基于本体的扩散制造主动式知识服务系统研究与应用
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摘要
在快速扩散制造模式下多企业环境中,由于企业与企业之间缺乏一个全局的知识服务平台,工程技术人员很难获取其它企业中的相关知识,并且企业内部的知识搜索也没有考虑到员工的知识背景存在个性化差异,导致扩散企业知识服务的水平不高。为此,本文给出了一种基于本体的主动式知识服务系统,旨在解决扩散制造模式下多企业环境中知识利用存在的问题。
本文首先分析了扩散制造模式下多企业环境中知识利用所存在的问题,并分析了主动式知识服务及其关键技术的研究现状。根据扩散制造模式下多企业环境中的知识利用需求,给出了基于本体的主动式知识服务系统框架,并详细阐述了框架的各个组成部分以及主动式知识服务系统的运行过程。
然后,研究了基于本体的主动式知识服务系统的关键技术:扩散制造业务过程知识需求模型构建和用户模型自学习。针对扩散制造领域多企业环境的特征,给出了一种扩散制造业务过程知识需求模型构建方法,并为其中的本体划分关键过程设计了一种基于遗传禁忌算法的本体划分算法。针对扩散制造多企业环境中的用户模型自学习需求,设计了基于本体归并的扩散制造用户模型自学习算法。
最后,结合某国防科研重大专项项目的应用背景,给出了基于本体的主动式知识服务系统的初步实现,为解决扩散制造模式下多企业环境中知识利用存在的问题提供了切实有效的方法。
Due to a lack of global knowledge service platform between enterprises in the multi-enterprises environment of the extended manufacturing, engineers obtain relevant knowledge in other enterprises difficultly. And the knowledge search within enterprise does not take into account the personalized difference of the staff. All these induce that the performance of knowledge service in the multi-enterprises environment is low. Therefore, the idea of proactive knowledge service system based on ontology is studied to solve these problems.
Firstly, the issues that existed in the knowledge use in the extended manufacturing multi-enterprises environment are analyzed. Then, the research situations of proactive knowledge service and the key techniques’research situations of proactive knowledge service are analyzed. According to the requirements of knowledge use in the multi-enterprises environment of the extended manufacturing, a proactive knowledge service system framework based on ontology is given; besides, the components of this framework and the process of proactive knowledge service system are described in detail.
Then, the key techniques of the framework including business process knowledge requirement model construction and the self-study process of user profile are deeply researched. Aim at the characteristics of the extended manufacturing multi-enterprises environment, a business process knowledge requirement model construction method is presented, and an ontology partition algorithm based on Tabu and Genetic Algorithm is designed for the ontology partition process in the method. According to the requirements of user profile self-study in the multi-enterprises environment of the extended manufacturing, a user profile self-study method based on ontology merge is given.
Lastly, combining with the application background of a national defense special project, the initial realization of the ontology-based extended manufacturing proactive knowledge service system is presented, which provides an effectual method to solve issues existed in the knowledge use in the multi-enterprises environment of the extended manufacturing.
本文首先分析了扩散制造模式下多企业环境中知识利用所存在的问题,并分析了主动式知识服务及其关键技术的研究现状。根据扩散制造模式下多企业环境中的知识利用需求,给出了基于本体的主动式知识服务系统框架,并详细阐述了框架的各个组成部分以及主动式知识服务系统的运行过程。
然后,研究了基于本体的主动式知识服务系统的关键技术:扩散制造业务过程知识需求模型构建和用户模型自学习。针对扩散制造领域多企业环境的特征,给出了一种扩散制造业务过程知识需求模型构建方法,并为其中的本体划分关键过程设计了一种基于遗传禁忌算法的本体划分算法。针对扩散制造多企业环境中的用户模型自学习需求,设计了基于本体归并的扩散制造用户模型自学习算法。
最后,结合某国防科研重大专项项目的应用背景,给出了基于本体的主动式知识服务系统的初步实现,为解决扩散制造模式下多企业环境中知识利用存在的问题提供了切实有效的方法。
Due to a lack of global knowledge service platform between enterprises in the multi-enterprises environment of the extended manufacturing, engineers obtain relevant knowledge in other enterprises difficultly. And the knowledge search within enterprise does not take into account the personalized difference of the staff. All these induce that the performance of knowledge service in the multi-enterprises environment is low. Therefore, the idea of proactive knowledge service system based on ontology is studied to solve these problems.
Firstly, the issues that existed in the knowledge use in the extended manufacturing multi-enterprises environment are analyzed. Then, the research situations of proactive knowledge service and the key techniques’research situations of proactive knowledge service are analyzed. According to the requirements of knowledge use in the multi-enterprises environment of the extended manufacturing, a proactive knowledge service system framework based on ontology is given; besides, the components of this framework and the process of proactive knowledge service system are described in detail.
