智慧城市信息系统关键技术研究
|
摘要
城市作为人类的聚集中心和交易中心,是人类文明进步的标志。城市化是城市发展的必然趋势,也是一个社会问题不断涌现的过程：低效的城市管理方式、拥堵的交通系统、难以发挥实效的城市应急系统、远不完善的环境监测体系等等。数字城市以全球定位系统GPS、地理信息系统GIS、遥感RS、计算机、网络通信等技术手段来采集、分析和处理各类城市管理问题,利用网格化管理和城市部件管理法,在城市治安、交通、环卫、规划等方面的应用取得明显成效,也为以城市可持续发展为目标的智慧城市模式提供了动力。作为智慧城市的信息系统必须拥有强大的计算能力、感知能力和数据应用能力。云计算、物联网和语义网三大技术的发展为智慧城市建设提供了可行性,对云计算、物联网和语义网的应用基础和关键技术研究具有重要的理论意义和实用价值。
本文以武汉市智慧城市及其重点工程市行政服务中心的需求、规划、实施、运营为课题背景,对城市数据中心的云计算资源调度、城市感知网的架构与中间件设计和城市关联数据的组织与应用三个方面的关键技术进行了深入研究。在分析智慧城市信息系统需求的基础上,首先建立基于演化算法的云计算虚拟资源调度模型,然后提出城市感知网的技术体系与实现方法,最后提出针对海量感知数据的关联政府数据组织与应用方法。论文研究的理论和关键技术对于智慧城市信息系统的规划、设计、实施和评价具有重要的理论指导意义,对未来提升城市公共服务水平、确保城市可持续发展具有重大的应用价值。
本文在关键技术与应用方案层面,具有如下的贡献和创新。
(1)基于多目标演化算法的云计算虚拟机资源分配方法
针对数据中心云计算资源调度模型、策略及目标,提出了虚拟机资源的初次分配与自适应动态调度方法；根据虚拟机的多维向量特征提出了基于多目标演化算法的资源虚拟机分配算法；给出了根据性能向量的综合负载能力评估策略、基于历史负载数据的触发迁移策略、基于迁移开销的定位策略实现、基于最佳匹配的迁移物理目标机选择策略。在保证应用服务性能的基础上,能更有效地降低虚拟机迁移次数,减轻因迁移对云计算中心整体性能的影响,同时更大程度地减少启用物理服务器数量,提高服务器资源的有效利用率,达到更佳的节能效果。
(2)面向泛在服务的城市感知网技术体系与中间件架构
智慧城市汇聚异构、多维、多时相和海量的泛在信息,需要构建统一的数据接入与汇集机制。本文以物联网的计算框架为基础,提出了满足城市感知网的数据采集、传输与计算服务的技术体系；根据物联网网关的功能需求,提出了软件系统和硬件系统构建方法；提出了符合M2M需求的城市感知网中间件架构,即可以实现感知资源的动态管理,也支持通过HTTP协议提供泛在服务。这对于解决器、控制器和计算终端的多样性条件下提供智能服务具有重要意义。
(3)关联政府数据组织的系统架构和数据处理技术
关联语义数据广泛应用于语义搜索和个性化推荐等智能服务。运用关联数据技术发布的资源对象,具有可共享、可重用、结构化和规范化的特性。在对关联政府数据组织的系统架构、方法技术进行深入分析的基础上,提出了一种由数据转换、数据加工和关联、数据存储及索引、数据应用及服务层组成的关联数据组织4层架构。分别对各层的组成模块和主要功能进行了详细描述,给出了相应的算法和分析。重点阐述了文本、表格等政府开放数据转换为RDF三元组以及进一步加工、关联、存储、索引、查询、呈现的具体方法和技术,通过属性项向关联数据集中本体映射、表元值与实体匹配、联合推断属性项间关系,形成一套自动生成高质量关联数据技术,在政府资源间建立链接,实现跨平台、跨系统查询。以实例说明它们的有效性和可用性,对网络资源语义级共享和关联政府数据组织、知识管理和知识服务科学问题的求解有较大的理论意义和应用价值。
综上所述,本文面向提升智慧城市的运算能力、感知能力和数据应用能力展开研究,提出了基于多目标演化算法的云计算虚拟机分配方法,设计了城市感知网的体系与中间件架构,探究了关联政府数据组织的系统架构和数据处理技术,最后将本文的研究成果应用在武汉市智慧城市和市行政服务中心的项目实践中,验证了理论模型和技术方案的有效性。
As human gathering centers and trade centers, cities are the signs of human civilization progress. Urbanization is an inevitable trend of urban development, The way towards urbanization is coupled with a string of social problems, which is exemplified by the low efficiency of urban management model, crowded traffic system, emergency system without actual effect, and incomplete environmental monitoring system, etc. Digital cities collect, analyse and deal with urban management problems based on various technical means such as GPS, GIS, RS, computers, network communication and so on. Significant results are obtained by implement of grid management and urban component management in urban order, traffic, sanitation and planning. Smart cities'information systems must have enhanced computing power, sensory ability and data application. The development of cloud computing, internet of things and semantic net is the matter of feasibility to the construction of smart cities. The research of cloud computing, Internet of Things and the Semantic Web application infrastructure and key technology has important theoretical significance and practical value.
This article had a depth studied on three key technologies including cloud computing resource schedule of urban data center, framework of urban sensor network and designs of middlewares, and organization and application of urban linked date, which are on the basis of demands, planning, implementation and operation of Wuhan Smart City and its major project municipal administrative service center. On the basis of analyzing smart city information system demand. Firstly, it establishes scheduling mode of cloud computing virtual resource based upon evolutionary algorithm. Secondly, it presents repertoire and implementation method of urban sensor networks. Finally, it states organization and application of government Linked data on face of a mass of sensor data. Researched theory and key technology will, on the one hand, possess import theory significance on planning, designs, implementation and evaluation of smart city information system, and, on the other, have important practical value on improving urban public service and maintaining urban sustainable development in the future.
This article possesses the following contribution and innovation on key technologies and application proposals:
(1) Allocation method of cloud computing virtual machines on the base of multiple-objective evolutionary algorithm
According to the data center cloud computing resources scheduling model, strategy and goal, the article puts forward the first distribution of virtual machine resources and self-adaptive dynamic scheduling method, meanwhile, because of virtual machines with characteristics of multidimensional vector, it proposes allocation algorithm of resource virtual machines based on multi-objective evolutionary algorithm and it comes out comprehensive load capacity evaluation strategy based on performance vector, trigger migration strategy based on the historical load data, positioning strategy implementation based on migration traffic, and selection strategy of migration physical target machines based on the optimum matching. Therefore, with respect to the maintaining of application service, virtual machine migration frequency can be more effectively reduced, as well as the migration's influence on overall performance of the cloud computing center, at the same time, fewer physical servers are equipped, the server resources can be used effectively, and energy saving achieves better effect.
