语义Web和语义网格及数据资源整合的探讨

目前,许多电子政务系统面临着一个共同的问题,这就是所谓的＂信息孤岛＂问题,解决该问题的关键是如何将现有数据资源进行有效的整合。而语义网格理论的提出,为这一难题的解决提供了一个新的思路。应用语义网格理论,针对出租汽车管理信息化的应用需求,构造语义网格模型Tx-Grid,并且讨论一种利用本体整合分布式异构数据库资源的机制,有效地解决这一问题。     

语义网格：语义Web与网格计算的融合

本文对近几年Web服务、语义Web及网格计算等一些新兴的技术进行了简要的回顾，并对它们的背景、特点及相互关系进行了分析，在这基础上，介绍了一种新的网格发展趋势——语义网格，对它的背景、目标、体系结构及其知识层进行了详细描述并简要总结了当前语义网格的研完现状。     

语义传感器Web中的数据管理技术研究

语义传感器Web是由传感器网络技术、分布式计算技术、数据库管理技术和语义Web技术整合发展而来的.语义传感器Web能够感知、收集、整合信息,抽取新的知识并为感知器提供增强语义,因此能对环境的变化有更智能的感知,用户可以通过访问Web获取这些信息.作为计算机科学中一个新的研究领域,它有着广阔的应用前景,引起了工业界和学术界浓厚的兴趣.介绍了语义传感器Web的基本概念、特点,并着重讨论了语义传感器Web数据管理中所存在的研究问题、研究现状和研究成果.     

基于本体的网格服务语义匹配方法

针对目前网格系统中,将名字作为关键字对服务进行查找存在的不准确、不灵活的缺陷,提出了一种网格服务的语义匹配方法,在该方法中,用OWL-S对网格服务进行语义上的描述,用UDDI来进行服务的注册和发现,用一个语义服务库存储虚拟组织中所有发布的网格服务功能的语义信息,这个语义服务库对每个领域的服务用树的形式进行组织。通过建立领域本体库为服务功能语义上的匹配提供知识支持。对方法进行了可行性分析。     

Searching and browsing Linked Data with SWSE: The Semantic Web Search Engine

In this paper, we discuss the architecture and implementation of the Semantic Web Search Engine (SWSE). Following traditional search engine architecture, SWSE consists of crawling, data enhancing, indexing and a user interface for search, browsing and retrieval of information; unlike traditional search engines, SWSE operates over RDF Web data – loosely also known as Linked Data – which implies unique challenges for the system design, architecture, algorithms, implementation and user interface. In particular, many challenges exist in adopting Semantic Web technologies for Web data: the unique challenges of the Web – in terms of scale, unreliability, inconsistency and noise – are largely overlooked by the current Semantic Web standards. Herein, we describe the current SWSE system, initially detailing the architecture and later elaborating upon the function, design, implementation and performance of each individual component. In so doing, we also give an insight into how current Semantic Web standards can be tailored, in a best-effort manner, for use on Web data. Throughout, we offer evaluation and complementary argumentation to support our design choices, and also offer discussion on future directions and open research questions. Later, we also provide candid discussion relating to the difficulties currently faced in bringing such a search engine into the mainstream, and lessons learnt from roughly six years working on the Semantic Web Search Engine project.     

Shortipedia aggregating and curating Semantic Web data

Shortipedia is a Web-based knowledge repository, that pulls together a growing number of sources in order to provide a comprehensive, diversified view on entities of interest. Contributors to Shortipedia can easily add claims to the knowledge base, provide sources for their claims, and find links to knowledge already available on the Semantic Web.     

数据网格信息服务体系的研究与设计

网格信息服务是网格中间件的核心部分之一,提供各种分布式网格资源的静态与动态信息。现有的网格中间件几乎都是针对计算网格设计的,它们所提供的信息服务不能胜任数据网格的要求。论文主要对数据网格中的信息服务体系进行研究和设计。     

基于语义Web数据交换平台下的负载平衡研究

通过语义Web中RDF(资源描述框架)的包容器和本体的资源整合,将请求的相同任务形成一种任务池,通过任务池将请求发到服务提供方,有服务提供方直接把服务响应发回给请求方。通过任务池来实现负载的平衡和系统的稳定性,同时通过分片提交发送的效率,并在其基础上实现负载平衡。     

DILIGENT: integrating digital library and Grid technologies for a new Earth observation research infrastructure

This paper introduces DILIGENT, a digital library infrastructure built by integrating digital library and Grid technologies and resources. This infrastructure allows different communities to dynamically build specialised digital libraries capable to support the entire e-Science knowledge production and consumption life-cycle by using shared computing, storage, content, and application resources. The paper presents some of the main software services that implement the DILIGENT system. Moreover, it exemplifies the provided features by presenting how the DILIGENT infrastructure is being exploited in supporting the activity of user communities working in the Earth Science Environmental sector. This work is partially funded by the European Commission in the context of the DILIGENT project, under the 2nd call of FP6 IST priority.     

