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。     

本体相似度研究

不同本体之间的交互成为语义Web的首要任务,其中本体相似度计算是本体映射的关健环节。在以往的研究中,本体相似度计算通常专注于模式及其结构的匹配。目前研究朝着进一步考虑本体内部语义信息方向努力。本文描述了语义相似度栈的各个层次,依据各个层次的语义特征对目前本体相似度方法进行分类,并对每种方法进行了详细描述。最后对现有一些主要的本体间相似度计算方法进行归纳总结。这项研究工作将为人们提出新的相似度方法或者组合的计算方法作一个参考。     

语义Web中的本体匹配研究

随着本体应用的快速发展,本体数量大幅增长,这些本体描述的内容存在重复和关联,但在本体模式上却表现各异。本体匹配旨在识别异构本体中存在语义关联的实体,并建立它们之间匹配关系。它对于消除本体异构、实现本体集成和数据融合等具有重要作用。形式化定义了语义Web中的本体匹配问题,并从本体匹配方法、本体匹配挑战和本体匹配原型系统3个方面调研了最新研究进展,旨在为进一步研究指明方向。     

Determining semantic similarity among entity classes from different ontologies

Semantic similarity measures play an important role in information retrieval and information integration. Traditional approaches to modeling semantic similarity compute the semantic distance between definitions within a single ontology. This single ontology is either a domain-independent ontology or the result of the integration of existing ontologies. We present an approach to computing semantic similarity that relaxes the requirement of a single ontology and accounts for differences in the levels of explicitness and formalization of the different ontology specifications. A similarity function determines similar entity classes by using a matching process over synonym sets, semantic neighborhoods, and distinguishing features that are classified into parts, functions, and attributes. Experimental results with different ontologies indicate that the model gives good results when ontologies have complete and detailed representations of entity classes. While the combination of word matching and semantic neighborhood matching is adequate for detecting equivalent entity classes, feature matching allows us to discriminate among similar, but not necessarily equivalent entity classes.     

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XQuery扩展的异构语义Web服务发现

语义Web服务发现作为分布式计算的前提和关键,备受研究者关注。多种语义Web服务描述语言的存在给异构语义Web服务的发现带来了挑战。本文提出了一种支持异构语义Web服务描述语言的发现框架i XQuery。该框架从两个方面扩展XQuery,使其支持异构语义Web服务的结构化查询与逻辑推理、模糊匹配的联合查询。一方面i XQuery利用XQuery的外部函数机制,建立了多种类型的相似度比较函数;另一方面i XQuery制定了一个统一的语义Web服务抽象描述本体,并建立了语义Web服务语言SAWSDL与OWLS与该本体之间的映射,并利用XQuery的用户自定义函数机制,建立了一系列用于抽取异构语义Web服务中信息的抽象描述操作子。最后,介绍了基于i XQuery框架的异构语义Web服务匹配器H-i Matcher。     

基于词汇相似度的元素级本体匹配

随着语义Web的不断发展，本体数量日益增加。本体匹配作为本体映射、比较和集成的基础，具有重要的实际意义。由第3届国际语义Web大会(3th ISWC)主办的本体匹配竞赛(EON2004)对多种本体匹配工具进行比较和评估。该文提出了一种元素级本体匹配算法LANA(Lexical Analyzer)，该算法通过计算词汇相似度得到两本体间的元素匹配对。与EON2004中的其它元素级本体匹配方法相比，LANA在没有增加匹配复杂度的基础上，具有较好的准确率和召回率。     

Automatic extraction of OWL ontologies from UML class diagrams: a semantics-preserving approach

Full implementation of the Semantic Web requires widespread availability of OWL ontologies. Manual ontology development using current OWL editors remains a tedious and cumbersome task that requires significant understanding of the new ontology language and can easily result in a knowledge acquisition bottleneck. On the other hand, abundant domain knowledge has been specified by existing database schemata such as UML class diagrams. Thus developing an automatic tool for extracting OWL ontologies from existing UML class diagrams is helpful to Web ontology development. In this paper we propose an automatic, semantics-preserving approach for extracting OWL ontologies from existing UML class diagrams. This approach establishes a precise conceptual correspondence between UML and OWL through a semantics-preserving schema translation algorithm. The experiments with our implemented prototype tool, UML2OWL, show that the proposed approach is effective and a fully automatic ontology extraction is achievable. The proposed approach and tool will facilitate the development of Web ontologies and the realization of semantic interoperations between existing Web database applications and the Semantic Web.     

从ER模式到OWL DL本体的语义保持的翻译

提出了一种从ER模式到OWL DL本体的语义保持的翻译方法.该方法在形式化表示ER模式的基础上,建立ER模式和OWL DL本体之间精确的概念对应,通过一个翻译算法按照一组预定义的映射规则实现模式翻译.理论分析表明,该方法是语义保持的和有效的;算法实现和案例研究进一步证实,完全自动的机器翻译是可实现的.该文方法是原创性的,为Web本体的开发以及数据库和语义Web之间语义互操作的实现开辟了一条有效途径.     

