Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/77308
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Type: Conference paper
Title: Automated extraction of semantic concepts from semi-structured data: supporting computer-based education through the analysis of lecture notes
Author: Atapattu Mudiyanselage, T.
Falkner, K.
Falkner, N.
Citation: Proceedings of the 23rd International Conference on Database and Expert Systems Applications, held in Vienna, 3-6 September, 2012 / S.W. Liddle, K.-D. Schewe, A. Min Tjoa and X. Zhou (eds.): pp.161-175
Publisher: Springer-Verlag
Publisher Place: Germany
Issue Date: 2012
Series/Report no.: Lecture Notes in Computer Science; 7446
ISBN: 9783642325991
ISSN: 0302-9743
1611-3349
Conference Name: International Conference on Database and Expert Systems Applications (23rd : 2012 : Vienna)
Statement of
Responsibility: 
Thushari Atapattu, Katrina Falkner and Nickolas Falkner
Abstract: Computer-based educational approaches provide valuable supplementary support to traditional classrooms. Among these approaches, intelligent learning systems provide automated questions, answers, feedback, and the recommendation of further resources. The most difficult task in intelligent system formation is the modelling of domain knowledge, which is traditionally undertaken manually or semi-automatically by knowledge engineers and domain experts. However, this error-prone process is time-consuming and the benefits are confined to an individual discipline. In this paper, we propose an automated solution using lecture notes as our knowledge source to utilise across disciplines. We combine ontology learning and natural language processing techniques to extract concepts and relationships to produce the knowledge representation. We evaluate this approach by comparing the machine-generated vocabularies to terms rated by domain experts, and show a measurable improvement over existing techniques.
Keywords: Ontology; POS tagging; lecture notes; concept extraction
Rights: © Springer, Part of Springer Science+Business Media
RMID: 0020126404
DOI: 10.1007/978-3-642-32600-4_13
Appears in Collections:Computer Science publications

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