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.     

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.     

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.     

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     

Argumentation in the Semantic Web

In this article, we define ArgSciFF, a prototype operational argumentation framework to support dialogic argument exchange between Semantic Web services. ArgSciFF is based on the Sciff abductive-logic programming (ALP) framework. (Sciff is an abbreviation for "IFF with constraints for agent societies," referring to the "if and only if' proof procedure developed by Tze Ho Fung and Robert Kowal-ski.) In ArgSciFF, an intelligent agent can interact with a Web service and reason from the interaction result. The reasoning semantics is an argumentation semantics that views the interaction as a dialogue. The dialogue lets two parties exchange arguments and attack, challenge, and justify them on the basis of their knowledge. This format has the potential to overcome a well-known barrier to human users' adoption of IT solutions because it permits interaction that includes justified answers that can be reasoned about and rebutted.     

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.     

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.     

Metcalfe's law, Web 2.0, and the Semantic Web

The power of the Web is enhanced through the network effect produced as resources link to each other with the value determined by Metcalfe's law. In Web 2.0 applications, much of that effect is delivered through social linkages realized via social networks online. Unfortunately, the associated semantics for Web 2.0 applications, delivered through tagging, is generally minimally hierarchical and sparsely linked. The Semantic Web suffers from the opposite problem. Semantic information, delivered through ontologies of varying amounts of expressivity, is linked to other terms (within or between resources) creating a link space in the semantic realm. However, the use of the Semantic Web has yet to fully realize the social schemes that provide the network of users. In this article, we discuss putting these together, with linked semantics coupled to linked social networks, to deliver a much greater effect.     

语义网与本体

深入了解了语义网的由来,研究语义网的结构,通过对Unicode和URI、XML+NS+XML Schema、RDF+RDF Schema、Ontology vocabulary 4个层次模型来铸造稳固的结构。将本体技术融入到语义网运行过程中,实现知识共享和交换的目标。     

一种面向语义Web服务的语义程序变换方法

语义程序变换是面向语义Web服务的软件设计方法的基础,语义程序只有通过程序变换后才能被运行环境执行和调用,然而目前还缺乏有效的语义程序变换方法。针对这一问题,基于语义编程语言SPL,提出了一种面向语义Web服务的语义程序变换方法。该方法通过对语义数据类型、语义规则、语义服务和语义流程等语义信息的有效变换,不仅提高了面向服务的程序设计的灵活性和健壮性,而且有助于提高业务流程的柔性和重用性。     

XML及语义Web技术

1 引言 XML的目标就是要改变Web的基本结构,超越HTML并代之以更强大、更具有可扩展的体系结构。XML旨在使Web返回到基于内容的结构,而不再是开发人员强加给它的基于格式的结构。但是到目前为止,几乎所有的Web页面都是用HTML编写的。虽然HTML具有通用、简单易学、句法简单紧凑等许多优点,使得它得以在Web网页上大显身手,但是随着Web应用的越来越深入,HTML过于简单的弱点也越来越突出了。其中一个明显的弱点即是由HTML编写