(2) To urban sensor network repertoire and frame works of middlewares under ubiquitous service
Smart city converges isomerism, multiple dimensions and time phase, and a great deal of ubiquitous information, so a unified data access and collection mechanism needs to build. Based on the calculation framework of internet of things, the article puts forward the data acquisition and transmission of urban sensor network, and the repertoire of calculation service. In view of the functional requirements of the internet of things'gateway, the construction method of software system and hardware system is proposed, and the article also presents the middleware structure of urban sensor network as required by M2M, namely, the structure can not only realize dynamic management of sensor resources, but also support ubiquitous service through HTTP protocol. This has important significance in providing intelligent service by solver, controller and computing terminal under diversity conditions.
(3) The organization and application method of Linked government data
Linked semantic data are widely used in semantic search and personalized recommend intelligent service. Resources released by Linked data technology with the characteristics of share, reusability, structurization and normalized are in favor of integrating isolated government data. The article presents a technology automatically generating high quality Linked data through jointly infering attribute items, table element values and the relationship between attribute items. This technology builds links among the same and different fields of government resources, and realize the query across both platform and system. It possess great theoretical significance to network knowledge management and knowledge serving science problem solving and the network resource semantic level Internet sharing has prospect widely application.
To sum up, this article studies how to enhancing smart city operational capability, sensory ability and data application capability in the following three steps.
The first step is to present allocation method of cloud computing virtual machines on the base of multiple-objective evolutionary algorithm. The second step is to establish urban sensor networks and frame works of middlewares. The last step is to research on generation technology and application of Linked government data. Finally, all the above research results are fed into projects of Wuhan smart city and urban administrative service center and they turn out to be valuable both in theoretical model and technical proposal.
本文以武汉市智慧城市及其重点工程市行政服务中心的需求、规划、实施、运营为课题背景,对城市数据中心的云计算资源调度、城市感知网的架构与中间件设计和城市关联数据的组织与应用三个方面的关键技术进行了深入研究。在分析智慧城市信息系统需求的基础上,首先建立基于演化算法的云计算虚拟资源调度模型,然后提出城市感知网的技术体系与实现方法,最后提出针对海量感知数据的关联政府数据组织与应用方法。论文研究的理论和关键技术对于智慧城市信息系统的规划、设计、实施和评价具有重要的理论指导意义,对未来提升城市公共服务水平、确保城市可持续发展具有重大的应用价值。
本文在关键技术与应用方案层面,具有如下的贡献和创新。
(1)基于多目标演化算法的云计算虚拟机资源分配方法
针对数据中心云计算资源调度模型、策略及目标,提出了虚拟机资源的初次分配与自适应动态调度方法；根据虚拟机的多维向量特征提出了基于多目标演化算法的资源虚拟机分配算法；给出了根据性能向量的综合负载能力评估策略、基于历史负载数据的触发迁移策略、基于迁移开销的定位策略实现、基于最佳匹配的迁移物理目标机选择策略。在保证应用服务性能的基础上,能更有效地降低虚拟机迁移次数,减轻因迁移对云计算中心整体性能的影响,同时更大程度地减少启用物理服务器数量,提高服务器资源的有效利用率,达到更佳的节能效果。
(2)面向泛在服务的城市感知网技术体系与中间件架构
智慧城市汇聚异构、多维、多时相和海量的泛在信息,需要构建统一的数据接入与汇集机制。本文以物联网的计算框架为基础,提出了满足城市感知网的数据采集、传输与计算服务的技术体系；根据物联网网关的功能需求,提出了软件系统和硬件系统构建方法；提出了符合M2M需求的城市感知网中间件架构,即可以实现感知资源的动态管理,也支持通过HTTP协议提供泛在服务。这对于解决器、控制器和计算终端的多样性条件下提供智能服务具有重要意义。
(3)关联政府数据组织的系统架构和数据处理技术
关联语义数据广泛应用于语义搜索和个性化推荐等智能服务。运用关联数据技术发布的资源对象,具有可共享、可重用、结构化和规范化的特性。在对关联政府数据组织的系统架构、方法技术进行深入分析的基础上,提出了一种由数据转换、数据加工和关联、数据存储及索引、数据应用及服务层组成的关联数据组织4层架构。分别对各层的组成模块和主要功能进行了详细描述,给出了相应的算法和分析。重点阐述了文本、表格等政府开放数据转换为RDF三元组以及进一步加工、关联、存储、索引、查询、呈现的具体方法和技术,通过属性项向关联数据集中本体映射、表元值与实体匹配、联合推断属性项间关系,形成一套自动生成高质量关联数据技术,在政府资源间建立链接,实现跨平台、跨系统查询。以实例说明它们的有效性和可用性,对网络资源语义级共享和关联政府数据组织、知识管理和知识服务科学问题的求解有较大的理论意义和应用价值。
综上所述,本文面向提升智慧城市的运算能力、感知能力和数据应用能力展开研究,提出了基于多目标演化算法的云计算虚拟机分配方法,设计了城市感知网的体系与中间件架构,探究了关联政府数据组织的系统架构和数据处理技术,最后将本文的研究成果应用在武汉市智慧城市和市行政服务中心的项目实践中,验证了理论模型和技术方案的有效性。
As human gathering centers and trade centers, cities are the signs of human civilization progress. Urbanization is an inevitable trend of urban development, The way towards urbanization is coupled with a string of social problems, which is exemplified by the low efficiency of urban management model, crowded traffic system, emergency system without actual effect, and incomplete environmental monitoring system, etc. Digital cities collect, analyse and deal with urban management problems based on various technical means such as GPS, GIS, RS, computers, network communication and so on. Significant results are obtained by implement of grid management and urban component management in urban order, traffic, sanitation and planning. Smart cities'information systems must have enhanced computing power, sensory ability and data application. The development of cloud computing, internet of things and semantic net is the matter of feasibility to the construction of smart cities. The research of cloud computing, Internet of Things and the Semantic Web application infrastructure and key technology has important theoretical significance and practical value.
