Automated Extraction of Semantic Concepts from Semi-structured Data: Supporting Computer-Based Education through the Analysis of Lecture Notes
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.
Keywordsontology POS tagging lecture notes concept extraction
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- 2.McArthur, D., Stasz, C., Hotta, J., Peter, O., Burdorf, C.: Skill-oriented task sequencing in an intelligent tutor for basic algebra. RAND Note 17(4), 281–307 (1988)Google Scholar
- 7.Issa, R., Arciszewski, T.: Ontology: An Introduction, Teaching Modules (PowerPoint presentation). In: ASCE Global Center of Excellence in Computing (2011)Google Scholar
- 8.Toutanova, K., Klein, D., Manning, C., Singer, Y.: Feature-Rich Part-of-Speech Tagging with a Cyclic Dependency Network. In: North American Chapter of the Association for Computational Linguistics on Human Language Technology, pp. 252–259. Association for Computational Linguistics, Canada (2003)Google Scholar
- 9.The Stanford NLP (Natural Language Processing) Group, http://nlp.stanford.edu/software/corenlp.shtml
- 13.Ono, M., Harada, F., Shimakawa, H.: Semantic Network to Formalize Learning Items from Lecture Notes. International Journal of Advanced Computer Science 1(1), 10–15 (2011)Google Scholar
- 17.Apache POI- the Java API for Microsoft Documents, http://poi.apache.org/
- 18.Brown Corpus, http://en.wikipedia.org/wiki/Brown_Corpus
- 19.Cimiano, P.: Ontology Learning and Population from Text: Algorithms, Evaluation and Applications. Springer, New York (2006)Google Scholar
- 20.Understanding the PowerPoint MS-PPT Binary File Format, http://msdn.microsoft.com/en-us/library/gg615594.aspx#UnderstandMS_PPT_Overview