowledge framework is of great helpful for learners to select and utilize some courseware documents for their self-learning process. But at present, there are few studies concentrated on the information extraction for e-courseware. In this paper, a knowledge mining algorithm for e-courseware is proposed to facilitate learners quickly grasp main knowledge points of the courseware. In order to effectively organize the knowledge framework, this algorithm firstly uses the IRAKE algorithm to get some key phrases in each page of the e-courseware, and then uses the MMS process to filter the phrases with similar meanings, finally uses the query likelihood model to retrieve the sentences related to these key phrases in all the pages of the e-courseware document. The algorithm has good performance in mining the knowledge covered by the e-courseware.