This article had a depth studied on three key technologies including cloud computing resource schedule of urban data center, framework of urban sensor network and designs of middlewares, and organization and application of urban linked date, which are on the basis of demands, planning, implementation and operation of Wuhan Smart City and its major project municipal administrative service center. On the basis of analyzing smart city information system demand. Firstly, it establishes scheduling mode of cloud computing virtual resource based upon evolutionary algorithm. Secondly, it presents repertoire and implementation method of urban sensor networks. Finally, it states organization and application of government Linked data on face of a mass of sensor data. Researched theory and key technology will, on the one hand, possess import theory significance on planning, designs, implementation and evaluation of smart city information system, and, on the other, have important practical value on improving urban public service and maintaining urban sustainable development in the future.
This article possesses the following contribution and innovation on key technologies and application proposals:
(1) Allocation method of cloud computing virtual machines on the base of multiple-objective evolutionary algorithm
According to the data center cloud computing resources scheduling model, strategy and goal, the article puts forward the first distribution of virtual machine resources and self-adaptive dynamic scheduling method, meanwhile, because of virtual machines with characteristics of multidimensional vector, it proposes allocation algorithm of resource virtual machines based on multi-objective evolutionary algorithm and it comes out comprehensive load capacity evaluation strategy based on performance vector, trigger migration strategy based on the historical load data, positioning strategy implementation based on migration traffic, and selection strategy of migration physical target machines based on the optimum matching. Therefore, with respect to the maintaining of application service, virtual machine migration frequency can be more effectively reduced, as well as the migration's influence on overall performance of the cloud computing center, at the same time, fewer physical servers are equipped, the server resources can be used effectively, and energy saving achieves better effect.
(2) To urban sensor network repertoire and frame works of middlewares under ubiquitous service
Smart city converges isomerism, multiple dimensions and time phase, and a great deal of ubiquitous information, so a unified data access and collection mechanism needs to build. Based on the calculation framework of internet of things, the article puts forward the data acquisition and transmission of urban sensor network, and the repertoire of calculation service. In view of the functional requirements of the internet of things'gateway, the construction method of software system and hardware system is proposed, and the article also presents the middleware structure of urban sensor network as required by M2M, namely, the structure can not only realize dynamic management of sensor resources, but also support ubiquitous service through HTTP protocol. This has important significance in providing intelligent service by solver, controller and computing terminal under diversity conditions.
(3) The organization and application method of Linked government data
Linked semantic data are widely used in semantic search and personalized recommend intelligent service. Resources released by Linked data technology with the characteristics of share, reusability, structurization and normalized are in favor of integrating isolated government data. The article presents a technology automatically generating high quality Linked data through jointly infering attribute items, table element values and the relationship between attribute items. This technology builds links among the same and different fields of government resources, and realize the query across both platform and system. It possess great theoretical significance to network knowledge management and knowledge serving science problem solving and the network resource semantic level Internet sharing has prospect widely application.
To sum up, this article studies how to enhancing smart city operational capability, sensory ability and data application capability in the following three steps.
The first step is to present allocation method of cloud computing virtual machines on the base of multiple-objective evolutionary algorithm. The second step is to establish urban sensor networks and frame works of middlewares. The last step is to research on generation technology and application of Linked government data. Finally, all the above research results are fed into projects of Wuhan smart city and urban administrative service center and they turn out to be valuable both in theoretical model and technical proposal.
引文
[1]骆小平.“智慧城市”的内涵论析[J],城市管理与科技,2010(12).
[2]巫细波,杨再高.智慧城市理念与未来城市发展[J].城市发展研究,2010,17(11).
[3]张永民,智慧城市总体方案[J],中国信息界,2011(3)
[4]邓贤峰,张晓伟.城市”智慧化”发展的趋势研究[J].电子政务,2011(4).
[5]陈柳钦.智慧城市：全球城市发展新热点[J],青岛科技大学学报(社会科学版),2011(3)
[6]宋丽华,姜家轩,张建成等.黄河三角洲云计算平台关键技术的研究[J],计算机技术与发展,2011(6)
[7]孙茂源,于翔,魏以鹏,数字化城市物联网信息平台设计,物联网技术,2011(10).
[8]张凌云,黎崾,刘敏.智慧旅游的基本概念与理论体系[J],旅游学刊,2012(5)
[9]王家耀,刘嵘,成毅,孙力楠.让城市更智慧[J].测绘科学技术学报,2011,28(2)
[10]张海涛,张永奎.物联网体系架构与核心技术[J],长春工业大学学报(自然科学版),2012(4)
[11]袁媛,高林,王潮阳,董建.物联网与SOA在智慧城市的应用研究[J],信息技术与标准化,2012(7)
[12]陈康,郑纬民.云计算-系统实例与研究现状[J].软件学报.2009(5)
[13]张建勋,古志民,郑超.云计算研究进展综述[J].计算机研究进展综述.2010(2)
[14]Tim Berners-Lee. Linked Data. April 2012 URL, http://www.w3.org/DesignIssues/LinkedData.html.
[15]潘有能,张悦.关联数据研究与应用进展[J].情报科学.2011(1)
[16]孙坦.基于LinkedData的RDF关联框架综析[J]_现代图书情报技术.2012(12)
[17]Urban Computing [EB/OL].[2012-9-19]. http://research.microsoft.com/en-us/projects/ urban-computing.
[18]Liu, S. et. al., Towards mobi lity-based clustering,Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining,2010,919-928
[19]Balan, R.K. and Nguyen, K.X. and Jiang, L., Real-time trip information service for a large taxi fleet, Proceedings of the 9th International Conference on Mobile Systems, Applications, and Services,2011, 99-112.
[20]Yuan, J. and Zheng, Y. and Xie, X. and Sun, G., Driving with knowledge from the physical world, Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining,2011,316-324.
[21]Yuan, J. et. al., T-drive:driving directions based on taxi trajectories, Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems,2010, 99-108.
[22]Zheng, Y. et. al., Urban computing with taxicabs,Proceedings of the 13th International Conference onUbiquitous Computing,2011,89-98.
[23]吴余龙,艾浩军.智慧城市：物联网背景下的现代城市建设之道[M],电子工业出版社,2011(10).
