OWL 2: The next step for OWL

Since achieving W3C recommendation status in 2004, the Web Ontology Language (OWL) has been successfully applied to many problems in computer science. Practical experience with OWL has been quite positive in general; however, it has also revealed room for improvement in several areas. We systematically analyze the identified shortcomings of OWL, such as expressivity issues, problems with its syntaxes, and deficiencies in the definition of OWL species. Furthermore, we present an overview of OWL 2—an extension to and revision of OWL that is currently being developed within the W3C OWL Working Group. Many aspects of OWL have been thoroughly reengineered in OWL 2, thus producing a robust platform for future development of the language.     

Web本体语言OWL

随着对语义Web研究的深入，人们越来越关注在Web上信息内容的表示和检索问题。在语义Web的七层模型中，建立在RDF模式层之上的本体语言层用于形式化描述Web文档中词汇的含义，是建立语义Web的关键环节。该文介绍了W3C最新给出的Web本体语言OWL，给出TOWL3个子语言构造成分的含义说明，并比较了3种子语言间的差别。     

基于路径的网络本体语言存储模型

为提高信息检索效率,提出基于路径的网络本体语言(OWL)存储模型,首先设计了转换和存储OWL数据的方法,实现构建包含有类和属性层次结构关系的数据图,然后通过深度优先搜索(DFS)算法建立从根节点的类和属性信息到每个节点的类和属性信息的路径,再将这些信息存储到设计的关系数据库表中。通过实验与现有方法进行了比较,在查询处理时间和本体更新时间性能方面都有改进,方案具有可行性。     

A Rule-Based Object-Oriented OWL Reasoner

In this paper, we describe O-DEVICE, a memory-based knowledge-based system for reasoning and querying OWL ontologies by implementing RDF/OWL entailments in the form of production rules in order to apply the formal semantics of the language. Our approach is based on a transformation procedure of OWL ontologies into an object-oriented schema and the application of inference production rules over the generated objects in order to implement the various semantics of OWL. In order to enhance the performance of the system, we introduce a dynamic approach of generating production rules for ABOX reasoning and an incremental approach of loading ontologies. O-DEVICE is built over the CLIPS production rule system, using the object-oriented language COOL to model and handle ontology concepts and RDF resources. One of the contributions of our work is that we enable a well-known and efficient production rule system to handle OWL ontologies. We argue that although native OWL rule reasoners may process ontology information faster, they lack some of the key features that rule systems offer, such as the efficient manipulation of the information through complex rule programs. We present a comparison of our system with other OWL reasoners, showing that O-DEVICE can constitute a practical rule environment for ontology manipulation.     

Verifying feature models using OWL

Feature models are widely used in domain engineering to capture common and variant features among systems in a particular domain. However, the lack of a formal semantics and reasoning support of feature models has hindered the development of this area. Industrial experiences also show that methods and tools that can support feature model analysis are badly appreciated. Such reasoning tool should be fully automated and efficient. At the same time, the reasoning tool should scale up well since it may need to handle hundreds or even thousands of features a that modern software systems may have. This paper presents an approach to modeling and verifying feature diagrams using Semantic Web OWL ontologies. We use OWL DL ontologies to precisely capture the inter-relationships among the features in a feature diagram. OWL reasoning engines such as FaCT++ are deployed to check for the inconsistencies of feature configurations fully automatically. Furthermore, a general OWL debugger has been developed to tackle the disadvantage of lacking debugging aids for the current OWL reasoner and to complement our verification approach. We also developed a CASE tool to facilitate visual development, interchange and reasoning of feature diagrams in the Semantic Web environment.     

