Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
Knowledge maps: An essential technique for conceptualisation
Data & Knowledge Engineering
A new approach for constructing the concept map
Computers & Education
Mining e-Learning domain concept map from academic articles
Computers & Education
Corpus-based Chinese-Korean abstracting translation system
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Research on the Auto-construction Methods of Concept Map
IHMSC '09 Proceedings of the 2009 International Conference on Intelligent Human-Machine Systems and Cybernetics - Volume 02
Design of a performance-oriented workplace e-learning system using ontology
Expert Systems with Applications: An International Journal
Data mining for adaptive learning in a TESL-based e-learning system
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Ontology technology to assist learners' navigation in the concept map learning system
Expert Systems with Applications: An International Journal
Ontology learning from biomedical natural language documents using UMLS
Expert Systems with Applications: An International Journal
A cloud of FAQ: A highly-precise FAQ retrieval system for the Web 2.0
Knowledge-Based Systems
| Hi-index | 0.00 |
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.