[24]赵艳玲,李战宝.云计算及其安全在美国的发展研究[J],信息网络安全,2011(10)
[25]工业和信息化部电信研究院,政府在云计算发展中的作用[J],数据通信,2012(8)
[26]张贤明,NASA火星漫游车项目使用云计算[J],中国航天,2011(5)
[27]武传坤,物联网安全架构初探[J],战略与决策研究,2010,25(4)
[28]_王忠,美国推动大数据技术发展的战略价值及启示[J],中国发展观察,2012(6)
[29]钱国富,基于关联数据的政府数据发布[J],《图书情报工作》,2012(3).
[30]吴妥,开放数据在英美政府中的应用及启示[J],图书与情报,2012(1).
[31]Nigel Shadbolt, Kieron O'Hara, Tim Berners-Lee, Nicholas Gibbins, Hugh Glaser, Wendy Hall, M.C. Schraefel, Open Government Data and the Linked Data Web:Lessons from data.gov.uk, IEEE Intelligent Systems,08 March 2012. PrePrint.
[32]DMTF. Open virtualization format specification, DSP0243[S].Portland,OR:DMTF,2009.
[33]LI Qiang, HAO Qin-Fen et al. Adaptive Management and Multi-Objective Optimization for Virtual Machine Placement in Cloud Computing [J]. Chinese Journal of Computers. 2011(12), Vol.34.
[34]杨星,马自堂,孙磊.云环境下基于性能向量的虚拟机部署算法[J],计算机应用,2012(1)：16-19.
[35]Falkenauer E, Delchambre A. A genetic algorithm for Bin Packing and line balancing. Proceedings of the IEEE International Conference on Robotics and Automation.1992: 1186-1192.
[36]Ozgur Ulker et al. A Grouping Genetic Algorithm Using Linear Linkage Encoding for Bin Packing. Lecture Notes in Computer Science,2008, Volume 5199,1140-1149.
[37]Michael Emmerich et al. An EMO Algorithm Using the Hypervolume Measure as Selection Criterion. Lecture Notes in Computer Science, Volume 3410/2005,62-76.
[38]方锦明,云计算中基于NSGA-II的虚拟资源调度算法[J],计算机工程与设计,2012(4)vo1.33.
[39]Nicola Beume et al. SMS-EMOA:Multi-objective selection based on dominated hypervolume. European Journal of Operational Research (2007) 1653-1669.
[40]刘鹏程,陈榕.面向云计算的虚拟机动态迁移框架[J].计算_工程,2010,36(5)：37-39.
[41]钱琼芬,李春林,张小庆,李腊元.云数据中心虚拟资源管理研究综述[J],计算机应用研究,2012(7).
[42]CALHEIROS R N, RANJAN R, De ROSE CAT, et al. CloudSim:A novel framework for modeling and simulation of cloud computing infrastructures and services, GRIDS-TR-2009-1[R]. Parkville, VIC:The University of Melbourne Australia, Grid Computing and Distributed Systems Laboratory, 2009.
[43]Uckelmann, Dieter; Harrison, Mark; Michahelles, Florian, An Architectural Approach Towards the Future Internet of Things, Architecting the Internet of Things,2011, l-24,Springer
[44]Roch H. Glitho, Application Architectures for Machine to Machine Communications:Research Agenda vs. State-Of-The Art, Proceedings of the 6th International Conference on Broadband Communications & Biomedical Applications, November 21-24,2011, Melbourne, Australia, pp:1-5
[45]Uckelmann, Dieter; Harrison, Mark; Michahelles, Florian (Eds.), Architecting the Internet of Things, 1st Edition.,2011, XXXI,351 p.77 illus.
[46]Rajesh Karunamurthy, Ferhat Khendek, Roch H. Glitho, A novel architecture for Web service composition, Journal of Network and Computer Applications, Volume 35, Issue 2, March 2012, Pages 787-802
[47]Harshal Patni, Cory Henson and Amit Sheth, Linked Sensor Data, in Proceedings of 2010 International Symposium on Collaborative Technologies and Systems (CTS 2010), Chicago, IL, May 17-21,2010.
[48]Qian Zhu, Ruicong Wang, Qi Chen, Yan Liu and Weijun Qin, IOT Gateway:BridgingWireless Sensor Networks into Internet of Things, in 2010 IEEE/IFIP International Conference on Embedded and Ubiquitous Computing, pp347-352
[49]Luis M. Alvarez Sabucedo, Luis E. Anido Rifon, Ruben Miguez Perez, Juan M. Santos Gago, Providing standard-oriented data models and interfaces to eGovernment services:A semantic-driven approach, Computer Standards & amp; Interfaces, Volume 31, Issue 5, September 2009, Pages 1014-1027
[50]丁博,王怀民,史殿习,普适计算中间件技术,计算机科学与探索,2007年第3期,pp241-254
[51]Wen Yingyou, Li Zhi, Peng Xuena, Zhao Hong, A Middleware Architecture for Sensor Networks Applied to Industry Solutions of Internet of Things, in 2011 Second International Conference on Digital Manufacturing & Automation, pp50-54
[52]Gabriella Castelli, Alberto Rosi, Franco Zambonelli, Design and Implementation of a Socially-Enhanced Pervasive Middleware,2012 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops),, March 2012, Page(s):137-142
[53]Gabriella Castelli, Marco Mamei, Franco Zambonelli, The changing role of pervasive middleware: From discovery and orchestration to recommendation and planning, IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops), March 2011, Page(s): 214-219
[54]D. Guinard, V. Trifa, F. Mattern, and E. Wilde. From the Internet of Things to the Web of Things: Resource Oriented Architecture and Best Practices. In D. Uckelmann, M. Harrison, and F. Michahelles, editors, Architecting the Internet of Things, chapter 5. Springer,2011.
[55]http://www.w3.org/wiki/SweoIG/TaskForces/CommunityProjects/LinkingOpenData
[56]Christian Bizer, Tom Heath,Tim Berners-Lee,Michael Hausenblas DERI.4th Linked Data on the Web Workshop (LDOW2011). Proceeding WWW'11 Proceedings of the 20th international conference companion on World Wide Web ACM New York, NY, US A,2011.
[57]Nigel Shadbolt, Kieron O'Hara, Tim Berners-Lee, Nicholas Gibbins, Hugh Glaser, Wendy Hall, M.C. Schraefel, Open Government Data and the Linked Data Web:Lessons from data.gov.uk, IEEE Intelligent Systems,08 March 2012. PrePrint.
[58]Dieter Fensel, Federico Michele Facca, Elena Simperl and loan Toma. Semantic Web[EB]. Semantic Web Services 2011, Part 1,87-104.
