Ontology在语义Web中的应用研究

探讨了本体Ontology及语义Web,描述了Ontology在语义Web中的作用,结合信息检索和B2B的电子商务这两个具体应用,研究了Ontology在其中的作用,并且对实现中需要注意的问题进行了说明。     

基于奇异值分解的中文Ontology自动学习技术

语义Web是一个美好的构想，Ontology在语义Web中起着举足轻重的作用，它不仅能为人类用户而且能为软件agent提供从语法层次到语义层次上的互操作性。目前Web上主要是各种布局的HTML文档，未来的语义Web页面将是各种领域Ontology的实例以及到其它实例上的链接，因此语义Web的成功强烈依赖于Ontology的增殖，方便快捷地构造各领城Ontology是实现语义Web的关健。该文提出一种基于奇异值分解的中文Ontology自动学习技术，这种技术的特点是其简易性以及准确的数学理论基础。     

语义Web服务——Web服务的扩展

语义Web服务是应用语义Web技术对Web服务的扩展.使信息具有语义就是用计算机内的Ontology中的概念作标记符对信息进行标记,对该过程予以支持的就是语义Web技术,即Ontology的构建技术、Ontology的使用技术(语义推理技术)和信息的语义标记技术.语义Web技术对Web服务的扩展可具体化为两项任务:服务提供者、服务请求者和服务注册处三类服务主体均内置Ontology;发布、查找和绑定三种交互信息均采用语义标记.     

语义Web技术及其逻辑基础

随着语义Web的产生和发展,人-机及机-机间的交流与协作将变得更加方便。该文从介绍语义Web概念及体系结构入手,对关键技术XML, RDF及Ontology进行了对比分析说明,同时研究了其逻辑基础(描述逻辑),分析了其语法语义及推理任务。对语义Web的未来发展热点问题进行了展望。     

语义万维网中基于Agent的智能检索框架

语义万维网是目前国际万维网联盟（world wide web consortium，W3C）为了解决因Web上的数据缺少语义信息而难以实现自动化处理的问题所开展的研究项目，其目的是为了对Web上发布的信息实现智能推理和自动化处理。Agent作为一种智能化主体，非常适合语义万维网环境下的各种应用。在语义万维网和智能Agent研究的基础上，综合信息检索、知识表示、Ontology建模等多方面技术，提出并实现了一个基于Ontology实现语义信息检索的多Agent系统框架，该系统包括信息收集、存储、查询和推理4个主要部分。     

语义Web中工程设计类知识表示研究

研究了如何在语义Web中，实现工程设计类知识的表示与应用。对工程设计类各种异构知识进行统一描述，在此基础上，运用RDF(S)技术，建立了语义Web环境下，工程设计类知识的Ontology。提出了基于语义Web的知识存储模型以及面向上层应用程序的接口设计，为语义Web环境下工程设计类知识管理系统的开发提供了底层平台。     

基于Ontology的Web语言OIL及其应用

Ontology在网络信息交换中起着重大的作用，OIL是一种基于Ontology技术的Web环境下的领域模型语言。使用OIL定义的领域模型扩展了RDPS，可以实现在Web体系中的计算机可理解知识系统。将Onlology应用到Web上，就产生了语义化的Web。     

XML＋RDF＋Ontology——实现Web数据语义的描述

本文首先给出了语义Web的体系结构，继而分析了XML结合RDF与Ontology怎样用于实现Web数据语义的描述．最后总结了全文。     

基于机器学习的本体集成框架

语义服务是下一代Web服务面临的关键问题．语义网为实现广泛的语义服务提供了可能，Ontology是语义网体系结构的核心。针对协作的分布式系统需要语义互联的问题，本文分析了造成语义互联困难的主要因素是本体的匹配和集成，提出了一个基于机器学习的Ontology集成的框架模型。     