Then, the key techniques of the framework including business process knowledge requirement model construction and the self-study process of user profile are deeply researched. Aim at the characteristics of the extended manufacturing multi-enterprises environment, a business process knowledge requirement model construction method is presented, and an ontology partition algorithm based on Tabu and Genetic Algorithm is designed for the ontology partition process in the method. According to the requirements of user profile self-study in the multi-enterprises environment of the extended manufacturing, a user profile self-study method based on ontology merge is given.
Lastly, combining with the application background of a national defense special project, the initial realization of the ontology-based extended manufacturing proactive knowledge service system is presented, which provides an effectual method to solve issues existed in the knowledge use in the multi-enterprises environment of the extended manufacturing.
引文
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[2] Andrade J., Ares J., Garcia R., et al.. Lessons learned for the knowledge management systems development. Proceedings of the 2003 IEEE International Conference on Information Reuse and Integration, IEEE, New York, 2003: 471~476.
[3]董颖.知识服务机制研究,[博士学位论文].北京:中国科学院研究生院,2003.
[4]任俊为.知识经济与图书馆的知识服务.图书情报知识,1999,12(1):27~29.
[5]姜永常.论知识服务与信息服务.情报学报,2001,20(5):572~578.
[6] N. F. Abernethy, R.B. Altman. SOPHIA: Providing basic knowledge services with a common DBMS. Proceedings of the 5th KRDB Workshop, Seattle WA, ACM, New York, 1998: 1~6.
[7] L. Chen, S.J. Cox, C Goble, et al.. Knowledge Services for Distributed Service Integration. Proceedings of UK e-Science all-hands meeting, Sheffield, UK, 2003: 2~4.
[8]杨清,游星雅,蒋向红.基于智能信息处理的数字图书馆知识服务系统的研究与设计.计算机工程与科学,2004,26(10):11~14.
[9]曾铮.基于语义网技术构造知识服务系统.情报学报,2005,24(3):336~340.
[10]赵勇,吕娜.基于Ontology的专利知识服务系统研究.现代情报,2005,25(3):93~95.
[11] Giorgos PapaVassiliou, Spyridon Ntioudis. Business process knowledge modeling : method and tool. Proceedings of the 13th International Workshop on Database and Expert Systems , Provence, France, Springer , 2002: 138~142.
[12] P.W.H. Chung, L. Cheung, J. Stader, et al.. Knowledge-based process management-an approach to handling adaptive workflow. Knowledge-Based Systems. 2003 16(3): 149~161.
[13] Markus Strohmaier. Designing Business Process Oriented Knowledge Infrastructures. Available from: http://km.aifb.uni-karlsruhe.de/ws/LLWA/fgml/fgwm/Resources, 2003.
[14] Igor Hawryszliewycz, Win Maung. A Method for Integrating Knowledge Management into Business Processes. Decision Support in an Uncertain and Complex World: The IFIP TC8/WG8.3 International Conference, PRATO, TUSCANY, Italy, 2004: 330~338.
[15]杨涛,王云莉,肖田元,等.个性化主动设计知识服务系统研究.计算机集成制造系统-CIMS,2002,8(12):950~959.
[16]张磊,谢强,王金栋,等.基于知识需求的主动式知识系统.武汉大学学报,2006,39(5):125~130.
[17]张磊,谢强,王金栋,等.基于业务过程的知识需求.吉林大学学报,2005,23(6):679~684.
[18]邓志宏,唐世渭,张铭,等. Ontology研究综述.北京大学学报(自然科学版),2002,38(5):730~738.
[19] H.Wache, T.Vogele, U.Visser, et al.. Ontology-based integration of information-A Survey of Existing approaches. Proceedings of IJCAI-01 workshop: Ontology and information Sharing, Seattle, WA, 2001: 108~117.
[20] Natalya F. Noy. Semantic Integration: A Survey of Ontology Based Approaches. SIGMOD Record, 2004, 33(4): 65~70.
[21] F. Giunchiglia, P. Shvaiko. Semantic matching. The Knowledge Engineering Review Journal, 2003, 18(3): 265~280.
[22] F. Giunchiglia, P. Shvaiko, M. Yatskevich. S-match: an algorithm and an implementation of semantic matching. Proceedings of ESWS, Schloss Dagstuhl, Germany, 2005: 61~75.
[23] HongHai Do, Erhard Rahm. COMA-A system for flexible combination of schema matching approaches. Proceedings of VLDB Conference, Hong Kong, China, 2002: 610~621.
