摘要
云计算产业联盟中知识的分散性、海量性及复杂性增加了联盟成员知识匹配的难度,影响知识匹配的全面性和准确性。针对上述问题提出云计算产业联盟知识匹配流程,并设计基于Map Reduce的改进综合语义相似度计算匹配方法。该方法将综合语义相似度计算中加入非层次关系的影响因素,从而确保云计算产业联盟知识匹配的全面性。同时,将改进的综合语义相似度计算与Map Reduce函数结合实现并行化处理,提高知识匹配的准确性和时效性。最后通过实验进行仿真结果分析,证明了该方法的优越性。
In the cloud computing industry alliance, the complexity of knowledge increases the difficulty of knowl-edge puts matching, and affects the comprehensiveness and accuracy. In order to solve the above problems, this paper forward the knowledge matching process of the cloud computing industry alliance, and designs an improved method based on Map Reduce. In this method, the influence factors of the non-hierarchical relationship are added into the comprehensive semantic similarity calculation, so as to ensure the comprehensiveness of the knowledge matching of the cloud computing industry alliance. At the same time, the improved semantic similarity calculation and Map Reduce function are combined to realize thee parallel processing to improve the accuracy and timeliness of knowledge matching. Finally, the simulation results are given to demonstrate the superiority of the method.
作者
于建萍
高长元
何晓燕
YU Jian-plng GAO Chang-yuan HE Xiao-yan(College of Management, Harbin University of Science and Technology, Harbin 150040, China High-tech Industrial Development Research Center, Harbin University of Science and Technology,Harbin 150040, China)
出处
《科技与管理》
2017年第2期9-15,共7页
Science-Technology and Management
基金
国家自然科学基金项目(71272191)
黑龙江省哲学社会科学研究规划项目(16GLB01)
黑龙江省博士后基金项目(LBH-Z15046)
关键词
云计算产业联盟
知识匹配
综合语义相似度计算
cloud computing industry alliance
knowledge matching
integrated semantic similarity computing
