信息网格中基于本体的Web服务动态集成和重构

基于语法的Web服务资源组织和管理策略不能满足信息网格中服务请求多样性和信息关联的需要.提出了一种基于本体的Web服务动态集成和重构策略(Web service dynamic integrating and constructing,简称WS-DIC).该策略以本体及其推理能力为核心,复用信息网格中已有服务,生成优化重构路径集合完成服务集成,满足请求多样性和信息关联的需要.通过对该策略的抽象和形式化描述,讨论了动态重构和集成规则,并设计了动态重构和集成算法.模拟实验表明,与传统的全文检索和数据库查询方式相比,该策略可以按照用户请求,通过服务重构集成,满足请求多样性并准确、全面地获取关联信息.     

面向业务协作的本体协同编辑系统

为提高本体编辑工具的群体协作支持功能,发挥本体编辑必需的群体智慧作用,针对当前本体编辑工具不能较好满足协同性需求的实际情况,将网格的技术理念应用到本体编辑系统的构建之中,提出基于资源动态集成与协同交互技术的网格化虚拟协同本体编辑系统,并介绍了系统的体系结构与关键技术.     

基于本体语义映射的数据集成机制研究

数据集成的难点是如何解决数据之间的语义异构问题,本文利用本体在语义集成上的优点,提出了一种基于本体语义映射的数据集成框架。根据本体概念的定义及其结构,给出了一种本体语义映射算法,该算法通过属性集合间的比较确定概念语义关系,在计算概念相似度时,考虑了概念名称、概念属性集合和相关概念的语义信息。最后通过概念的属性集映射算法和概念映射算法实现了本体语义映射,从而重点解决了数据集成中的语义映射问题。     

基于本体的区域医疗信息系统设计

对区域协同医疗信息系统平台的总体框架及在此框架TN集成技术进行研究,采用基于本体的方法分别从语义和结构2个方面进行数据集成,同时将数据仓库与虚拟数据库相结合,建立区域医疗数据中心,运用层次结构提高系统的稳定性,以实现面向服务的医院信息系统WebService集成,实验结果表明,该设计方案是有效可行的。     

基于本体的关系数据集成的查询处理

为实现异构关系数据库的语义集成,针对传统集成技术存在的问题,在对语义网等相关技术进行分析的基础上,研究基于本体的关系数据集成系统中的查询处理问题,提出了一种基于本体的关系数据库集成框架。设计了基于本体的关系数据的描述方法,使用本体作为集成的全局模式来描述关系模式的语义。设计了查询重写算法,该算法可以将基于全局模式的SPARQL查询重写为针对具体关系数据库的查询,从而实现对异构关系数据库的集成。实验表明,该算法具有良好的可扩展性。     

网格服务本体匹配算法研究

由于网格具有动态异构等特点,传统的基于属性的精确匹配方法不够灵活,并且扩展性差.将本体论的思想和方法引入到网格服务集成环境中,建立了基于本体的网格服务匹配系统结构,分析了基于语义上下文的领域匹配算法和领域内的服务匹配算法,从而实现了一种应用本体、基于语义的服务匹配机制.实验结果表明,提出的算法具有可接受的服务匹配时间、较高的匹配成功率和较强的获取网格服务的能力.     

网格环境下基于本体的信息检索体系研究

为了优化基于本体的信息检索的处理过程、提高应用系统的可靠性,提出了一种网格环境中基于本体的信息检索体系模型.利用Globus和OGSA-DAI工具进行计算资源和数据资源的管理,整合了闲置资源,提高了资源利用率,同时,将数据访问服务化,统一了接口访问类型.利用工作流模型管理业务流程的执行,实现了对数据的分布式部署,对业务服务的并行执行,能够在一定程度上解决因为信息量庞大、流程算法复杂带来的检索低效问题,提高系统的客错能力.     

基于混合本体方法的集成算法研究

本文提出了一种基于混合本体的集成算法。该方法充分利用了领域知识模型以及局部本体之间存在的语义相关性，从语义匹配的角度探讨了构造新的全局本体的可能。本文还重点讨论了n个局部本体如何构造全局本体的算法；最后介绍了一个本体构造系统，并结合项目给出了具体实例。     

基于数据流图本体的Web服务自动集成方法

提出一种基于本体的数据流图建模方法,能够为.服务请求动态集成Web服务.该方法利用本体建模及其可推理能力,通过对数据流图的描述实现领域业务逻辑建模,然后基于该描述给出了一个有效的服务集成推理算法,并着重分析基于该方法的软件体系设计.与现有的方法相比,该方法直接把软件工程分析成果应用到服务的自动地集成与部署中,同时保证了服务的可行性和质量.     

一种基于本体和Agent的网格服务发现机制

在动态的网格计算环境中分布着大量不同类型的网格服务。快速、准确地进行网格服务发现是影响网格计算性能的关键因素。本文提出了基于本体和Agent的网格服务发现机制,它具有良好的扩展性和完备性。该模型采用Agent代理技术进行服务搜索,不同的代理能够提供不同虚拟组织的本体知识库;利用本体论语义进行服务的匹配,从而实现智能化的服务发现。     

On ontology

A previously unpublished text by I. Kh. Shmain [On Ontology] written as a letter to V. B. Borshchev, a member of the editorial board of the collection NTI is presented below.     

