语义Web

介绍了语义Web的概念有本体模型在其中的运用，比较了XML/XML Schema和RDF/RDF Schema在语义表示方面的使用，提出RDF（S）更适合于语义Web；最后讨论了扩展RDF开发语义Web应用所应该注意的问题。     

Mining the Semantic Web

In the Semantic Web vision of the World Wide Web, content will not only be accessible to humans but will also be available in machine interpretable form as ontological knowledge bases. Ontological knowledge bases enable formal querying and reasoning and, consequently, a main research focus has been the investigation of how deductive reasoning can be utilized in ontological representations to enable more advanced applications. However, purely logic methods have not yet proven to be very effective for several reasons: First, there still is the unsolved problem of scalability of reasoning to Web scale. Second, logical reasoning has problems with uncertain information, which is abundant on Semantic Web data due to its distributed and heterogeneous nature. Third, the construction of ontological knowledge bases suitable for advanced reasoning techniques is complex, which ultimately results in a lack of such expressive real-world data sets with large amounts of instance data. From another perspective, the more expressive structured representations open up new opportunities for data mining, knowledge extraction and machine learning techniques. If moving towards the idea that part of the knowledge already lies in the data, inductive methods appear promising, in particular since inductive methods can inherently handle noisy, inconsistent, uncertain and missing data. While there has been broad coverage of inducing concept structures from less structured sources (text, Web pages), like in ontology learning, given the problems mentioned above, we focus on new methods for dealing with Semantic Web knowledge bases, relying on statistical inference on their standard representations. We argue that machine learning research has to offer a wide variety of methods applicable to different expressivity levels of Semantic Web knowledge bases: ranging from weakly expressive but widely available knowledge bases in RDF to highly expressive first-order knowledge bases, this paper surveys statistical approaches to mining the Semantic Web. We specifically cover similarity and distance-based methods, kernel machines, multivariate prediction models, relational graphical models and first-order probabilistic learning approaches and discuss their applicability to Semantic Web representations. Finally we present selected experiments which were conducted on Semantic Web mining tasks for some of the algorithms presented before. This is intended to show the breadth and general potential of this exiting new research and application area for data mining.     

一种渐进式论辩语义的论证博弈模型

在可计算论辩模型中,论辩语义的证明理论解决如何判定给定论辩语义中某个论证的证成状态的问题,这通常需要建构与之对应的论证博弈模型。论证博弈发生在正方和反方的论证交互过程中,正反双方都是通过给出攻击论证来质疑对方的论证和辩护己方的论证,正方只有在论证博弈中获胜才能使其初始论证获得确定的证成状态。文中定义了一种被称为BRD-论辩语义的渐进式论辩语义,不同于Dung的抽象论辩语义,它是在结构化论辩框架ASPIC+中嵌入了一种用于计算论证的强度和证成度的循环语义。为了给出该语义的证明理论,建构了与之对应的论证博弈模型。     

语义Web在Web服务中的应用

在分析了现有的Web服务标准和语义Web的基础上，介绍了一种把二者结合起来的语义Web服务（SwS）。它可以实现在不需要人的参与下，Web服务的自动发现、自动调用和自动组合。对于语义Web服务的开发有3种方法，文中重点介绍了实现SWS的一种基于本体的面向代理的OWL—S开发方法。     

语义Web在Web服务中的应用

在分析了现有的Web服务标准和语义Web的基础上,介绍了一种把二者结合起来的语义Web服务(SWS)。它可以实现在不需要人的参与下,Web服务的自动发现、自动调用和自动组合。对于语义Web服务的开发有3种方法,文中重点介绍了实现SWS的一种基于本体的面向代理的OWL-S开发方法。     

语义网

语义网WWW使得人们获取信息和指定服务的主要源泉发生了重要转变。然而,当前Web网仅仅是面向人的。目前,大部分Web网是一种人机系统。如果要从Web网得到什么,必须输入一个统一资源定位器(URL)地址,打开一个收藏地址,使用搜索引擎等等。机器可以理解的信息:语义Web与此相反,语义     

语义网、语义网格和语义网络

语义网、语义网格和语义网络是三个容易混淆的概念,语义网是对WWW的延伸,其目标是使得Web上的信息具有计算机可以理解的语义,并为人们提供各种智能服务;语义网格是语义Web和网格相结合产生的新的研究领域;语义网络是知识的一种图解表示,它由节点和弧线或链线组成.通过对三者的概念、特征、应用等方面进行介绍从而说明了三者的联系以及不同,并说明了今后对三者的研究方向和重点问题.     

Semantic Sensor Web

Sensors are distributed across the globe leading to an avalanche of data about our environment. It is possible today to utilize networks of sensors to detect and identify a multitude of observations, from simple phenomena to complex events and situations. The lack of integration and communication between these networks, however, often isolates important data streams and intensifies the existing problem of too much data and not enough knowledge. With a view to addressing this problem, the Semantic Sensor Web (SSW) proposes that sensor data be annotated with semantic metadata that will both increase interoperability and provide contextual information essential for situational knowledge.     

Semantic Web services

The authors propose the markup of Web services in the DAML family of Semantic Web markup languages. This markup enables a wide variety of agent technologies for automated Web service discovery, execution, composition and interoperation. The authors present one such technology for automated Web service composition.     

Semantic Web 2.0

The Web 2.0 phenomena is poised to set the creative tone for software developers for years. It could even become a technology wave--one that the Semantic Web would do well to catch.     

Agents and the Semantic Web

Many challenges of bringing communicating multi-agent systems to the World Wide Web require ontologies. The integration of agent technology and ontologies could significantly affect the use of Web services and the ability to extend programs to perform tasks for users more efficiently and with less human intervention.     

Search on the Semantic Web

To help human users and software agents find relevant knowledge on the Semantic Web, the Swoogle search engine discovers, indexes, and analyzes the ontologies and facts that are encoded in Semantic Web documents.     

语义Web中的对象技术

对象技术是增加Web功能的有效途径。作为Web技术的发展，语义Web为对象技术与Web的结合提供了新方法和新技术。讨论了语义Web的页面对象模型和分布式对象与语义Web的结合，阐述了以XML为基础的语义Web技术在其中所起的作用，并指出了发展方向。