Int J Performability Eng ›› 2019, Vol. 15 ›› Issue (8): 2258-2266.doi: 10.23940/ijpe.19.08.p26.22582266

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Heterogeneous Knowledge Fusion Algorithm for Minority Cultural Resources based on MapReduce

Ying Liu, Juxiang Zhou*, and Jianhou Gan   

  1. Key Laboratory of Education Informalization for Nationalities, Ministry of Education, Yunnan Normal University, Kunming, 650500, China
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  • About author:Ying Liu is a master's student in the Key Laboratory of Educational Informatization for Nationalities (Ministry of Education) at Yunnan Normal University. Her research interests include the informatization of ethnic education resources. Juxiang Zhou received her Ph.D. in 2019 from Dalian University of Technology. She is currently an assistant research fellow from the Key Laboratory of Educational Informatization of Nationalities (Ministry of Education) at Yunnan Normal University. She specializes in education informationization in digital resource processing. Jianhou Gan received his Ph.D. in computational metallurgy from Kunming University of Science and Technology in 2016. He is currently the vice director of the Key Laboratory of Educational Informatization for Nationalities at Yunnan Normal University. His research interests include knowledge engineering and educational informatization for nationalities.
  • Supported by:
    The research is supported by the National Nature Science Fund Project (No. 61862068), Key Project of Applied Basic Research Program of Yunnan Province (No. 2016FA024), Program for Innovative Research Team (in Science and Technology) in University of Yunnan Province, and Starting Foundation for Doctoral Research of Yunnan Normal University (No. 2017ZB013).

Abstract: Aiming at the shortcomings of current knowledge fusion methods and understanding the knowledge fusion algorithm in the big data environment, this paper proposes a heterogeneous knowledge fusion algorithm based on MapReduce for minority cultural resources. In order to improve the performance of the fusion algorithm, the algorithm is used in the similarity calculation. It is improved on the basis of the traditional attribute similarity calculation method. Based on the Hadoop platform and MapReduce framework, the experimental platform is validated. The experimental results show that the proposed MapReduce-based heterogeneous knowledge fusion algorithm for ethnic cultural resources is effective and feasible, both in terms of accuracy and effectiveness.

Key words: knowledge fusion, ethnic minorities, MapReduce, ontology alignment