Computer Science ›› 2018, Vol. 45 ›› Issue (7): 286-292.doi: 10.11896/j.issn.1002-137X.2018.07.049

• Interdiscipline & Frontier • Previous Articles     Next Articles

KAAS-based Service Mechanism of Technology Resources for Collaborative Innovation

RAO Yuan, LU Shu-min   

  1. The Lab of Social Intelligence and Complex Data Processing,School of Software Engineering,Xi’an Jiaotong University,Xi’an 710049,China
  • Received:2017-05-21 Online:2018-07-30 Published:2018-07-30

Abstract: Based on the definition and analysis of KAAS,ascience and technology resource collaborative service model,called STRCS,was proposed in this paper.STRCS model includes technology resources model,service model and colla-borative model,and corresponding technology resources and service.At the same time,the service mapping mechanism between KAAS and STRCS was built to provide some new knowledge aggregation service pattern.Furthermore,aiming at the way of classification and aggregation of multiple tags querying and dynamic document indexing,an optimized mechanism was proposed.Meanwhile,knowledge synergy mechanism and personalized service of science and technology resources were given.A public service platform for science and technology resources based on socialization and know-ledge service was developed,and optimization of algorithm was used to enhance the ability and accuracy of personalized recommendation of science and technology resources platform,thus providing a new solution for the implementation and integration of collaborative innovation of science and technology resources and personalized knowledge service.

Key words: KAAS, Knowledge service cloud, Technology resource, Collaborative innovation service, STRCS

CLC Number: 

  • TP182
[1]吴兴惠,马文会,戴永年.加强产学研用协同创新,提升服务水平[EB/OL].[2016-5-17].<br /> [2]西安科技大市场[EB/OL].[2017-4-19].<br /> [3]广州科技资源服务平台[OL].[2017-4-19].<br /> [4]WANG F Y.Decision Service and Academic Analytics for Deve-lopment of S&T based on Open Source Intelligence and Big Data[J].Bulletin of Chinese Academy of Sciences,2012,27(5):527-537.(in Chinese)<br /> 王飞跃.知识产生方式和科技决策支撑的重大变革——面向大数据和开源信息的科技态势解析与决策服务[J].中国科学院院刊,2012,27(5):527-537.<br /> [5]SULTAN,NABIL.Knowledge management in the age of cloud computing and Web 2.0:Experiencing the power of disruptive innovations [J].IEEE Engineering Management Review,2015,33(1):37-46.<br /> [6]DEPEIGE A,DOYENCOURT D.Actionable Knowledge As a Service (AKAAS):Leveraging big data analytics in cloud computing environments [J].Journal of Big Data,2015,2(1):2-12.<br /> [7]JENSEN M B,JOHNSONE B,LORENZ E,et al.Forms ofknowledge and modes of innovation [J].Research Policy,2007,36(5):680-693.<br /> [8]BURT R S.Information and Structural Holes:Comment on Rea-gans and Zuckerman [J].Industrial and Corporate Change,2008,17(5):953-969.<br /> [9]ZHUGE H,XU B.Basic operations,completeness and dynamicity of Cyber Physical Socio semantic link network CPSSocio-SLN Concurrency and Computation[J].Practice and Experience,2011,23(9):924-939.<br /> [10]WANG Z,ZHANG J,FENG J,et al.Knowledge Graph andText Jointly Embedding[C]∥Conference on Empirical Methods in Natural Language Processing.2014:1591-1601.<br /> [11]LIU J,JIN S N.Kaas(Knowledge as a service):Stratified Model of Knowledge Service Based on Needs of Readers and Practice[J].Information Science,2014(3):55-60.(in Chinese)<br /> 刘军,金淑娜.Kaas知识即服务:面向读者需求的分层知识服务模型及实践[J].情报科学,2014(3):55-60.<br /> [12]GROLINGER K,MEZGHANI E,CAPRETZ M A M,et al.Collaborative knowledge as a service applied to the disaster ma-nagement domain[J].International Journal of Cloud Computing,2015,4(1):1-22.
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