Web Authoring for Accessibility (WAfA)

Conventional thought from the Semantic Web community equates the use of ontologies with the representation of the meaning of content. Here, we skew this viewpoint by describing our ontology, Web Authoring for Accessibility (WAfA), which investigates the way ontologies can describe the semantic structure of documents. By understanding the way heterogeneous XHTML (Extensible Hypertext Mark-up Language) documents are structured we can better transform documents, currently inaccessible to visually impaired users. WAfA performs two tasks: (1) it allows us to flexibly model an XHTML document within the context of navigation and orientation through the Web resource; (2) it enables non-expert users to quickly annotate a Web document by providing a ‘lingua franca’ between author and Web Accessibility Domain Experts. Here we describe our ontology, its use, novelty, and importance.     

A framework for modeling and evaluating automatic semantic reconciliation

The introduction of the Semantic Web vision and the shift toward machine understandable Web resources has unearthed the importance of automatic semantic reconciliation. Consequently, new tools for automating the process were proposed. In this work we present a formal model of semantic reconciliation and analyze in a systematic manner the properties of the process outcome, primarily the inherent uncertainty of the matching process and how it reflects on the resulting mappings. An important feature of this research is the identification and analysis of factors that impact the effectiveness of algorithms for automatic semantic reconciliation, leading, it is hoped, to the design of better algorithms by reducing the uncertainty of existing algorithms. Against this background we empirically study the aptitude of two algorithms to correctly match concepts. This research is both timely and practical in light of recent attempts to develop and utilize methods for automatic semantic reconciliation.Received: 6 December 2002, Accepted: 15 September 2003, Published online: 19 December 2003Edited by: V. Atluri.     

Managing multiple and distributed ontologies on the Semantic Web

In traditional software systems, significant attention is devoted to keeping modules well separated and coherent with respect to functionality, thus ensuring that changes in the system are localized to a handful of modules. Reuse is seen as the key method in reaching that goal. Ontology-based systems on the Semantic Web are just a special class of software systems, so the same principles apply. In this article, we present an integrated framework for managing multiple and distributed ontologies on the Semant ic Web. It is based on the representation model for ontologies, trading off between expressivity and tractability. In our framework, we provide features for reusing existing ontologies and for evolving them while retaining the consistency. The approach is implemented within KAON, the Karlsruhe Ontology and Semantic Web tool suite.Received: 18 November 2002, Accepted: 16 April 2003, Published online: 17 September 2003Edited by: Edited by B.V. Atluri, A. Joshi, and Y. YeshaA. Maedche: maedche@fzi.de     

基于匿名消息广播的电子选举方案

针对目前基于语义网的本体映射算法中背景本体搜索面少、本体收集不精确的问题,利用基于虚拟文档的映射技术提取在Word-Net中与概念同义的同义词集,将对单个概念进行搜索转换成对同义概念集进行搜索,从而扩大本体搜索面,获取更多背景本体.提出基于语义环境的动态本体映射算法来排除错误背景本体,使本体收集更加精确.实验结果表明,该算法可有效提高映射的查全率和查准率.     

一种XML Schema和ontology间的语义映射算法

语义映射是信息集成的重要基础．提出了一种XML Schema和ontology间的语义映射算法,该算法引入了模式图模型，通过该模型计算概念闽的词义匹配度和概念上下文匹配度的加权来计算概念间的相似度，以判断概念是否匹配,实验结果表明，该算法是有效的，能够匹配XSD、WSDL文档和ontology的相应概念.     

Semantic Interoperability Aggregation in Service Requirements Refinement

Semantic refinement of stakeholders’ requirements is a fundamental issue in requirements engineering. Facing with the on-demand collaboration problem among the heterogeneous, autonomous, and dynamic service resources in the Web, service requirements refinement becomes extremely important, and the key issue in service requirements refinement is semantic interoperability aggregation. A method for creating connecting ontologies driven by requirement sign ontology is proposed. Based on connecting ontologies, a ...     

Building linked ontologies with high precision using subclass mapping discovery

The creation of links between schemas of published datasets is a key part of the Linked Open Data (LOD) paradigm. The ability to discover these links “on the go” requires that ontology matching techniques achieve good precision and recall within acceptable execution times. In this paper, we add similarity-based and mediator-based ontology matching methods to the Agreementmaker ontology matching system, which aim to efficiently discover high precision subclass mappings between LOD ontologies. Similarity-based matching methods discover subclass mappings by extrapolating them from a set of high quality equivalence mappings and from the interpretation of compound concept names. Mediator-based matching methods discover subclass mappings by comparing polysemic lexical annotations of ontology concepts and by considering external web ontologies. Experiments show that when compared with a leading LOD approach, Agreementmaker achieves considerably higher precision and F-measure, at the cost of a slight decrease in recall.     

Encoding Semantic Awareness in Resource-Constrained Devices

With the semantic Web relying on ontologies to establish online machine- interpretable information, the Internet is growing into a semantically aware computing paradigm that facilitates Web entities' discovery of the knowledge and resources they need. Ambient intelligence aims to enable smart interaction beyond the Internet by embedding intelligence into our environment to unobtrusively support users' daily activities. To accomplish these goals, ontologies and semantic awareness are crucial for better understanding a user's context. While interest in the Semantic Web has spurred the development of large-scale semantic grid architectures, expanding the Semantic Web to the other side of the computing spectrum is a complex undertaking. The techniques and tools that support the semantic Web aren't designed to deal with the resource-constrained devices with which people frequently interact in an ambient-intelligence environment. To counter this disadvantage, we developed a coding scheme for ontologies that embeds semantic awareness in devices with limited memory and processing capabilities, such as sensory nodes and smart phones. This scheme provides a compact representation of an ontology and is enhanced with an efficient and effective semantic-matching algorithm.