[59]P. Buitelaar, P. Cimiano, and B. Magnini (Eds.). Ontology Learning from Text:Methods, Evaluation and Applications, Series information for Frontiers in Artificial Intelligence and Applications, IOS Press,2005.
[60]Wong, W. (2009), "Learning Lightweight Ontologies from Text across Different Domains using the Web as Background Knowledge". Doctor of Philosophy thesis, University of Western Australia.
[61]Wong, W., Liu, W.& Bennamoun, M. (In Press), Ontology Learning from Text:A Look back and into the Future. To appear in ACM Computing Surveys.
[62]VARGAS-VERA M,MOTTA E,DOMINGUE J,SHUM S B,LANZONI M.Knowledge, Extraction by Using An Ontology-based Annotation Tool[C]. Proceedings of the 1st International Conference on Knowledge Capture workshop on knowledge markup and semantic annotation, Victoria, BC, Canada, 2001,5-12.
[63]VARGAS-VERA M,MOTTA E,DOMINGUE J,LANZONI M,STUTT A,CIRAVEGNA F. Mnm: Ontology Driven Semi-Automatic and Automatic Support for Semantic Markup[C].Proceedings of the 13th International Conference on Knowledge Engineering and Management, Victoria, BC, Canada. 2001,5-12.
[64]BUITELAAR P,RAMAKA S.Unsupervised Ontology-based Semantic Tagging for Knowledge Markup, in:W.Buntine,A.Hotho,S.Bloehdorn (Eds.)[C]. [11] KOGUT P,HOLMES W.AeroDAML:Apply ing Information Extraction to Generate DAML Annotations from Web Pages[C].Proceedings of the Workshop on Knowledge Markup and Semantic Annotation at the 1 st International Conference on Knowledge Capture,2001.
[65]Selmer Bringsjord. Psychometric artificial intelligence[J], Journal of Experimental& Theoretical Artificial Intelligence Special Issue:Psychometric Artificial Intelligence Volume 23, Issue 3,2011.
[66]Dilek Kuquka, Adnan Yazici. Exploiting information extraction techniques for automatic semantic video indexing with an application to Turkish news videos. Knowledge-Based Systems Volume 24, Issue 6, August 2011, Pages 844-857.
[67]Scratch. Roberto Navigli, Paola Velardi and Stefano Faralli. A Graph-based Algorithm for Inducing Lexical Taxonomies from Scratch. Proc. of the 22nd International Joint Conference on Artificial Intelligence (IJCAI 2011)
[68]Leland Wilkinson, Anushka Anand, Dang Tuan Nhon:CHIRP:a new classifier based on composite hypercubes on iterated random projections. Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Diego, CA, USA, August 21-24,2011. Pages:6-14.
[69]SVAB O,Labsky M,Svatek VRDF-based Retrieval of Information Extracted from Web Product Catalogues[C],Proceedings of the 6th International Conference on Enterprise Information System,Porto,Portugal,2004.
[70]杨柳,胡志刚,龙军,郭涛.专家领域本体建模及语义信息服务研究[J],小型微型计算机系统, 2012(8).
[71]王硕,周华琳.基于语义搜索引擎的数字图书馆服务优化研究[J],图书馆学研究,2012(7)
[72]牟向伟.模糊语义个性化推荐系统在电子政务中的应用研究[D],大连海事大学博_士论文大连海事大学2010(9)
[73]http://www.w3.org/RDF.
[74]http://www.w3.org/TR/rdf-schema/.
[75]http://www.w3.org/TR/owl-features/.
[76]Gu Yunhua, Research on RDF Query Using SPARQL Language[C], Proceedings of 2010 International Conference on Services Science, Management and Engineering(Volume 1).
[77]SCHMIDT M, MEIER M, LAUSEN G. Foundations of SPARQL Query Optimization [J]. The Computing Research Repository, ACM,2008, abs/0812.3788
[78]HARTING O. Querying Trust in RDF Data with SPARQL[C]. Proceedings of the 6th European Semantic Web Conference (ESWC 09). Berlin:Springer,2009, pp:5-20
[79]Bastian Quilitz and Ulf Leser, Querying Distributed RDF Data Sources with SPARQL [J],Lecture Notes in Computer Science,2008, Volume 5021/2008,524-538.
[80]Fadi Maalil, Richard Cyganiak, Vassilios Peristeras. A Publishing Pipeline for Linked Government Data [J], ESWC 2012, LNCS 7295, pp.778-792,2012
[81]http://www.data.gov. April 2012.
[82]http://www.data.gov.uk. April 2012.
[83]John Sheridan, Jeni Tennison. Linking UK Government Data[M].London:LDOW Press,2010.
[84]Anders Sderbck, Martin Malsten. Libris. Linked Library Data. Nodalities,2008(5):19-20
[85]Xing Niu, Xinruo Sun, Haofen Wang, Shu Rong, Guilin Qi and Yong Yu. Zhishi.me-Weaving Chinese Linking Open Data, The Semantic Web-ISWC 2011 http://zhishi.me/lookup/
[86]http://linkeddata.org/
[87]Varish Mulwad et al., "Automatically Generating Government Linked Data from Tables", InCollection, Working notes of AAAI Fall Symposium on Open Government Knowledge:AI Opportunities and Challenges, November 2011.
[88]Fadi Maali, Richard Cyganiak, Vassilios Peristeras. Enabling Interoperability of Government Data Catalogues[J], Electronic Government,2010, Lecture Notes in Computer Science, Volume 6228.
[89]Li Ding, Timothy Lebo,etc.TWC LOGD:A portal for linked open government data ecosystems[J],Web Semantics:Science, Services and Agents on the World Wide Web 9 (2011) 325-333.
[90]Li Ding, Dominic DiFranzo,etc. The Data-gov Wiki:A Semantic Web Portal for Linked Government Data[J],JOURNAL OF WEB SEMANTICS2011(3)vol.9 P:325-333.
[91]叶育鑫,语义Web下的知识搜索及其核心技术[D],吉林大学,2010
[92]韦福如.基于图模型多文档自动文摘研究.武汉大学博士学位论文.2009.
[93]刘晓华；韦福如；段亚娟；周明。基于语义分析的微博搜索。山东大学学报(理学版)。2012.3.
[94]黄智生,潘志霖,漆桂林.语义万维网中的大规模推理.中国计算机学会通讯.2010年第6卷,第8期,P：9-25.
[95]李亚楠,王斌,李锦涛,李鹏.给互联网建立索引：基于词关系网络的智能查询推荐.软件学报.2011年8期.
