图形化与知识形式企业模型间的相互转换

为有效地支持生产运营的知识共享和协同建模,提出一种在图形化形式与OWL本体形式企业模型间相互转换的方法。建立模型本体,图形化元模型对应于本体中的类,图形化模型元素对应于本体中类的实例。图形化模型元素之间的关联,对应于OWL本体中实例之间的对象属性。建模系统则作为一个建立模型本体实例的工具,将整个图形模型转化为OWL描述的本体实例。反之,通过解析OWL本体形式的模型,建模系统生成相应的图形化模型。     

基于HBase的领域本体存储方法及其应用研究

在分析民航突发事件应急管理领域本体及其存储特点的基础上,提出了一种基于HBase的领域本体存储方法,采用将领域本体元数据与RDF实例数据分开存储的方式,给出了描述领域本体类及属性信息的元数据和RDF实例数据的存储模型,及其基于MapReduce的领域本体RDF数据并行加载过程。结合应用实现了领域本体基于HBase API的基本图模式查询,并在Hadoop环境下进行了实验与效果分析,为民航应急管理领域本体的海量数据存储提供了理论与方法支撑。     

基于事件本体的突发事件领域知识建模

基于本体的知识建模方法有很多,在某些特定领域采用传统的本体建模方法存在着一些不足。以突发事件领域为例,提出了基于事件本体的知识建模方法。该模型分为上层事件类、下层事件类和事件实例,上层事件类描述的抽象的事件的分类体系,下层事件类是通过事件类关系组成的事件格结构。该模型不仅可以描述事件的时间、地点、对象等要素,还能描述事件类之间的关系。采用本体建模工具Protégé来构建突发事件领域本体,并以“恐怖袭击”作为实例验证了该模型的可用性。研究结果表明,该模型可以清晰地描述事件类的完整性,语义清晰,扩展性强。     

基于UML的本体开发模式研究

本体开发技术已经成为近年来国际国内研究的热点问题,但知识管理系统的开发需要大量软件工程技术人员的参与.为了尽快地使软件工程领域人员快速熟悉与开发本体,本文提出基于软件工程常用的基本工具--UML技术进行本体开发的新模式,从而有效地弥合人工智能与软件工程领域之间的鸿沟.重点提出了两个问题的解决方法:一是实现软件工程元模型与本体元模型之间的映射;二是实现UML描述本体向OWL本体的转换.最后通过一个实例验证了基于UML的本体开发模式.     

基于本体的分层抽象模型

KRA表示模型给出了对物理世界W进行形式化建模的一般框架。在扩展后的广义KRA模型中引入本体概念,将抽象对象库扩展为本体类,在本体类中实现KRA模型框架中的感知层、语言层和理论层的知识共享和重用,简化了KRA模型的表示。分别定义了作用在物理世界W和本体类上的3种本体抽象算子(集):基本本体抽象算子、实体本体抽象算子集和连接本体抽象算子集,并给出了本体类之间的映射关系,通过这种映射关系能够实现模型抽象和模型还原。对本体类的抽象度进行形式化的定义,并给出了相关定理,指出在这样的框架中利用抽象映射可以自动构建物理世界W的抽象度不同的模型。     

基于本体的城市交通的知识分析和推理

以城市交通为研究和应用背景,基于本体模型的知识表示技术,建立城市交通本体。对城市交通领域知识进行规范描述,详细描述了定义类层次及类的属性和关系,以及实例的基本规则,并从领域知识中提取出类内公理和类间公理,建立公理库,用于领域知识的一致性分析和知识推理。解决了语义层次上信息共享和交互的问题,为上海城市交通信息网格的交通信息服务提供语义支撑。     

UML在关系数据库设计中的应用

分析了UML建模和关系数据库设计的关系；说明了应用UML进行关系数据库设计的优点和基本过程；最后结合实例给出由UML类图映射到关系数据库的基本策略，从而实现对象的持久性存储。面向对象模型和关系模型紧密结合为信息系统的成功开发提供了良好的保障。     