基于OWL的本体集成

提出一种新的本体集成方法。分析了本体集成的原因,阐述了本体集成时应遵循的4条基本原则,并给出了集成的分类,提出了一种基于OWL DL图闭包的本体集成方法。该方法将OWL DL本体抽象为RDFS图模型,根据给定的OWL DL推理规则生成OWL DL本体的图闭包,在此基础上进行本体集成,同时提出了几种计算实体相似度的方法,将本方法与COMA++和FCA-merge进行实验对比,本方法在准确率和召回率上占优势。     

ORM模型转换为OWL 2公理的映射规则

对象角色建模方法ORM目前已发展成为一种本体工程方法。需要将ORM表达的领域知识发布到语义Web上,以供不同应用系统共享和交换。OWL 2是W3C推荐使用的语义Web本体语言。探讨了将ORM模型映射为OWL 2公理的方法,通过模型等价变换和引入新的公理,给出了所有可行的模型映射规则。     

一种OWL本体进化方法*

针对从关系数据库模式学习所得的OWL本体大都是轻量级的,其概念层次结构过于扁平,很难被直接用于实际的本体应用,提出一种新颖的OWL本体进化方法。其通过形式概念分析对已有轻量级OWL本体进行概念聚类,根据概念等同度、概念包含度计算,自动提出丰富、修改本体概念语义关系的建议,从而辅助设计者实现本体进化。该方法将FCA与相似度计算结合使用,既发挥FCA语义强度较高的特点,又发挥相似度计算执行效率高且容易实现的特点;同时,规避了相似度计算语义强度较低与FCA实现较为困难且执行效率较低的不足。一个实例结果的评估证实     

基于OWL的软件需求获取

基于网络本体语言OWL支持软件需求获取过程，通过OWL语言建立领域知识的本体，获取该领域的共享知识，并基于该领域知识引导需求涉众获取软件需求。该需求获取方式更加系统，并具有高可复用性和精确性等特点。     

基于本体语言OWL的模糊扩展

本体能够对特定领域的概念、术语以及关系提供一种形式化的描述方法.尽管本体在知识表示上有很强的能力,但是有一个缺陷,即不能表达不确定和不精确的信息.而这些信息在语义网和多媒体应用中,又是至关重要的.针对模糊信息的本体表示问题,本文对本体语言OWL DL进行了基于模糊逻辑的扩展,给出了形式化的语法和语义,并通过一个实例说明了该方法在表达能力上的灵活性. .     

基于OWL的软件工程数据建模

网络本体语言(Web ontology language,OWL)是语义网技术的一个重要组成部分,适合于对复杂的数据进行语义描述和建模.在软件系统的开发过程中通常会产生大量结构复杂、语义丰富的数据,而建立一个灵活的语义模型是对各类软件工程数据进行统一管理的基础.从设计和实现海量软件工程数据管理平台的需求出发,提出了一种基于OWL的软件工程数据描述模型.该模型不仅能够对源代码、需求、测试、版本和缺陷数据进行描述,同时还能对这些数据之问的语义关联进行描述.通过案例分析对模型的有效性进行了讨论.     

OWL的本体推理方法研究

针对RDFS与OWL语言之间的兼容性问题,从本体推理机的角度研究了扩展RDFS推理机支持OWL语义的两种方法。在Sesame系统的基础上,通过规则扩展的方法实现了升级方案对RDFS和OWL语言双重支持功能。实验测试表明,扩展后的本体推理机完全支持RDFS语言,其推理能力也大大超过了单纯的OWL语言推理机。     

基于OWL本体的UML类图推理

分析UML模型一致性检测的研究意义,以及UML类图推理的研究现状,提出一种基于OWL本体的UML类图推理方案.研究UML类图向OWL本体转换的映射规则,给出UML类图包含关系、可满足性、等价关系和相离关系的判断方法及其推理规则.利用Jena推理机制实现了UML类图包含关系、可满足性、等价关系和相离关系的推理.     

OWL本体构建方法的研究

通过分析语义网的特点和对本体构建方法以及本体表示语言的要求,提出了一种新的使用OWL构建本体的方法.该方法分4步详细论述了构建本体的过程,解决了现实领域知识到计算机可以理解的形式化编码的转化问题.最后采用该方法构建食物链本体验证了该方法的有效性.