[96]蒲强,何大庆,杨国纬.一种基于统计语义聚类的查询语言模型估计.计算机研究与发展.2011年2期
[97]欧伟杰,曾承,项小明,彭智勇,李德毅.基于概念松弛的高效Web服务查询方法.计算机学报.2011年12期.
[98]姜吉发,王树西.一种自举的二元关系和二元关系模式获取方法.中文信息学报.2005,19(2)：71-77
[99]陈波.2011.基于特征结构的汉语主谓谓语句标注.中国计算语言学研究前沿进展.
[100]王丹,姬东鸿,黄玮.2011.一种基于MIRA和遗传算法的句法分析模型构造方法.全国第九届计算语言学学术会议(CNCCL-2011),洛阳.
[101]刘德喜何炎祥姬东鸿杨华.基于基本要素向量空间的英文多文档自动摘要.计算机工程,2007,33(14)：166-168.
[102]刘茂福,李文捷,姬东鸿,基于事件项语义图聚类的多文档摘要方法,中文信息学报,2010,(05).
[103]JentzschA.DBpedia-Extracting Structured Data from Wikipedia[EB/OL]. [2012-10-9]. http: //wikimania2009.wikimedia.org/wiki/Proceedings:174.
[104]GeorgiK.DBpedia-A Linked DataHub and Data Source for Web and Enterprise Applications [EB/OL]. [2012-10-9].http://wtlab.um.ac.ir/parameters/wtlab/filemanager/LD_resources/other/ DBpedia-WWW2009-DevTrack-Abstract.pdf.
[105]Triplify.org:Overview[EB/OL].[2012-09-02].http://triplify.org/.
[106]Sem Wiki.http://en.wikipedia.org/wiki/Search_engine_marketing
[2]巫细波,杨再高.智慧城市理念与未来城市发展[J].城市发展研究,2010,17(11).
[3]张永民,智慧城市总体方案[J],中国信息界,2011(3)
[4]邓贤峰,张晓伟.城市”智慧化”发展的趋势研究[J].电子政务,2011(4).
[5]陈柳钦.智慧城市：全球城市发展新热点[J],青岛科技大学学报(社会科学版),2011(3)
[6]宋丽华,姜家轩,张建成等.黄河三角洲云计算平台关键技术的研究[J],计算机技术与发展,2011(6)
[7]孙茂源,于翔,魏以鹏,数字化城市物联网信息平台设计,物联网技术,2011(10).
[8]张凌云,黎崾,刘敏.智慧旅游的基本概念与理论体系[J],旅游学刊,2012(5)
[9]王家耀,刘嵘,成毅,孙力楠.让城市更智慧[J].测绘科学技术学报,2011,28(2)
[10]张海涛,张永奎.物联网体系架构与核心技术[J],长春工业大学学报(自然科学版),2012(4)
[11]袁媛,高林,王潮阳,董建.物联网与SOA在智慧城市的应用研究[J],信息技术与标准化,2012(7)
[12]陈康,郑纬民.云计算-系统实例与研究现状[J].软件学报.2009(5)
[13]张建勋,古志民,郑超.云计算研究进展综述[J].计算机研究进展综述.2010(2)
[14]Tim Berners-Lee. Linked Data. April 2012 URL, http://www.w3.org/DesignIssues/LinkedData.html.
[15]潘有能,张悦.关联数据研究与应用进展[J].情报科学.2011(1)
[16]孙坦.基于LinkedData的RDF关联框架综析[J]_现代图书情报技术.2012(12)
[17]Urban Computing [EB/OL].[2012-9-19]. http://research.microsoft.com/en-us/projects/ urban-computing.
[18]Liu, S. et. al., Towards mobi lity-based clustering,Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining,2010,919-928
[19]Balan, R.K. and Nguyen, K.X. and Jiang, L., Real-time trip information service for a large taxi fleet, Proceedings of the 9th International Conference on Mobile Systems, Applications, and Services,2011, 99-112.
[20]Yuan, J. and Zheng, Y. and Xie, X. and Sun, G., Driving with knowledge from the physical world, Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining,2011,316-324.
[21]Yuan, J. et. al., T-drive:driving directions based on taxi trajectories, Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems,2010, 99-108.
[22]Zheng, Y. et. al., Urban computing with taxicabs,Proceedings of the 13th International Conference onUbiquitous Computing,2011,89-98.
[23]吴余龙,艾浩军.智慧城市：物联网背景下的现代城市建设之道[M],电子工业出版社,2011(10).
[24]赵艳玲,李战宝.云计算及其安全在美国的发展研究[J],信息网络安全,2011(10)
[25]工业和信息化部电信研究院,政府在云计算发展中的作用[J],数据通信,2012(8)
[26]张贤明,NASA火星漫游车项目使用云计算[J],中国航天,2011(5)
[27]武传坤,物联网安全架构初探[J],战略与决策研究,2010,25(4)
[28]_王忠,美国推动大数据技术发展的战略价值及启示[J],中国发展观察,2012(6)
[29]钱国富,基于关联数据的政府数据发布[J],《图书情报工作》,2012(3).
[30]吴妥,开放数据在英美政府中的应用及启示[J],图书与情报,2012(1).
[31]Nigel Shadbolt, Kieron O'Hara, Tim Berners-Lee, Nicholas Gibbins, Hugh Glaser, Wendy Hall, M.C. Schraefel, Open Government Data and the Linked Data Web:Lessons from data.gov.uk, IEEE Intelligent Systems,08 March 2012. PrePrint.
[32]DMTF. Open virtualization format specification, DSP0243[S].Portland,OR:DMTF,2009.
[33]LI Qiang, HAO Qin-Fen et al. Adaptive Management and Multi-Objective Optimization for Virtual Machine Placement in Cloud Computing [J]. Chinese Journal of Computers. 2011(12), Vol.34.
[34]杨星,马自堂,孙磊.云环境下基于性能向量的虚拟机部署算法[J],计算机应用,2012(1)：16-19.
[35]Falkenauer E, Delchambre A. A genetic algorithm for Bin Packing and line balancing. Proceedings of the IEEE International Conference on Robotics and Automation.1992: 1186-1192.
[36]Ozgur Ulker et al. A Grouping Genetic Algorithm Using Linear Linkage Encoding for Bin Packing. Lecture Notes in Computer Science,2008, Volume 5199,1140-1149.
[37]Michael Emmerich et al. An EMO Algorithm Using the Hypervolume Measure as Selection Criterion. Lecture Notes in Computer Science, Volume 3410/2005,62-76.