基于Ontology和描述逻辑推理的Web题目资源检索

Web网上存在着大量题目资源，学生在学习过程中需要准确找到与其所学知识真正相吻合的题目。但是从题目的语言表述往往很难获得其语义信息，合适的题目难以找到。该文提出了一种基于Ontology和描述逻辑推理的Web题目资源检索方案。该方案通过为Web题目资源添加语义注释，并通过描述逻辑推理完成基于语义的题目资源检索，使学生获得与其所学知识语义相关的题目。采用OWL描述Ontology、使用推理机RACER实现描述逻辑推理。     

Semantic Web研究综述

近年来,Semantic Web逐渐成为WWW领域的研究热点以及智能化网络服务和应用开发中的关键技术之一。归纳了Semantic Web技术的研究背景和主要发展历史。在分析了典型的Semantic Web概念后,给出了Semantic Web的定义。通过讨论Semantic Web构想的层次框架模型,指出了各个层次扮演的角色,并着重分析了Semantic Web的重要研究领域,指出了它们在Semantic Web构架中的核心作用。通过分析Semantic Web的应用领域和相关开发工具以及面临的问题和挑战,指明了Semantic Web研究和实践的方向。作为总结,给出了Semantic Web领域下一步的研究趋势。     

Semantic Web computing in industry

The Semantic Web has attracted significant attention during the last decade. On the one hand, many research groups have changed their focus towards Semantic Web research and research funding agencies particularly in Europe have explicitly mentioned Semantic Web in their calls for proposals. On the other hand, industry has also begun to watch developments with interest and a number of large companies have started to experiment with Semantic Web technologies to ascertain if these new technologies can be leveraged to add more value for their customers or internally within the company, while there are already several offers of vendors of Semantic Web solutions on the market. The essence of the Semantic Web is to structure Web-based information to make it more interoperable, machine-readable and thereafter to provide a means to relate various information concepts more easily and in a reusable way. The Semantic Web acts as an additional layer on the top of the Web, and is built around explicit representations of information concepts and their relationships such as ontologies and taxonomies. Furthermore, Semantic Web technologies are not only valuable on an open environment like the Web, but also in closed systems such as in industrial settings. Hence, these technologies can be efficiently deployed for domains including Web Services, Enterprise Application Integration, Knowledge Management and E-Commerce, fulfilling existing gaps in current applications. This paper focuses on this synthesis between Semantic Web technologies and systems problems within industrial applications. There will be a short review of Semantic Web standards, languages and technologies followed by a more detailed review of applications of Semantic Web computing in industry. The paper covers theoretical considerations as well as use cases and experience reports on the topic, and we also present some current challenges and opportunities in the domain.     

基于语义距离度量模型的语义Web服务匹配排序机制

随着语义Web服务技术研究工作的不断深入,因特网上语义Web服务数量急剧增加。如何快速便捷地定位可用语义Web服务已经成为一个迫切且关键的问题。在语义Web服务匹配技术研究中,其中一个重要的研究主题就是语义Web服务匹配结果的排序机制。本文在综合概括和分析各种关于语义Web服务匹配结果排序机制的基础上,提出了一种基于语义距离度量模型的语义Web服务匹配结果排序机制,利用该排序机制,计算待匹配语义Web服务的语义相似度量,并依据此度量对语义Web服务匹配结果进行排序。该度量模型将语义Web服务引用概念间的语义关系转换成可精确比较的量化度量值,对属于相同语义匹配类型的匹配候选服务也能够分别计算语义距离,区分出相同匹配类型的候选服务与服务请求的匹配程度,从而达到改善用户对语义Web服务的搜索体验的目的。     

Trends in built environment semantic Web applications: Where are we today?

