Research on Similarity Fusion in Knowledge Fusion of Learning Object Repository based on Fuzzy Set Theory
Author:
Affiliation:
1. National Engineering, Research Center for E-Learning, Central China Normal University(CCNU), Wuhan, China
Funder
Ministry of Education of the People's Republic of China
Publisher
ACM Press
Reference16 articles.
1. Palavitsinis, N., Manouselis, N., & Sanchez-Alonso, S. 2014. Metadata quality in learning object repositories: a case study. The Electronic Library,32(1), 62--82
2. Wang, C. H., Hong, T. P., & Tseng, S. S. 2000. Integrating membership functions and fuzzy rule sets from multiple knowledge sources. Fuzzy Sets and Systems, 112(1), 141--154.
3. Ren, H., & Gou, J. 2013. Integrating Fuzzy Systems with Optimization Algorithm. International Journal of Applied Mathematics and Statistics™, 43(13), 23--29.
4. Huang, A. F., Wu, J. T., Yang, S. J., & Hwang, W. Y. 2012. The success of ePortfolio-based programming learning style diagnosis: Exploring the role of a heuristic fuzzy knowledge fusion. Expert Systems with Applications,39(10), 8698--8706.
5. Kuo, T. T., Tseng, S. S., & Lin, Y. T. 2003. Ontology-based knowledge fusion framework using graph partitioning. In International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems (pp. 11--20). Springer Berlin Heidelberg.
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