Name
Abstract | ||
|---|---|---|
Maps such as concept maps and knowledge maps are often used as learning materials. These maps have nodes and links, nodes as key concepts and links as relationships between key concepts. From a map, the user can recognize the important concepts and the relationships between them. To build concept or knowledge maps, domain experts are needed. Therefore, since these experts are hard to obtain, the cost of map creation is high. In this study, an attempt was made to automatically build a domain knowledge map for e-learning using text mining techniques. From a set of documents about a specific topic, keywords are extracted using the TF/IDF algorithm. A domain knowledge map (K-map) is based on ranking pairs of keywords according to the number of appearances in a sentence and the number of words in a sentence. The experiments analyzed the number of relations required to identify the important ideas in the text. In addition, the experiments compared K-map learning to document learning and found that K-map identifies the more important ideas. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1016/j.compedu.2012.01.017 | Computers & Education |
Keywords | Field | DocType |
key concept,knowledge map,text mining technique,idf algorithm,map creation,important concept,domain knowledge map,domain expert,important idea,concept map,natural language processing,electronic learning,content analysis,programming,computational linguistics,concept mapping | Ontology,Concept map,Content analysis,E learning,Ranking,Domain knowledge,Computer science,Computational linguistics,Natural language processing,Artificial intelligence,Sentence | Journal |
Volume | Issue | ISSN |
59 | 2 | 0360-1315 |
Citations | PageRank | References |
17 | 0.80 | 15 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jae Hwa Lee | 1 | 17 | 0.80 |
| Aviv Segev | 2 | 249 | 21.04 |