面向语义Web的领域本体表示、推理与集成研究

语义Web的基础之一是本体，为了让机器能够理解Web的内容并做推理，需要建立本体，并利用本体中定义的术语作为元数据来标记Web的内容．阐明了本体和领域本体的关系；介绍了现有面向语义Web的本体语言的概况；根据表达能力和推理效率的综合权衡，选择OWL Lite作为本体语言；提出了一种面向语义Web的领域本体表示、推理方法DORRSW和一种面向语义Web的多领域本体集成方法MDOISW；最后给出应用实例来说明方法的应用．通过这些论述，阐明面向语义Web的领域本体表示、推理与集成的基本情况，从而为创建面向语义Web的本体提供了基础知识．     

CDDL:动态描述逻辑的不确定性扩展

本体(Ontology)是语义Web中共享知识的形式化建模工具,其逻辑基础是描述逻辑.动态描述逻辑(DDL)具有同时表示静态和动态知识的优势.本文针对语义Web需要处理不确定性动态知识的需求,利用云模型对DDL进行不确定性扩展,提出了一种能够有效实现不确定性静态和动态知识进行表示和推理的不确定性动态描述逻辑CDDL.与...     

语义Web应用程序:性质与开发模型

语义Web应用程序是应用语义Web技术开发的Web应用程序,其特点是全部信息均由应用程序本体所定义的概念进行标记,因而含有丰富的语义,能更好地支持信息集成和知识共享,从而能够更加充分地利用已有Web资源.给出语义Web应用程序的定义、构成与性质;说明应用程序本体与知识库的作用;建立语义Web应用程序的开发模型.通过编写一个基于计算机文献本体的语义Web应用程序,用实例验证了所提出开发模型的可行性以及在信息集成和信息检索方面取得的明显效果.     

基于知识元本体的知识统一表示

分析描述逻辑和本体论语义,提出知识元本体论点,并用Web本体语言OWL详细地构建了知识元本体的初步版本.提出基于知识元本体的知识表示方法.从而为构建具有更小知识单元共享粒度和知识语义推理的知识库系统提供统一的知识元本体定义.     

一种基于Rough本体的语义搜索引擎模型

基于关键字匹配的搜索引擎无法反映Web信息在现实世界中的语义,由此不可避免地导致查准率和查全率低的缺陷:另一方面,目前本体支持的形式化概念还不足以表示不完备知识.因此本文结合Rough本体理论,提出了一种基于Rough本体的语义搜索引擎模型,讨论了模型设计和实现其中的若干关键技术.最后对模型的实现技术进行概述.     

语言变量模糊本体的表示与构建

语言变量模糊本体是语言变量在语义Web中的明确的规范化说明,有利于模糊系统与语义Web的结合,使得语义web更加方便地处理模糊信息。通过引入语言变量模糊本体的概念,研究使用RDF表示模糊本体的方法,将本体与模糊概念表示为“资源”。进而以工业洗衣机的模糊控制为例,提出从模糊系统构造语言变量模糊本体的过程。     

基于OWL的自然灾害领域应急响应知识表示方法

利用语义网中本体和OWL(Ontology Web Language,即网络本体语言)等相关技术,通过一阶谓词逻辑及产生式知识表示方法具体描述地震灾害应急响应,为实现自然灾害领域应急响应的知识表示和共享提供一种参考。     

基于本体的Web分类技术研究

主要提出了一种基于本体的抽象的Web挖掘模型。首先利用本体的方法表示出要挖掘的领域,然后把从用户处收集来的数据转换成表格;最后再根据定义和公式来进行知识发现。抽象的Web挖掘模型可以提取出语义Web中隐藏在大量信息背后的近似概念,来实现知识发现。     

一种利用模糊关系数据库构建模糊OWL本体的方法

经典OWL本体不能直接表示和处理语义Web应用中广泛存在的模糊知识,鉴于模糊关系数据库在模糊数据表示与处理方面的优势,提出利用模糊关系数据库来构建模糊OWL本体.通过对RDF数据类型进行模糊扩展,并从模糊数据类型角度扩展OWL,解决了模糊OWL本体的表示问题;在此基础上,研究了以模糊关系数据库为数据源的模糊OWL本体的构建方法,该方法为语义Web中模糊本体的构建提供了一个有效的解决方案.     

