Research on Similarity Fusion in Knowledge Fusion of Learning Object Repository based on Fuzzy Set Theory
Authors: 
Tao Huang, Yun Chen, Hao ZhangAuthors Info & Claims
Tao Huang, Yun Chen, Hao ZhangAuthors Info & ClaimsPages 79 - 83
Abstract
The construction of learning object repository is the key infrastructure of learning object organization under the E-learning environment. At present, the learning object repository is redundant construction, scattered and confused, lack the creation of tacit knowledge into explicit knowledge, is not conducive to knowledge sharing. In view of the above problems, this paper proposed a learning object repository fusion method, designed and constructed learning object knowledge units and fusion rule base, and applied fuzzy set theory to knowledge unit similarity fusion. Finally, we verified the effectiveness and feasibility of the method through the experiments, and get the more reliable results than single knowledge source detection, reduced the uncertainty of the fusion results. It has a certain reference value for improving the quality of learning object repository and collaborative work among repositories.
References
[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.
[6]
Lao N, Mitchell T, Cohen W W. 2011. Random walk inference and learning in a large scale knowledge base[C]//Proceedings of the Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 529--539.
[7]
Dong X, Gabrilovich E, Heitz G, et al. 2014. Knowledge vault: A web-scale approach to probabilistic knowledge fusion[C]//Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 601--610.
[8]
Dong X L, Naumann F. 2009. Data fusion: resolving data conflicts for integration[J]. Proceedings of the VLDB Endowment, 2(2): 1654--1655.
[9]
Dempster, A. 2008. Upper and lower probabilities induced by a multivalued mapping. Classic Works of the Dempster-Shafer Theory of Belief Functions, 57--72.
[10]
Smirnov, A., Levashova, T., & Shilov, N. 2015. Patterns for context-based knowledge fusion in decision support systems. Information Fusion, 21, 114--129.
[11]
Morgado, E. M. M., Ortuño, R. A. C., Yang, L. L., & Ferreras-Fernández, T. 2014. Adaptation of Descriptive Metadata for Managing Educational Resources in the GREDOS Repository. International Journal of Knowledge Management (IJKM), 10(4), 50--72.
[12]
Yanzhang, C. X. D. E. W., & Qi, X. W. G. 2011. Knowledge Unit Model for Emergency Management of Unconventional Incident [J]. Journal of Intelligence, 12, 004.
[13]
Bryniarska, A. 2012. The algorithm of knowledge defuzzification in semantic network. Information Systems Architecture and Technology, Networks Design and Analysis, 23--32.
[14]
Dong, X. L., Gabrilovich, E., Heitz, G., Horn, W., Murphy, K., Sun, S., & Zhang, W. 2014. From data fusion to knowledge fusion. Proceedings of the VLDB Endowment,7(10), 881--892.
[15]
Fisch, D., Kalkowski, E., & Sick, B. 2014. Knowledge fusion for probabilistic generative classifiers with data mining applications. IEEE Transactions on Knowledge and Data Engineering, 26(3), 652--666.
[16]
Bryl, V., Bizer, C., Isele, R., Verlic, M., Hong, S. G., Jang, S., ... & Choi, K. S. 2014. Interlinking and knowledge fusion. In Linked Open Data--Creating Knowledge Out of Interlinked Data (pp. 70--89). Springer International Publishing.
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- Research on Similarity Fusion in Knowledge Fusion of Learning Object Repository based on Fuzzy Set Theory
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Published In
August 2017
94 pages
ISBN:9781450352833
DOI:10.1145/3134847
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Published: 06 August 2017
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ICDTE '17
ICDTE '17: 2017 International Conference on Digital Technology in Education
August 6 - 8, 2017
Taipei, Taiwan
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