[38]方锦明,云计算中基于NSGA-II的虚拟资源调度算法[J],计算机工程与设计,2012(4)vo1.33.
[39]Nicola Beume et al. SMS-EMOA:Multi-objective selection based on dominated hypervolume. European Journal of Operational Research (2007) 1653-1669.
[40]刘鹏程,陈榕.面向云计算的虚拟机动态迁移框架[J].计算_工程,2010,36(5)：37-39.
[41]钱琼芬,李春林,张小庆,李腊元.云数据中心虚拟资源管理研究综述[J],计算机应用研究,2012(7).
[42]CALHEIROS R N, RANJAN R, De ROSE CAT, et al. CloudSim:A novel framework for modeling and simulation of cloud computing infrastructures and services, GRIDS-TR-2009-1[R]. Parkville, VIC:The University of Melbourne Australia, Grid Computing and Distributed Systems Laboratory, 2009.
[43]Uckelmann, Dieter; Harrison, Mark; Michahelles, Florian, An Architectural Approach Towards the Future Internet of Things, Architecting the Internet of Things,2011, l-24,Springer
[44]Roch H. Glitho, Application Architectures for Machine to Machine Communications:Research Agenda vs. State-Of-The Art, Proceedings of the 6th International Conference on Broadband Communications & Biomedical Applications, November 21-24,2011, Melbourne, Australia, pp:1-5
[45]Uckelmann, Dieter; Harrison, Mark; Michahelles, Florian (Eds.), Architecting the Internet of Things, 1st Edition.,2011, XXXI,351 p.77 illus.
[46]Rajesh Karunamurthy, Ferhat Khendek, Roch H. Glitho, A novel architecture for Web service composition, Journal of Network and Computer Applications, Volume 35, Issue 2, March 2012, Pages 787-802
[47]Harshal Patni, Cory Henson and Amit Sheth, Linked Sensor Data, in Proceedings of 2010 International Symposium on Collaborative Technologies and Systems (CTS 2010), Chicago, IL, May 17-21,2010.
[48]Qian Zhu, Ruicong Wang, Qi Chen, Yan Liu and Weijun Qin, IOT Gateway:BridgingWireless Sensor Networks into Internet of Things, in 2010 IEEE/IFIP International Conference on Embedded and Ubiquitous Computing, pp347-352
[49]Luis M. Alvarez Sabucedo, Luis E. Anido Rifon, Ruben Miguez Perez, Juan M. Santos Gago, Providing standard-oriented data models and interfaces to eGovernment services:A semantic-driven approach, Computer Standards & amp; Interfaces, Volume 31, Issue 5, September 2009, Pages 1014-1027
[50]丁博,王怀民,史殿习,普适计算中间件技术,计算机科学与探索,2007年第3期,pp241-254
[51]Wen Yingyou, Li Zhi, Peng Xuena, Zhao Hong, A Middleware Architecture for Sensor Networks Applied to Industry Solutions of Internet of Things, in 2011 Second International Conference on Digital Manufacturing & Automation, pp50-54
[52]Gabriella Castelli, Alberto Rosi, Franco Zambonelli, Design and Implementation of a Socially-Enhanced Pervasive Middleware,2012 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops),, March 2012, Page(s):137-142
[53]Gabriella Castelli, Marco Mamei, Franco Zambonelli, The changing role of pervasive middleware: From discovery and orchestration to recommendation and planning, IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops), March 2011, Page(s): 214-219
[54]D. Guinard, V. Trifa, F. Mattern, and E. Wilde. From the Internet of Things to the Web of Things: Resource Oriented Architecture and Best Practices. In D. Uckelmann, M. Harrison, and F. Michahelles, editors, Architecting the Internet of Things, chapter 5. Springer,2011.
[55]http://www.w3.org/wiki/SweoIG/TaskForces/CommunityProjects/LinkingOpenData
[56]Christian Bizer, Tom Heath,Tim Berners-Lee,Michael Hausenblas DERI.4th Linked Data on the Web Workshop (LDOW2011). Proceeding WWW'11 Proceedings of the 20th international conference companion on World Wide Web ACM New York, NY, US A,2011.
[57]Nigel Shadbolt, Kieron O'Hara, Tim Berners-Lee, Nicholas Gibbins, Hugh Glaser, Wendy Hall, M.C. Schraefel, Open Government Data and the Linked Data Web:Lessons from data.gov.uk, IEEE Intelligent Systems,08 March 2012. PrePrint.
[58]Dieter Fensel, Federico Michele Facca, Elena Simperl and loan Toma. Semantic Web[EB]. Semantic Web Services 2011, Part 1,87-104.
[59]P. Buitelaar, P. Cimiano, and B. Magnini (Eds.). Ontology Learning from Text:Methods, Evaluation and Applications, Series information for Frontiers in Artificial Intelligence and Applications, IOS Press,2005.
[60]Wong, W. (2009), "Learning Lightweight Ontologies from Text across Different Domains using the Web as Background Knowledge". Doctor of Philosophy thesis, University of Western Australia.
[61]Wong, W., Liu, W.& Bennamoun, M. (In Press), Ontology Learning from Text:A Look back and into the Future. To appear in ACM Computing Surveys.
[62]VARGAS-VERA M,MOTTA E,DOMINGUE J,SHUM S B,LANZONI M.Knowledge, Extraction by Using An Ontology-based Annotation Tool[C]. Proceedings of the 1st International Conference on Knowledge Capture workshop on knowledge markup and semantic annotation, Victoria, BC, Canada, 2001,5-12.
[63]VARGAS-VERA M,MOTTA E,DOMINGUE J,LANZONI M,STUTT A,CIRAVEGNA F. Mnm: Ontology Driven Semi-Automatic and Automatic Support for Semantic Markup[C].Proceedings of the 13th International Conference on Knowledge Engineering and Management, Victoria, BC, Canada. 2001,5-12.
[64]BUITELAAR P,RAMAKA S.Unsupervised Ontology-based Semantic Tagging for Knowledge Markup, in:W.Buntine,A.Hotho,S.Bloehdorn (Eds.)[C]. [11] KOGUT P,HOLMES W.AeroDAML:Apply ing Information Extraction to Generate DAML Annotations from Web Pages[C].Proceedings of the Workshop on Knowledge Markup and Semantic Annotation at the 1 st International Conference on Knowledge Capture,2001.