The built environment sector impacts significantly on communities. At the same time, it is the sector with the highest cost and environmental saving potentials provided effective strategies are implemented. The emerging Semantic Web promises new opportunities for efficient management of information and knowledge about various domains. While other domains, particularly bioinformatics have fully embraced the Semantic Web, knowledge about how the same has been applied to the built environment is sketchy. This study investigates the development and trend of Semantic Web applications in the built environment. Understanding the different applications of the Semantic Web is essential for evaluation, improvement and opening of new research. A review of over 120 refereed articles on built environment Semantic Web applications has been conducted. A classification of the different Semantic Web applications in relation to their year of application is presented to highlight the trend. Two major findings have emerged. Firstly, despite limited research about easy-to-use applications, progress is being made from often too-common ontological concepts to more innovative concepts such as Linked Data. Secondly, a shift from traditional construction applications to Semantic Web sustainable construction applications is gradually emerging. To conclude, research challenges, potential future development and research directions have been discussed.     

语义Web链接结构分析之综述

随着语义Web研究的发展,其数据量也不断增长,要实现语义Web追求的目标——数据的共享和重用,语义Web上的实体搜索和文档搜索必不可少。而面对这样不断增长的数据以及不同于传统Web的搜索要求,就需要使用链接结构分析来指导语义Web上的搜索。同时,语义Web的发展现状也无时无刻不吸引着研究人员的关注,而链接结构分析对于揭示其宏观结构起着关键作用。分别从实体和文档两个粒度对面向语义Web链接结构分析的研究进行总结,特别关注链接模型的构建以及链接结构分析方法的应用。     

基于语义的Web挖掘

基于语义的Web挖掘是使用从现有Web数据中抽取的语义或直接使用Web数据中已有的语义结构来帮助Web挖掘。它有效地结合了语义网和Web挖掘两个领域的研究成果,既可以通过开发新的语义结构来帮助Web挖掘,又可以利用挖掘结果促进语义网的创建。本文介绍了基于语义的Web挖掘的基本思想和研究现状,分析了语义网和Web挖掘相结合的优势,并详细论述了国际上关于利用数据挖掘技术创建语义网,利用语义挖掘Web数据和直接挖掘语义网三个方面的研究工作。     

基于QoS的语义Web服务组合研究

Web服务技术与语义网技术的发展,产生了一个新的研究领域——语义网服务。语义网服务利用本体技术增强了Web服务的语义表达能力,使服务的发现与执行,组合与交互更加自动智能化。关注Web服务的非功能属性,即服务的QoS属性,研究分析了基于QoS的语义网服务组合工作,提出了一个Web服务的QoS本体模型,并讨论了该模型在语义网服务组合工作中的应用。     

Toward a New Generation of Semantic Web Applications

Although research on integrating semantics with the Web started almost as soon as the Web was in place, a concrete Semantic Web that is, a large-scale collection of distributed semantic metadata emerged only over the past four to five years. The Semantic Web's embryonic nature is reflected in its existing applications. Most of these applications tend to produce and consume their own data, much like traditional knowledge- based applications, rather than actually exploiting the Semantic Web as a large-scale information source. These first-generation semantic Web applications typically use a single ontology that supports integration of resources selected at design time.     

Semantic Web: A road to the knowledge infrastructure on the internet

In this article, I describe the basic technologies for Semantic Web and relationship between Semantic Web and Knowledge Representation in Artificial Intelligence. Semantic Web is planned as an extension of the current web in order to help cooperation between computers and humans, i.e., computers and humans are expected to understand each other in the knowledge level. I first describe the vision of the Semantic Web, then introduce the current Semantic Web technologies, i.e., RDF, RDFS, and OWL. I describe relationship between the trend of Semantic Web and Knowledge Representation, and clarify challenges and difficulties of Semantic Web from the point of view of Knowledge Representation. Hideaki Takeda: He is a professor at National Institute of Informatics (NII) and a professor in Department of Informatics at the Graduate University of Advanced Studies (Sokendai). He received his Ph.D. from the University of Tokyo in 1991. His research interest in computer science includes ontology engineering, community informatics and knowledge sharing systems.