科研管理

科研管理 ›› 2011, Vol. 32 ›› Issue (5): 87-93.

• 论文 • 上一篇    下一篇

知识超网络环境中集成优化过程的知识共享服务研究

潘旭伟, 汪雅喜, 杨祎   

  1. 浙江理工大学管理科学与工程系,浙江 杭州 310018
  • 收稿日期:2010-10-11 修回日期:2011-01-29 出版日期:2011-05-27 发布日期:2011-06-14
  • 作者简介:潘旭伟(1977-11),男(汉),浙江缙云人,浙江理工大学管理科学与工程系主任,副教授,研究方向:知识管理,个性化信息服务。汪雅喜(1987-06),女(汉),浙江台州人,浙江理工大学硕士研究生,研究方向:知识管理,可拓理论。杨 祎(1988-05),男(汉),陕西西安人,浙江理工大学硕士研究生,研究方向:知识管理,超网络。
  • 基金资助:

    国家自然科学基金:知识超网络环境中快慢结合的知识可拓优化与共享理论研究(编号:71071144),2011.1-2013.12;浙江省自然科学基金:情境感知的自适应个性化信息服务研究(编号:Y107133),2008.1-2010.12。

The optimization step-Integrated knowledge sharing servicein knowledge supernetwork environment

Pan Xuwei, Wang Yaxi, Yang Yi   

  1. Department of Management Science and Engineering, Zhejiang University of Scienceand Technology, Hangzhou 310018, China
  • Received:2010-10-11 Revised:2011-01-29 Online:2011-05-27 Published:2011-06-14

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摘要： 为解决知识超网络环境中知识数量极大丰富但知识质量并不稳定可靠所引起的知识组织维护困难和知识共享服务效率与效果不佳问题,引入了知识优化环节,提出了集成优化过程的知识共享服务模式,该模式利用知识优化过程实现知识超网络环境中知识的自优化与自组织,并通过知识共享服务过程与知识优化过程的有机集成为用户提供快速有效的知识共享服务。借助可拓理论与方法,提出并建立了集成优化过程的知识共享服务实现流程,并对该流程中基于知识事物元可拓模型的自分类、自聚类和自识别等知识可拓优化方法以及知识共享服务过程与知识优化过程集成的原理与方式进行了研究。

关键词: 知识服务, 知识共享, 超网络, 知识优化, 可拓学

Abstract: In order to solve problems involving the difficulties in organizing and maintaining knowledge as well as low efficiency and lack of effectiveness in the knowledge sharing service process caused by the huge quantity of knowledge while its quality is instable and unreliable in the knowledge supernetwork environment, the knowledge optimization step is introduced, and an optimization step-integrated knowledge sharing service mode is proposed. In the proposal mode, knowledge will be self-optimized and self-organized in the optimization step, and the fast and effective knowledge sharing service is provided to the users by integrating a knowledge optimization step into the knowledge sharing service step. Using extenics theories and methods, the implementation process of optimization step-integrated knowledge sharing service is put forward and built, and extension knowledge optimization methods, such as self-classification, self-clustering, and self-recognition based on matter-behavior element extension model and the mechanisms with the ways of the integration between the knowledge sharing service step and optimization step in the implementation process are also studied.

Key words: knowledge service, knowledge sharing, supernetwork, knowledge optimization, extenics

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