[65]Selmer Bringsjord. Psychometric artificial intelligence[J], Journal of Experimental& Theoretical Artificial Intelligence Special Issue:Psychometric Artificial Intelligence Volume 23, Issue 3,2011.
[66]Dilek Kuquka, Adnan Yazici. Exploiting information extraction techniques for automatic semantic video indexing with an application to Turkish news videos. Knowledge-Based Systems Volume 24, Issue 6, August 2011, Pages 844-857.
[67]Scratch. Roberto Navigli, Paola Velardi and Stefano Faralli. A Graph-based Algorithm for Inducing Lexical Taxonomies from Scratch. Proc. of the 22nd International Joint Conference on Artificial Intelligence (IJCAI 2011)
[68]Leland Wilkinson, Anushka Anand, Dang Tuan Nhon:CHIRP:a new classifier based on composite hypercubes on iterated random projections. Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Diego, CA, USA, August 21-24,2011. Pages:6-14.
[69]SVAB O,Labsky M,Svatek VRDF-based Retrieval of Information Extracted from Web Product Catalogues[C],Proceedings of the 6th International Conference on Enterprise Information System,Porto,Portugal,2004.
[70]杨柳,胡志刚,龙军,郭涛.专家领域本体建模及语义信息服务研究[J],小型微型计算机系统, 2012(8).
[71]王硕,周华琳.基于语义搜索引擎的数字图书馆服务优化研究[J],图书馆学研究,2012(7)
[72]牟向伟.模糊语义个性化推荐系统在电子政务中的应用研究[D],大连海事大学博_士论文大连海事大学2010(9)
[73]http://www.w3.org/RDF.
[74]http://www.w3.org/TR/rdf-schema/.
[75]http://www.w3.org/TR/owl-features/.
[76]Gu Yunhua, Research on RDF Query Using SPARQL Language[C], Proceedings of 2010 International Conference on Services Science, Management and Engineering(Volume 1).
[77]SCHMIDT M, MEIER M, LAUSEN G. Foundations of SPARQL Query Optimization [J]. The Computing Research Repository, ACM,2008, abs/0812.3788
[78]HARTING O. Querying Trust in RDF Data with SPARQL[C]. Proceedings of the 6th European Semantic Web Conference (ESWC 09). Berlin:Springer,2009, pp:5-20
[79]Bastian Quilitz and Ulf Leser, Querying Distributed RDF Data Sources with SPARQL [J],Lecture Notes in Computer Science,2008, Volume 5021/2008,524-538.
[80]Fadi Maalil, Richard Cyganiak, Vassilios Peristeras. A Publishing Pipeline for Linked Government Data [J], ESWC 2012, LNCS 7295, pp.778-792,2012
[81]http://www.data.gov. April 2012.
[82]http://www.data.gov.uk. April 2012.
[83]John Sheridan, Jeni Tennison. Linking UK Government Data[M].London:LDOW Press,2010.
[84]Anders Sderbck, Martin Malsten. Libris. Linked Library Data. Nodalities,2008(5):19-20
[85]Xing Niu, Xinruo Sun, Haofen Wang, Shu Rong, Guilin Qi and Yong Yu. Zhishi.me-Weaving Chinese Linking Open Data, The Semantic Web-ISWC 2011 http://zhishi.me/lookup/
[86]http://linkeddata.org/
[87]Varish Mulwad et al., "Automatically Generating Government Linked Data from Tables", InCollection, Working notes of AAAI Fall Symposium on Open Government Knowledge:AI Opportunities and Challenges, November 2011.
[88]Fadi Maali, Richard Cyganiak, Vassilios Peristeras. Enabling Interoperability of Government Data Catalogues[J], Electronic Government,2010, Lecture Notes in Computer Science, Volume 6228.
[89]Li Ding, Timothy Lebo,etc.TWC LOGD:A portal for linked open government data ecosystems[J],Web Semantics:Science, Services and Agents on the World Wide Web 9 (2011) 325-333.
[90]Li Ding, Dominic DiFranzo,etc. The Data-gov Wiki:A Semantic Web Portal for Linked Government Data[J],JOURNAL OF WEB SEMANTICS2011(3)vol.9 P:325-333.
[91]叶育鑫,语义Web下的知识搜索及其核心技术[D],吉林大学,2010
[92]韦福如.基于图模型多文档自动文摘研究.武汉大学博士学位论文.2009.
[93]刘晓华；韦福如；段亚娟；周明。基于语义分析的微博搜索。山东大学学报(理学版)。2012.3.
[94]黄智生,潘志霖,漆桂林.语义万维网中的大规模推理.中国计算机学会通讯.2010年第6卷,第8期,P：9-25.
[95]李亚楠,王斌,李锦涛,李鹏.给互联网建立索引：基于词关系网络的智能查询推荐.软件学报.2011年8期.
[96]蒲强,何大庆,杨国纬.一种基于统计语义聚类的查询语言模型估计.计算机研究与发展.2011年2期
[97]欧伟杰,曾承,项小明,彭智勇,李德毅.基于概念松弛的高效Web服务查询方法.计算机学报.2011年12期.
[98]姜吉发,王树西.一种自举的二元关系和二元关系模式获取方法.中文信息学报.2005,19(2)：71-77
[99]陈波.2011.基于特征结构的汉语主谓谓语句标注.中国计算语言学研究前沿进展.
[100]王丹,姬东鸿,黄玮.2011.一种基于MIRA和遗传算法的句法分析模型构造方法.全国第九届计算语言学学术会议(CNCCL-2011),洛阳.
[101]刘德喜何炎祥姬东鸿杨华.基于基本要素向量空间的英文多文档自动摘要.计算机工程,2007,33(14)：166-168.
[102]刘茂福,李文捷,姬东鸿,基于事件项语义图聚类的多文档摘要方法,中文信息学报,2010,(05).
[103]JentzschA.DBpedia-Extracting Structured Data from Wikipedia[EB/OL]. [2012-10-9]. http: //wikimania2009.wikimedia.org/wiki/Proceedings:174.
[104]GeorgiK.DBpedia-A Linked DataHub and Data Source for Web and Enterprise Applications [EB/OL]. [2012-10-9].http://wtlab.um.ac.ir/parameters/wtlab/filemanager/LD_resources/other/ DBpedia-WWW2009-DevTrack-Abstract.pdf.
[105]Triplify.org:Overview[EB/OL].[2012-09-02].http://triplify.org/.
[106]Sem Wiki.http://en.wikipedia.org/wiki/Search_engine_marketing
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |