Ontology Based Scientific Keywords Recommendation System under Web 2.0

Authors

  • Na Xue School of Economics and Management, Beihang University
  • Su ling Jia School of Economics and Management, Beihang University
  • Jin xing Hao School of Economics and Management, Beihang University
  • Qiang Wang School of Economics and Management, Beihang University

DOI:

https://doi.org/10.3991/ijet.v8i4.2942

Abstract


As the born and communication base of new science and new technology, scientific research requires effective information integration and knowledge management to improve the efficiency of scientific research. The growth of e-science in circumstance of Web 2.0 has created a need to integrate large quantities of diverse and heterogeneous data. To tackle with the scientific information management problems, we proposed an ontology-based scientific keywords recommendation system under web 2.0. The main goal achieved is to extract the meaningful information and recommend to web scholars through the proposed system. The components of the system are Integration Interface, Service Module, Text Processor, Recommendation Module and Ontology Database. Experimental results and performance evaluation shows that the proposed system provides the effective way to recommend semantic related keywords for scholars.

Author Biographies

Na Xue, School of Economics and Management, Beihang University

School of Economics and Management, Beihang University

Su ling Jia, School of Economics and Management, Beihang University

School of Economics and Management, Beihang University

Jin xing Hao, School of Economics and Management, Beihang University

School of Economics and Management, Beihang University

Qiang Wang, School of Economics and Management, Beihang University

School of Economics and Management, Beihang University

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Published

2013-07-25

How to Cite

Xue, N., Jia, S. ling, Hao, J. xing, & Wang, Q. (2013). Ontology Based Scientific Keywords Recommendation System under Web 2.0. International Journal of Emerging Technologies in Learning (iJET), 8(4), pp. 17–22. https://doi.org/10.3991/ijet.v8i4.2942

Issue

Section

Special Focus Papers