Generating Linked Course Data

Abstract:

The uptake of semantic technology depends on the availability of useful tools that enable Web developers to generate linked course data automaticly. RDF triple allows web page to contain machine-readable content that is easier to find and mashable with other content. This paper describes a framework that turns this idea around, using RDF as a template language for the generation of machine-readable triple from human-readable data on Web page. Most existing methords generate RDF triple by combining the template with query results from a relational database. In the Linked Course Data Generating framework, the raw course data is turned into RDF triple, then is turned into linked data, finally is turned into ontology. This paper evaluates the performance of framework.

• Periodical: Advanced Materials Research (Volumes 718-720)
• Pages: 2359-2364
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.718-720.2359
• Online since: July 2013
• Authors: Zhao Hui Cai
• Keywords: Generating, Linked Course Data, Semantic

References

[1] Ming Xie. MULTI-GRANULARITY KNOWLEDGE MINING ON WEB. International Journal of Software Engineering and Knowledge Engineering (IJSEKE),2012.2

[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

[3] Data-gov Wiki: Towards Linking Course Data, AAAI Publications, 2010 AAAI Spring Symposium Series

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

[5] Varish M. et al., 2011. "Automatically Generating Course Linked Data from Tables", InCollection, Working notes of AAAI Fall Symposium on Open Course Knowledge: AI Opportunities and Challenges

[6] Ming Xie. Semantic-Based Linked Data Mining and Services. Journal of Information and Computational Science,2011.12

[7] Ming Xie. Knowledge Topic Aware-based Modeling, Optimzation and on Web of Collaborative Logistics System. In: Proceedings of the International Conference of China Communication Technology2010,2010.11

[8] Varish M. et al., 2011. "DC Proposal: Graphical Models and Probabilistic Reasoning for Generating Linked Data from Tables", InProceedings, Proceedings of Tenth International Semantic Web Conference, Part II

DOI: 10.1007/978-3-642-25093-4_24

[9] Ming Xie. Intelligent Knowledge-Point Auto-Extracting Model in Web Learning Resources. Journal of Computational Information Systems,2010.6

[10] Ming Xie. A New Intelligent Topic Extraction Model on Web. Journal of Computers,2011.3

[11] Ming Xie. Semantic Knowledge Mining on Web. Journal of Computational Information Systems,2011.11

[12] Fuyuko Matsumura, Iwao Kobayashi, Fumihiro Kato, Tetsuro Kamura, Ikki Ohmukai, Hideaki Takeda. Producing and Consuming Linked Open Data on Art with a Local Community. Proceedings of the Third International Workshop on Consuming Linked Data, COLD 2012, Boston, MA, USA, November 12, 2012.

[13] Gofran Shukair, Nikolaos Loutas, Vassilios Peristeras. Integrating Linked Metadata Repositories into the Web of Data. Proceedings of the Third International Workshop on Consuming Linked Data, COLD 2012, Boston, MA, USA, November 12, 2012.

[14] Jakub Stárka, Martin Svoboda, Irena Mlynkova. Analyses of RDF Triples in Sample Datasets. Proceedings of the Third International Workshop on Consuming Linked Data, COLD 2012, Boston, MA, USA, November 12, 2012.