Paper Info
Title
Keyword extraction for mining meaningful learning-contents on the Web using Wikipedia
Abstract
The purpose of this paper is to provide a solution of extracting appropriate keywords to identify meaningful learning-contents on the Web. There are some issues in identifying documents that have learning content. Firstly, the documents need to be identified according to the learning area of a student's school year. Secondly, the documents need to be identified according to the learning area that the student is now studying or studied. In this paper, we present a method of extracting keywords for mining meaningful learning-contents using Wikipedia. At first, we select the articles in Wikipedia with the arbitrary input keyword of learning items. Then, we select other Wikipedia's articles related to the articles selected by the first process, using links and categories of Wikipedia. Furthermore, we calculate degrees of association between the articles and the keywords using PF-IBF, and put the degree on each keyword. Finally, we screen the keywords using his/her curriculum guideline to adjust the keywords to the learning area of the student's school year. In the next step, we are planning to develop a method of screening keywords according to each student's ability, so that we can select more appropriate keywords for each student.
Year
DOI
Venue
2014
10.1109/FIE.2014.7044344
FIE
Keywords
Field
DocType
internet,web sites,computer aided instruction,data mining,document handling,pf-ibf,wikipedia,world wide web,appropriate keyword extraction,curriculum guideline,document identification,meaningful learning-content mining,e-learning,educational technology,electronic publishing,encyclopedias
Educational technology,World Wide Web,E learning,Information retrieval,Keyword extraction,Computer science,Curriculum,Meaningful learning,Encyclopedia,Electronic publishing,The Internet
Conference
ISSN
Citations 
PageRank 
0190-5848
0
0.34
References 
Authors
3
2
Name
Order
Citations
PageRank
Toyota, T.101.01
Yuan Sun21005.03