基于RFID普适计算环境的形式化上下文模型

提出了一个适合普适计算环境的、基于本体的形式化的上下文感知模型。该模型使用本体描述语言OWL描述上下文，采用层次化的设计方法，提高了上下文表达能力和快速上下文原型。为普适计算的上下文建模和应用、上下文重用和建立面向普适计算环境的软件基础设施提供了一种可行的方法。     

有上下文觉察能力的智能会议室系统模型的研究

通过利用多Agent、语义Web、上下文觉察和逻辑推理等技术，参考早期的普适计算系统，提出了一个新的智能会议室系统模型。该模型使用本体Web语言——OWL实现知识共享和上下文推理；使用逻辑推理检测和解决上下文知识的不一致问题，并向使用者提供一种策略语言来控制他们的私人信息。     

基于本体的上下文感知系统

普适计算自20世纪90年代中期被提出以后得到了普遍的关注。上下文感知作为实现普适计算的关键技术已成为重要的研究课题。在分析上下文感知系统以及组成模块的基础上，借鉴软件方法学中的快速原型模型思想，通过本体标记语言OWL给出了系统中感应模块搭建的实例。     

基于上下文推理的指挥所业务交互研究

基于指挥所操作业务的应用需求,定义适用于指挥所中人机交互的上下文信息。在选取该上下文信息的过程中,利用OWL语言完成指挥所业务上下文描述,建立交互信息上下文感知模型。运用SWRL逻辑规则对交互信息上下文进行推理,获得业务交互过程中高层上下文语义后进行自适应调整。以一个师指挥所作战指挥业务想定为例,说明计算机通过用户交互位置和上级指令上下文信息进行调整,从而满足当前用户的交互位置和交互业务。     

有上下文感知能力的智能教室系统设计与实现

目前,有许多教育专家采取很多措施来提高教学效率,而应用先进技术是一条切实可行的方法。通过利用多agent、语义Web、推理和用于安全和隐私的声明性规则等技术,参考早期的普遍深入的计算系统,开发了一个新的智能教室系统模型。该模型使用本体Web语言——OWL来实现知识共享和上下文推理,使用逻辑推理来检测和解决上下文知识的不一致问题,并向老师和学生提供一种策略语言来控制他们的私人信息。ICS将完全改变传统的教学方法并为未来的教学工作提供更多的便利。     

虚拟计算环境中的上下文建模与推理方法研究

上下文建模与推理是支持虚拟计算环境自适应调整的技术基础,基于本体的建模是其中一种重要的方法,但缺乏相关的定量分析。本文分析了不同的上下文建模方法,重点以基于OWL和XML的建模方法为代表,通过实验从推理效率、建模能力和易操作性三方面对比了基于本体的语义化方法和基于标记语言的非语义方法。     

一个基于本体的动态上下文模型(英文)

上下文建模是开发上下文感知系统的核心任务,它是应用理解和使用上下文的关键。按照生命周期的长短,上下文可以分为持久上下文和动态上下文,而后者可以进一步分为状态上下文和转换上下文。许多研究者在持久上下文和状态上下文建模方面做了大量的工作,但涉及到转换上下文的工作却很少。因此提出了一个采用OWL语言描述的基于本体的形式化的动态上下文模型,以及一个上下文融合和消费机制的实现。同时,为应用提供了一些编程原则并以一个自动门禁系统的演示说明了系统如何从这个模型中受益。     

基于觉察上下文计算的健康智能家庭研究

利用觉察上下文计算技术来研究实现健康智能家庭,主要研究了健康智能家庭的上下文建模和上下文推理,并构建了一个实验系统AngelHome,分析了健康智能家庭中的各种上下文信息,利用本体技术对其进行建模,并用OWLDL语言表达上下文信息模型,构建了AngelHome本体;在上下文推理部分采用混合推理,对不同的推理任务分别采用本体推理机、自定义规则推理机和贝叶斯神经网络推理.AngelHome采用OSGi框架,具有良好的伸缩性,这里分析了系统的几个主要部分,并进行了测试.实验结果表明,利用觉察上下文计算技术来实现健康智能家庭是可行的.     

基于端和云的大规模上下文管理框架的研究与实现

上下文态势是将大规模、广地域范围内的上下文信息综合在一起形成的一种全局信息.随着各类具备感知能力的移动终端的普及,如何获取这种全局态势并利用态势来为用户提供更好的服务是亟待解决的问题.基于“端+云”相结合的计算模式,提出移动终端的统一抽象模型来实现上下文信息收集,进而提出了在云端对大规模上下文信息进行聚合、基于MapReduce计算模型的态势信息荻取算法.通过一个大规模上下文管理框架对研究内容进行验证,并以一个交通态势实例验证了框架的有效性.     

面向上下文感知计算的贝叶斯网络结构自学习算法的研究

通过对上下文感知计算中上下文特点的详细分析,提出一种面向上下文感知计算的通用贝叶斯网络结构自学习方法。该方法能在足够实例数据的支撑下自动对上下文感知计算中上下文之间的关系进行学习,进而形成贝叶斯网络结构,用于从低层上下文向高层上下文的演化。通过对上下文感知计算中上下文的层次化特点的有效利用,该方法对贝叶斯网络自学习方法进行了有效优化。研究分析表明,该方法能显著降低贝叶斯网络学习过程中的时间复杂度。     

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.     