一种本体概念实例操作方法研究

介绍在.NET集成开发环境下,通过XML DOM实现对本体实例数据加载,利用基于XML文档对象模型的编程接口,为就业系统本体的操作提供友好的人机交互界面。     

基于本体的特征模型演化的一致性验证

针对特征模型的演化导致特征间的冲突问题, 从特征模型的演化方面进行研究, 提出了一种基于本体的特征模型演化的一致性验证方法。首先采用本体理论方法对特征模型的演化进行分析和建模, 建立本体的特征模型元模型; 其次基于上述元模型, 为网上购物系统实例建立相应的网上购物的领域特征模型, 根据需求裁剪出产品特征模型; 然后在Eclipse集成开发环境下, 通过Jena推理机加载规则和产品特征模型进行一致性验证, 当检测到冲突时, 采用演化策略来消除冲突; 最后通过实例研究说明了该方法的有效性。     

一个基于本体的觉察上下文计算模型

觉察上下文计算（Context-Awareness Computing）中必然涉及到知识的共享，引入了本体融合（Ontology Fusion）概念，构建了一个基于本体的觉察上下文计算模型，讨论了该模型的基本框架，分析了上下文信息的获得，设计了上下文信息与本体库的映射过程，研究了本体融合、本体信息存储以及本体库与行为的映射中的相关问题。结合普适计算场景，用实例演示了该模型的运行过程。     

Multiagent Approach to Coreference Resolution Based on the Multifactor Similarity in Ontology Population

The multiagent approach to coreference resolution in the process of extracting information from texts in natural languages for ontology population is described. Special class agents corresponding to ontology classes are defined. They analyze the available information in the corresponding ontology instances. The results of this analysis are used for values to instance attribute, for detecting duplicates and equivalents of instances, for fixing coreferential relations, and for determining the weights of information connections used to resolve ambiguities. Coreferences are resolved taking into account a multifactor similarity measure between extracted objects that combines the semantic, context, positional, and grammar similarity measures. The class agents work within the multiagent approach to text analysis aimed at ontology population.     

Ontology-driven visualization system for semantic searching

Technical manuals are very diverse, ranging from software to commodities, general instructions and technical manuals that deal with specific domains such as mechanical maintenance. Due to the vast amount of documentation, finding the information is a tedious and time consuming task, especially for the mechanics. It is also difficult to grasp relationships among contents in manuals. Many researchers have adopted ontology to solve these problems and semantically represent contents of manuals. However, if ontology becomes very large and complex, it is not easy to work with ontology. Visualization has been an effective way to grasp and manipulate ontology. In this research, we propose a new ontology model to represent and retrieve contents from the manuals. We have also designed a visualization system based on our proposed ontology. In order to model the ontology, we have analyzed aircraft maintenance process, extracted the concepts and defined relationships between concepts. After modeling ontology schema, all instances of ontology are created by instance creator. From here, raw data of maintenance manuals are preprocessed to well-formed format. Next, we create a set of rule mapping well-formed document and ontology schema. For the Component class, instance creator uses a classifier to separate all parts into Component and Primitive part class. If population task is complete, validity of data for created instances will be checked by JENA engine. The inference process will create inferred triples based on the ontology schema, and then the triples are saved into a triple repository. Our system then will use this triples repository to search necessary information and visualize the search results. We use the Prefuse toolkit to visualize the search results. With this, the mechanics can intuitively grasp the relationship between maintenance manuals using the provided information. This will allow the mechanics to easily obtain information for given tasks, reduce their time to search related information and understand the information through visualization.     

语义Web本体匹配算法研究

随着语义Web不断发展,本体数量不断增加。由于不同领域专家构建的本体存在不匹配的问题,需要对本体进行匹配、合并和比较。本体匹配作为其他工作的基础,具有十分重要的意义。本文提出了一种基于实例的本体相似度计算方法,该方法为每个概念结点增加虚拟实例,并综合考虑了本体的结构特点,使用Word．Net调整相似度。我们把该方法运用到PROMPT本体匹配算法中,通过实验得到了较好的准确率和查全率。     

