ODF: An Efficient OWL-Based Linked Course Data Generating Framework

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Although the intention of OWL is to provide an open, minimally constraining way for representing to represent rich and complex knowledge about things, there exists an increasing demands for the efficiency of course data generating. Addressing this issue, we present the ODF: a new OWL-based Linked Course Data generating framework, which makes it possible to specify semantic data directly. Generating such data directly does not only help in maintaining course data quality, but also opens up new optimization opportunities for link sources and, most importantly, makes generating process easier for users and system developers. We present OWL-based Linked Course Data generating framework and discuss the impact on Linked Data.

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613-616

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January 2014

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© 2014 Trans Tech Publications Ltd. All Rights Reserved

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[1] Ming Xie. MULTI-GRANULARITY KNOWLEDGE MINING ON WEB. International Journal of Software Engineering and Knowledge Engineering (IJSEKE), 2012. 2.

Google Scholar

[2] Ming Xie, Chanle Wu. Open Rainbow Services-Oriented Testbed: Low Cost Cloud Computing Platform for Open Systems Research. In: Proceedings of the Intelligent Systems and Applications (ISA) 2010, 2010. 5.

DOI: 10.1109/iwisa.2010.5473267

Google Scholar

[3] Rajesh Chitnis, Fedor V. Fomin, Petr A. Golovach. Preventing Unraveling in Social Networks Gets Harder. Proceedings of the Twenty-Seventh AAAI Conference on Artificial Intelligence, 2013. 7.

DOI: 10.1609/aaai.v27i1.8462

Google Scholar

[4] Varish M. et al., 2012. A Domain Independent Framework for Extracting Linked Semantic Data from Tables, InCollection, Search Computing - Broadening Web Search.

Google Scholar

[5] Ming Xie. Semantic-Based Linked Data Mining and Services. Journal of Information and Compu- tational Science, 2011. 12.

Google Scholar

[6] Ming Xie. Knowledge Topic Aware-based Modeling, Optimzation and on Web of Collaborative Logistics System. In: Proceedings of the International Conference of China Communication Tech- nology2010, 2010. 11.

Google Scholar

[7] Ming Xie. Intelligent Knowledge-Point Auto-Extracting Model in Web Learning Resources. Journal of Computational Information Systems, 2010. 6.

Google Scholar

[8] Ming Xie. A New Intelligent Topic Extraction Model on Web. Journal of Computers, 2011. 3.

Google Scholar

[9] Ming Xie. Semantic Knowledge Mining on Web. Journal of Computational Information Systems, 2011. 11.

Google Scholar