本体中is-a层次的构建与本体集成

首先给出了本体中is-a层次的构建方法,并提出了is-a层次中删除概念的算法;其次,分析了本体集成的原因,给出了本体集成的分类、三种集成方式和四条集成原则;最后,提出一种基于OWL (Web ontology language)的本体集成算法,实验证明此算法可行.     

基于跨语本体重用的维语本体构建方法

针对维英本体共性知识的获取问题,提出一种基于跨语本体重用的快速构建维语领域本体方法。该方法将初始维语本体转换为英语本体,通过本体选择、映射和合并等过程对其丰富,达到一定阈值,转换为维语本体。提出了数据源势、本体势等概念和构建维语本体的数据模型。基于该方法构造了一个旅游领域本体实例,转换率达到78.8%,充分验证了该方法的可行性与有效性。     

基于本体翻译的领域本体自动构建

手工构建本体是一项既费时又费力的工作,为解决此项工作的瓶颈问题,本体自动构建成为当前的一个研究热点和重点。考虑到不同语言描述的本体在本质上是相同的,只是表层的表示符号不同,提出了一种基于本体翻译的领域本体自动构建算法,该算法针对已存在本体中概念标签的不同情况,分别采用不同的统计指标来筛选标签的目标语翻译。通过将一个英文金融本体翻译成中文对算法进行了实验验证,说明了算法的有效性。     

Supply chain management ontology from an ontology engineering perspective

Knowledge sharing and reuse are important factors affecting the performance of supply chains. These factors can be amplified in information systems by supply chain management (SCM) ontology. The literature provides various SCM ontologies for a range of industries and tasks. Although many studies make claims of the benefits of SCM ontology, it is unclear to what degree the development of these ontologies is informed by research outcomes from the ontology engineering field. This field has produced a set of specific engineering techniques, which are supposed to help developing quality ontologies. This article reports a study that assesses the adoption of ontology engineering techniques in 16 SCM ontologies. Based on these findings, several implications for research as well as SCM ontology adoption are articulated.     

本体中is a层次的构建与本体集成*

首先给出了本体中isa层次的构建方法,并提出了isa层次中删除概念的算法;其次,分析了本体集成的原因,给出了本体集成的分类、三种集成方式和四条集成原则;最后,提出一种基于OWL (Web ontology language)的本体集成算法,实验证明此算法可行。     

A K-way spectral partitioning of an ontology for ontology matching

Ontology matching, the process of resolving heterogeneity between two ontologies consumes a lot of computing memory and time. This problem is exacerbated in large ontology matching tasks. To address the problem of time and space complexity in the matching process, ontology partitioning has been adopted as one of the methods, however, most ontology partitioning algorithms either produce incomplete partitions or are slow in the partitioning process hence eroding the benefits of the partitioning. In this paper, we demonstrate that spectral partitioning of an ontology can generate high quality partitions geared towards ontology matching.     

A linguistic ontology

This paper defends the choice of a linguistically-based content ontology for natural language processing and demonstrates that a single common-sense ontology produces plausible interpretations at all levels from parsing through reasoning. The paper explores some of the problems and tradeoffs for a method which has just one content ontology. A linguistically-based content ontology represents the "world view" encoded in natural language. The content ontology (as opposed to the formal semantic ontology which distinguishes events from propositions, and so on) is best grounded in the culture, rather than in the world itself, or in the mind. By "world view" we mean naive assumptions about "what there is" in the world, and how it should be classified. These assumptions are time-worn and reflected in language at several levels: morphology, syntax and lexical semantics. The content ontology presented in the paper is part of a Naive Semantic lexicon, Naive Semantics is a lexical theory in which associated with each word sense is a naive theory (or set of beliefs) about the objects or events of reference. While naive semantic representations are not combinations of a closed set of primitives, they are also limited by a shallowness assumption. Included is just the information required to form a semantic interpretation incrementally, not all of the information known about objects. The Naive Semantic ontology is based upon a particular language, its syntax and its word senses. To the extent that other languages codify similar world views, we predict that their ontologies are similar. Applied in a computational natural language understanding system, this linguistically-motivated ontology (along with other native semantic information) is sufficient to disambiguate words, disambiguate syntactic structure, disambiguate formal semantic representations, resolve anaphoric expressions and perform reasoning tasks with text.     

Axiom-based ontology matching

Abstract: Managing multiple ontologies is now a core question in most of the applications that require semantic interoperability. The semantic web is surely the most significant application of this report: the current challenge is not to design, develop and deploy domain ontologies but to define semantic correspondences among multiple ontologies covering overlapping domains. In this paper, we introduce a new approach of ontology matching named axiom-based ontology matching. As this approach is founded on the use of axioms, it is mainly dedicated to heavyweight ontologies, but it can also be applied to lightweight ontologies as a complementary approach to the current techniques based on the analysis of natural language expressions, instances and/or taxonomical structures of ontologies. This new matching paradigm is defined in the context of the conceptual graphs model, where the projection (i.e. the main operator for reasoning with conceptual graphs which corresponds to homomorphism of graphs) is used as a means to semantically match the concepts and the relations of two ontologies through the explicit representation of the axioms in terms of conceptual graphs. We also introduce an ontology of representation, called MetaOCGL, dedicated to the reasoning of heavyweight ontologies at the meta-level.