语义集成中的不确定性本体映射模型

语义Web的高速发展使其具有动态性和异构性特征,解决语义信息的异构性问题成为实现信息集成的关键。本体作为一种语义Web的知识表示形式,增强了Web的语义信息。因此,为了解决语义异构性,实现数据间的互操作,必须建立异构本体间的映射关系。然而,为庞大的异构本体建立完全精确的本体映射是不现实的,本体映射中存在一定的不确定性。提出了一种新型的本体映射框架——语义集成中的不确定性本体映射。从不同方面研究本体特征,集合了多种映射策略,并引入了各映射策略中不确定性匹配的解决方案。实验证明,该方法具有可靠的实验性能,并且具有很好的通用性和可扩展性。     

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.     

基于语义Web上知识表示的研究及其应用

随着对语义研究的深入,人们越来越关注在W EB上信息内容的表示问题,通过分析语义W EB上知识表示的特点后指出,以RDF为基础的知识表示语言可较好地实现语义W EB的知识表示。最后通过语义W EB上的语义检索应用给予了说明。     

Semantic Web based innovative design knowledge modeling for collaborative design

This paper describes a study about how to use the Semantic Web technologies for innovative design knowledge modeling in a multi-agent distributed design environment. Semantic Web based knowledge modeling for innovative design is proposed as prelude to the meaningful agent communication and knowledge reuse for collaborative work among multidisciplinary organizations. A model for innovative design is proposed at first, based on which a knowledge schema is brought forward. For sharing the design knowledge among an internet-based or distributed work team, even globally, A RDF-based knowledge model is presented to realize its representation on Semantic Web. A Semantic Web based repository for innovative design and its API for topper Semantic Web applications have been also constructed. The proposed knowledge modeling extends traditional product modeling with capabilities of innovative design, knowledge sharing and distributed problem solving, and is employed as a content language within the messages in the proposed multi-agent system architecture. The proposed approach is viewed as a promising knowledge management method that facilitates the implementation of computer supported cooperative work in innovative design of Semantic Web applications.     

Semantic Web Constraint Language and its application to an intelligent shopping agent

Semantic Web society was initially focused only on data, but then gradually moved toward knowledge. If a vision of the Semantic Web is to enhance humans' decision-making assisted by machines, a missing but important part is knowledge about constraints on data and concepts represented by ontology. This paper proposes a Semantic Web Constraint Language (SWCL) based on OWL, and shows its effectiveness in representing and solving an internet shopper's decision-making problems by implementing a shopping agent in the Semantic Web environment.     

语义网环境下的知识集成系统设计

语义网近年来发展迅速,为网络环境下实现知识集成提供理论支撑和技术路线,可以为知识数据建立关联并增强系统的可扩展性。在资源描述框架下构建知识本体,采用Java语言和Jena API对知识集成系统进行设计和开发,构建知识集成框架实现知识内容显示和关联查询。系统设计突出了语义网的功能和特色,应用多种语义网体系技术和工具,提高知识集成系统的数据分析能力和用户体验效果。     

Collective knowledge systems: Where the Social Web meets the Semantic Web

What can happen if we combine the best ideas from the Social Web and Semantic Web? The Social Web is an ecosystem of participation, where value is created by the aggregation of many individual user contributions. The Semantic Web is an ecosystem of data, where value is created by the integration of structured data from many sources. What applications can best synthesize the strengths of these two approaches, to create a new level of value that is both rich with human participation and powered by well-structured information? This paper proposes a class of applications called collective knowledge systems, which unlock the “collective intelligence” of the Social Web with knowledge representation and reasoning techniques of the Semantic Web.     

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.     

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及其应用

语言文Web是下一代Interuet的发展方向。语义Web的定义、分层结构进行了概述，详细总结和研究了语义Web在Web服务、P2P网络、知识管理、E-learning、智能信息检索和语义Web挖掘、网格计算等多个领域的应用。