面向不同体型特征的服装款式迁移方法

大部分成衣的设计以标准比例的人模作为参照,而对于非标准体型的顾客,标码 服装的大小尺寸则很难与之进行有效匹配。基于此,提出了一种面向不同体型特征的服装款 式迁移方法。首先,对于一批不同款式的服装,通过物理模拟的方式穿着到标模及非标模人 体之上,形成标模试穿的服装实例及非标模试穿的服装实例;其次,使用仿射变换表示同款 服装在标模及非标模下服装实例间的变形映射,并借助主成分分析法求解服装变形,在保留 因体型特征导致的服装形变的基础上,剔除由服装款式信息引起的服装形变;最后,将服装 变形用于标模到非标模的服装款式迁移,并使用平均离散曲率衡量迁移前后服装款式的改变 程度。实验结果表明,迁移后的服装携带了标模服装的款式信息,并保留了非标模服装的体 型特征。     

浅析全球化视野下中国服装行业的调整与发展

世界经济已进入一个全球化竞争和发展的时代,服装行业必然也要参与其中。处于垄断竞争市场中的中国本土服装品牌想要在激烈的竞争中求生,需要经历一系列艰难的产业升级:明确产业结构的调整方向,利用自身优势资源分区域的实施结构调整的措施;加强纺织服装业的科技含量;借鉴国际先进的管理理念及营销模式,进一步扩大品牌的社会影响力和市场占有率,这样才能带来更高的销售额和国际认可度。     

IKARUS-Onto: a methodology to develop fuzzy ontologies from crisp ones

Fuzzy Ontologies comprise a relatively new knowledge representation paradigm that is being increasingly applied in application scenarios in which the treatment and utilization of vague or imprecise knowledge are important. However, the majority of research in the area has mostly focused on the development of conceptual formalisms for representing (and reasoning with) fuzzy ontologies, while the methodological issues entailed within the development process of such an ontology have been so far neglected. With that in mind, we present in this paper IKARUS-Onto, a comprehensive methodology for developing fuzzy ontologies from existing crisp ones that significantly enhances the effectiveness of the fuzzy ontology development process and the quality, in terms of accuracy, shareability and reusability, of the process’s output.     

业务本体开发与描述方法

业务本体是业务概念模型的形式化描述,对构建可重用的业务模型具有重要的价值。分析了现有本体开发方法对业务本体开发与描述的局限性,提出了面向活动的业务本体开发方法,阐述了基于UM-OCL的业务本体描述方法。     

Customizing 3D garments based on volumetric deformation

Improving the reusability of design results is very important for garment design industry, since designing an elegant garment is usually labor-intensive and time-consuming. In this paper, we present a new approach for customizing 3D garment models. Our approach can transfer garment models initially dressed on a reference human model onto a target human model. To achieve this goal, firstly a spatial mapping between the two human models is established with the shape constraints of cross-sections. Secondly, the space around the clothed reference human model is tetrahedralized into five tetrahedral meshes each of which either can be worked dependently with its adjacent ones or can be worked independently. The clothed reference human model is parametrically encoded in the tetrahedral meshes. Thirdly, these tetrahedral meshes are deformed by fitting the reference human model onto the target human model by using constrained volumetric graph Laplacian deformation. The updated garment models are finally decoded from the deformed tetrahedral meshes. As a result, the updated garment models are fitted onto the target human model. Experiments show that our approach performs very well and has the potential to be used in the garment design industry.     

Domain ontology graph model and its application in Chinese text classification

This paper proposes an ontology learning method which is used to generate a graphical ontology structure called ontology graph. The ontology graph defines the ontology and knowledge conceptualization model, and the ontology learning process defines the method of semiautomatic learning and generates ontology graphs from Chinese texts of different domains, the so-called domain ontology graph (DOG). Meanwhile, we also define two other ontological operations—document ontology graph generation and ontology graph-based text classification, which can be carried out with the generated DOG. This research focuses on Chinese text data, and furthermore, we conduct two experiments: the DOG generation and ontology graph-based text classification, with Chinese texts as the experimental data. The first experiment generates ten DOGs as the ontology graph instances to represent ten different domains of knowledge. The generated DOGs are then further used for the second experiment to provide performance evaluation. The ontology graph-based approach is able to achieve high text classification accuracy (with 92.3 % in f-measure) over other text classification approaches (such as 86.8 % in f-measure for tf–idf approach). The better performance in the comparative experiments reveals that the proposed ontology graph knowledge model, the ontology learning and generation process, and the ontological operations are feasible and effective.