基于本体的网络数据工作平台NetData

近年来,网格、语义网络等新技术迅速发展并日臻成熟。互联网发展焦点开始从信息的发布和互联转向知识的交互框架。随着语义网络迅速发展,世界各地各个领域的研究爱好者组成虚拟社区,对同一领域的知识信息一起协作研究。其中,对数据的整理、保存、检索、分析是实现语义网络远景的基础工作。本文为了帮助研究社区的研究人员更有效方便地加入社区的研究,利用长期帮助中国中医研究院建设专业结构化数据库群的项目中所取得的经验,结合了语义网络和数据库网格的研究,设计并初步实现了基于本体的网络数据工作平台。     

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。     

The Semantic Web: Collective Intelligence on the Web

The World Wide Web has turned hypertext into a success story by enabling world-wide sharing of unstructured information and informal knowledge. The Semantic Web targets the sharing of structured information and formal knowledge pursuing objectives of achieving collective intelligence on the Web. Germane to the structure of the Semantic Web is a layering and standardization of concerns. These concerns are reflected by an architecture of the Semantic Web that we present through a common use case. Semantic Web data for the use case is now found on the Web and is part of a quickly growing set of Semantic Web resources available for formal processing.     

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.     

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.     

The two cultures: Mashing up Web 2.0 and the Semantic Web

A common perception is that there are two competing visions for the future evolution of the Web: the Semantic Web and Web 2.0. A closer look, though, reveals that the core technologies and concerns of these two approaches are complementary and that each field can and must draw from the other’s strengths. We believe that future Web applications will retain the Web 2.0 focus on community and usability, while drawing on Semantic Web infrastructure to facilitate mashup-like information sharing. However, there are several open issues that must be addressed before such applications can become commonplace. In this paper, we outline a semantic weblogs scenario that illustrates the potential for combining Web 2.0 and Semantic Web technologies, while highlighting the unresolved issues that impede its realization. Nevertheless, we believe that the scenario can be realized in the short-term. We point to recent progress made in resolving each of the issues as well as future research directions for each of the communities.     

Explaining answers from the Semantic Web: the Inference Web approach

The Semantic Web lacks support for explaining answers from web applications. When applications return answers, many users do not know what information sources were used, when they were updated, how reliable the source was, or what information was looked up versus derived. Many users also do not know how implicit answers were derived. The Inference Web (IW) aims to take opaque query answers and make the answers more transparent by providing infrastructure for presenting and managing explanations. The explanations include information concerning where answers came from (knowledge provenance) and how they were derived (or retrieved). In this article we describe an infrastructure for IW explanations. The infrastructure includes: IWBase — an extensible web-based registry containing details about information sources, reasoners, languages, and rewrite rules; PML — the Proof Markup Language specification and API used for encoding portable proofs; IW browser — a tool supporting navigation and presentations of proofs and their explanations; and a new explanation dialogue component. Source information in the IWBase is used to convey knowledge provenance. Representation and reasoning language axioms and rewrite rules in the IWBase are used to support proofs, proof combination, and Semantic Web agent interoperability. The Inference Web is in use by four Semantic Web agents, three of them using embedded reasoning engines fully registered in the IW. Inference Web also provides explanation infrastructure for a number of DARPA and ARDA projects.     

语义Web的标记语言和体系结构

当前WWW上的信息主要是为人类阅读而设计的，而语义Web试图将WWW上的海量信息以一种机器可理解的方式组织起来，提供数据的语义关系的表达手段，以满足日益增加Web应用对数据互操作性的要求，XML提供了对数据表达的语法的统一描述，RDF和RDF Schema提供了对数据语义的表达手段，本体论（Ontology)是关于领域内共享概念的形式化的规格说明，在语义Web中起重要作用，本体论语言形成关于本体的逻辑描述，这样从下到上形成了语义Web的层次体系结构。     

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     

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.