[24] David Aumueller, HongHai Do, Erhard Rahm, et al.. Schema and ontology matching with COMA++. Proceedings of the 2005 ACM SIGMOD international conference on Management of data, Baltimore, Maryland, 2005: 906~908.
[25] Mare Ehrig, York Sure. Ontology mapping-an integrated approach. Proceedings of ESWS, Springer, Berlin, Germany, 2004: 76~91.
[26] Jayant MadhaVan, Philip Bernstein, Erhard Rahm. Generic schema matching with cupid. Proceedings of 27th VLDB Conference, Roma, Italy, 2001: 49~58.
[27] Sergey Melnik, Hector Garcia-Molina, Erhard Rahm. Similarity Flooding: A Versatile Graph Matching Algorithm and its Application to Schema Matching. Proc. 18th ICDE, San Jose CA, 2002: 232~240.
[28] AnHai Doan, Jayant MadhaVan, Pedro Domingos, et al.. Ontology Matching: A Machine Learning Approach. Handbook on Ontologies in Information Systems, Springer-Velag, 2003: 397~416.
[29]陈浩.企业知识与业务流程集成建模研究,[硕士学位论文].杭州:浙江大学,2005.
[30]卞建萍.基于本体和工作流的主动式知识服务系统,[硕士学位论文].南京:南京航空航天大学,2007.
[31]刘文斌,谢强,张磊.多本体中子本体抽取技术的研究.计算机应用研究,2006,23(3):35~40.
[32]谢强,张磊.基于任务类知识需求模板和用户模型的知识需求研究.武汉大学学报(工学版),2006,39(2): 36~41.
[33] Elaine Rich. User modeling via stereotypes. Cognitive Science, 1979, 3(4): 329~354.
[34] Ingo Schwab, Alfred Kobsa, Ivan Koychev. Learning about User from Observation. Proceeding of AAAI Spring Symposium on Adaptive User Interface, Melno Park, CA, AAAI Press, 2000: 201~222.
[35] Gediminas Adomavicius, Tuzhilin. Using data mining methods to build customer profiles. Computer, 2001, 34(2): 74~82.
[36]汪晓岩,胡庆生.面向Internet的个性化智能信息检索.计算机研究与发展,1999,36(9): 1039~1046.
[37]应晓敏.面向Internet个性化服务的用户建模技术研究,[博士学位论文].长沙:国防科学技术大学,2003.
[38]杨涛,王云莉,肖田元,等.主动设计知识服务系统中的用户建模研究.系统仿真学报,2003,15(2): 155~158.
[39]吴丽花,刘鲁.个性化推荐系统用户建模技术综述.情报学报,2006,25(1): 55~62.
[40] Liren Chen, Katia Sycara. WebMate: a personal agent for browsing and searching. Proceedings of the second international conference on Autonomous agents, Minneapolis, Minnesota, United States, ACM Press, 1998: 132~139.
[41] Marko Balabanovi?, Yoav Shoham. Fab: content-based collaborative recommendation. Communications of the ACM, 1997, 40(3): 66~72.
[42]张磊.基于本体的主动式知识系统及其若干关键技术研究,[博士学位论文].南京:南京航空航天大学,2006.
[43] Mehul Bhatt, Andrew Flahive, Carlo Wouters, et al.. A Distributed Approach to Sub-Ontology Extraction. Proceedings of the 18th International Conference on Advanced Information Networking and Applications, IEEE, Washington, DC, USA, 2004: 636~639.
[44] Natalya F.Noy, Mark A.Musen. Specifying ontology views by traversal. International Semantic Web Conference, Springer, Berlin Heidelberg, 2004: 713~725.
[45] Julian Seidenberg, Alan Rector. Web ontology segmentation: analysis, classification and use. In Proceedings of the 15th International Conference on World Wide Web, Edinburgh, Scotland, 2006: 13~22.
[46] Fred Glover, James P.Kelly, Manuel Laguna. Genetic algorithms and Tabu search: Hybrids foroptimization. Computers Ops Res, 1995, 22(1): 111~134.
[47] ChuanKang Ting, ShengTun Li, Chungnan Lee. A Novel Hybrid Optimization Algorithm Based on Genetic Algorithm and Tabu Search. Proceedings of International Computer Symposium, Poland, 2000: 157~162.
[48] ChuanKang Ting, ShengTun Li, Chungnan Lee. TGA: A New Integrated Approach to Evolutionary Algorithms. Proceedings of the 2001 Congress on Evolutionary Computation (CEC2001), Seoul, South Korea, IEEE Press, 2001: 917~924.
[49]谢强,张磊,周良.基于改进粒子群优化算法的Ontology划分方法.华南理工大学学报(自然科学版),2007,35(9):118~122.
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