OWL rules: A proposal and prototype implementation

Although the OWL Web Ontology Language adds considerable expressive power to the Semantic Web it does have expressive limitations, particularly with respect to what can be said about properties. We present the Semantic Web Rule Language (SWRL), a Horn clause rules extension to OWL that overcomes many of these limitations. SWRL extends OWL in a syntactically and semantically coherent manner: the basic syntax for SWRL rules is an extension of the abstract syntax for OWL DL and OWL Lite; SWRL rules are given formal meaning via an extension of the OWL DL model-theoretic semantics; SWRL rules are given an XML syntax based on the OWL XML presentation syntax; and a mapping from SWRL rules to RDF graphs is given based on the OWL RDF/XML exchange syntax. We discuss the expressive power of SWRL, showing that the ontology consistency problem is undecidable, provide several examples of SWRL usage, and discuss a prototype implementation of reasoning support for SWRL.     

OWL DL的形式化基础研究

W3C正在制定的OWL DL是一种面向语义Web的知识表示标记语言，具有较强的知识表达能力并适合大规模应用的推理效率，是语义Web领域对适合语义Web应用的知识表示标记语言进行研究的最新综合．在知识表示领域，为了对知识表达能力和推理效率做合适的折衷，进行了大量关于描述逻辑的研究，本文以描述逻辑作为OWL DL的形式化基础，详细分析了OWL DL和描述逻辑间的对应关系，用描述逻辑的语义解释了OWL DL的各个基本元素，从而可以将描述逻辑领域研究的大量成果应用到OWL DL上来，为进一步研究OWL DL的知识表示、推理等问题奠定了基础．     

GrOWL: A tool for visualization and editing of OWL ontologies

In an effort to optimize visualization and editing of OWL ontologies we have developed GrOWL—a browser and visual editor for OWL that accurately visualizes the underlying DL semantics of OWL ontologies while avoiding the difficulties of the verbose OWL syntax. In this paper, we discuss GrOWL visualization model and the essential visualization techniques implemented in GrOWL.     

Modelling and Reasoning in Metamodelling Enabled Ontologies

Ontologies are expected to play an important role in many application domains, as well as in software engineering in general. One problem with using ontologies within software engineering is that while UML, a widely used standard for specifying and constructing the models for a software-intensive system, has a four-layer metamodelling architecture, the standard Web Ontology Language (OWL) does not support reasoning over layered metamodels. OWL 2 provides simple metamodelling by using a punning approach, however, the interpretation function is different based on the context, which leads to non-intuitive results. The OWL FA Language has a well defined metamodelling architecture. However, there is no study and tool for supporting reasoning over OWL FA. In this paper, we discuss some reasoning tasks in OWL FA. We also introduce the OWL FA Tool kit, a simple tool kit for manipulating and reasoning with OWL FA.     

Translating the Foundational Model of Anatomy into OWL

The Foundational Model of Anatomy (FMA) represents the result of manual and disciplined modeling of the structural organization of the human body. It is a tremendous resource in bioinformatics that facilitates sharing of information among applications that use anatomy knowledge. The FMA was developed in Protégé and the Protégé frames language is the canonical representation language for the FMA. We present a translation of the original Protégé frame representation of the FMA into OWL. Our effort is complementary to the earlier efforts to represent FMA in OWL and is focused on two main goals: (1) representing only the information that is explicitly present in the frames representation of the FMA or that can be directly inferred from the semantics of Protégé frames; (2) representing all the information that is present in the frames representation of the FMA, thus producing an OWL representation for the complete FMA. Our complete representation of the FMA in OWL consists of two components: an OWL DL component that contains the FMA constructs that are compatible with OWL DL; and an OWL Full component that imports the OWL DL component and adds the FMA constructs that OWL DL does not allow.     

ComR: a combined OWL reasoner for ontology classification

Ontology classification, the problem of computing the subsumption hierarchies for classes (atomic concepts), is a core reasoning service provided by Web Ontology Language (OWL) reasoners. Although general-purpose OWL 2 reasoners employ sophisticated optimizations for classification, they are still not efficient owing to the high complexity of tableau algorithms for expressive ontologies. Profile-specific OWL 2 EL reasoners are efficient; however, they become incomplete even if the ontology contains only a small number of axioms that are outside the OWL 2 EL fragment. In this paper, we present a technique that combines an OWL 2 EL reasoner with an OWL 2 reasoner for ontology classification of expressive SROIQ. To optimize the workload, we propose a task decomposition strategy for identifying the minimal non-EL subontology that contains only necessary axioms to ensure completeness. During the ontology classification, the bulk of the workload is delegated to an efficient OWL 2 EL reasoner and only the minimal non- EL subontology is handled by a less efficient OWL 2 reasoner. The proposed approach is implemented in a prototype ComR and experimental results show that our approach offers a substantial speedup in ontology classification. For the wellknown ontology NCI, the classification time is reduced by 96.9% (resp. 83.7%) compared against the standard reasoner Pellet (resp. the modular reasoner MORe).     

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.     

使用OWL DL形式化表达对象角色建模模型

对象角色建模(ORM)方法已应用于本体工程,因此需要将ORM模型转换为OWL DL公理,以便将ORM本体发布到语义Web上,同时还可使用支持DL的推理机来检查ORM本体的语义一致性和冗余问题。通过模型语义分析、模型等价转换、引入新的运算符和特性等方法,提出将ORM模型形式化表达为OWL DL公理的规则。除了外部唯一约束等四种约束外,其他形态的ORM模型都可以形式化表达为OWL DL公理。