基于概念间双向语义和多重关系的Web服务发现

如何提高Web服务的发现效果是面向服务计算领域需要解决的关键问题.针对这一问题,文中提出基于概念间双向语义和多重关系的Web服务发现方法.在计算概念相似度时,考虑语义的双向性及连接路径上的多重关系.不同于以往的服务发现方法,不仅计算服务描述中的名词和动词,还计算形容词.在名词和动词相似度计算中,涵盖继承关系(ISA)、部分整体关系(HASA)和反义关系(ANT).在形容词相似度计算中,涉及相似关系(Similar-to)和ANT.Web服务相似度的计算综合I/O和功能描述两方面内容,提高Web服务发现的准确性.通过综合数据实验充分验证文中方法的有效性,该方法具有较优的查准率、查全率及F-measure.     

Interoperability results for Semantic Web technologies using OWL as the interchange language

Using Semantic Web technologies in complex scenarios requires that such technologies correctly interoperate by interchanging ontologies using the RDF(S) and OWL languages. This interoperability is not straightforward because of the high heterogeneity in Semantic Web technologies and, while the number of such technologies grows, affordable mechanisms for evaluating Semantic Web technology interoperability are needed to comprehend the current and future interoperability of Semantic Web technologies.This paper presents the OWL Interoperability Benchmarking, an international benchmarking activity that involved the evaluation of the interoperability of different Semantic Web technologies using OWL as the interchange language. It describes the evaluation resources used in this benchmarking activity, the OWL Lite Import Benchmark Suite and the IBSE tool, and presents how to use them for evaluating the OWL interoperability of Semantic Web technologies. Moreover, the paper offers an overview of the OWL interoperability results of the eight tools participating in the benchmarking: one ontology-based annotation tool (GATE), three ontology frameworks (Jena, KAON2, and SWI-Prolog), and four ontology development tools (Protégé Frames, Protégé OWL, SemTalk, and WebODE).     

基于语义索引词的语义网信息检索模型

提出了一个基于语义索引词的语义网信息检索模型(Semantic Information Retrieval，SIR)，解决了在传统的基于关键字的信息检索中只能从句法上对关键字进行分析，无法根据信息资源中的语义关系进行检索的问题。     

Semantic Web Mining: State of the art and future directions

Semantic Web Mining aims at combining the two fast-developing research areas Semantic Web and Web Mining. This survey analyzes the convergence of trends from both areas: More and more researchers are working on improving the results of Web Mining by exploiting semantic structures in the Web, and they make use of Web Mining techniques for building the Semantic Web. Last but not least, these techniques can be used for mining the Semantic Web itself.The Semantic Web is the second-generation WWW, enriched by machine-processable information which supports the user in his tasks. Given the enormous size even of today’s Web, it is impossible to manually enrich all of these resources. Therefore, automated schemes for learning the relevant information are increasingly being used. Web Mining aims at discovering insights about the meaning of Web resources and their usage. Given the primarily syntactical nature of the data being mined, the discovery of meaning is impossible based on these data only. Therefore, formalizations of the semantics of Web sites and navigation behavior are becoming more and more common. Furthermore, mining the Semantic Web itself is another upcoming application. We argue that the two areas Web Mining and Semantic Web need each other to fulfill their goals, but that the full potential of this convergence is not yet realized. This paper gives an overview of where the two areas meet today, and sketches ways of how a closer integration could be profitable.     

结构工程语义网格本体模型框架的设计与实现

针对结构工程领域中信息集成与共享时碰到的语义冲突问题,基于网格计算和语义Web的相关技术规范,设计和实现结构工程语义网格体系结构.并在此基础上,着重分析在构建结构工程语义网格语义层的过程中,在实例集成、模式集成、概念集成和查询应用几个阶段的语义冲突问题,设计与初步实现语义层本体模型框架.     

Web technologies for environmental Big Data

Recent evolutions in computing science and web technology provide the environmental community with continuously expanding resources for data collection and analysis that pose unprecedented challenges to the design of analysis methods, workflows, and interaction with data sets. In the light of the recent UK Research Council funded Environmental Virtual Observatory pilot project, this paper gives an overview of currently available implementations related to web-based technologies for processing large and heterogeneous datasets and discuss their relevance within the context of environmental data processing, simulation and prediction. We found that, the processing of the simple datasets used in the pilot proved to be relatively straightforward using a combination of R, RPy2, PyWPS and PostgreSQL. However, the use of NoSQL databases and more versatile frameworks such as OGC standard based implementations may provide a wider and more flexible set of features that particularly facilitate working with larger volumes and more heterogeneous data sources.