面向知识网格的本体学习研究

网格计算正在从单纯的面向大型计算的分布式资源共享发展为一种面向服务的架构,以实现透明而可靠的分布式系统集成。网格智能是指如何获取、预处理、表示和集成不同层次的网格服务(如HTML/XML/RDF/OWL文档、服务响应时间和服务质量等)的数据和信息,并最终转换为有用的智能(知识)。因为高层知识将在未来的网格应用起到越来越重要的作用,本体是知识网格实现的关键。文章提出了一种实现从Web文档中本体(半)自动构建的本体学习框架WebOntLearn,并讨论了本体学习中领域概念的抽取、概念之间关系的抽取和分类体系的自动构建等关键技术。     

基于OWL的模具企业经验知识库构建方法的研究

为了解决模具企业知识集成存在的问题,结合网络本体语言OWL(Web Ontology Language)所具有的优势.提出一种基于OWL的经验性知识库构建方法.该方法按照面向模具生产的知识分类框架建立概念树,使用Pmtégé建模工具建立基本的OWL本体库.同时采用Pellet本体推理机对OWL本体进行推理,并借助概念图帮助业务参与者在实际生产中扩展和完善OWL本体库.经验知识库的建立为模具企业知识集成系统的建立和知识重用打下基础,为模具设计和加工中经验性强的环节提供技术指导,从而提高模具的设计和生产效率,促进企业的可持续发展.     

基于多策略的异构数据库知识获取分析与实现

为对异构数据库中的大量孤立、没有语义描述的数据进行自动归类及本体建模,实现异构数据库数据的知识获取,提出了一个基于本体与Web服务的异构数据库知识获取框架,给出了通过Web服务包装异构数据库的访问机制,设计出贝叶斯分类器并应用该分类器对获取的异构数据自动映射到相关的本体.该方法能够通过贝叶斯分类器自动对异构数据归类,实现了异构数据库的交互知识获取.     

面向Web服务发现的本体知识库

本文探讨了面向Web服务发现的本体知识库的构建方法及其形式化描述。作为语义Web服务的发现框架中数据层和语义层,面向Web服务的本体知识库与一般的常识知识库不同,它既要表达领域本体的知识(本文涉及旅游领城知识),又涉及到Web服务的形式化描述以及个人兴趣模型的描述,以支持形式化的规范查询请求,并应用本体间的关系最终实现Web服务智能发现。本文通过一个Web服务发现实例说明此模型的应用价值,并与传统的本体知识库进行了对比。     

基于本体的模具企业知识集成系统研究*

针对模具企业的特点及其知识共享与重用的需求,提出了基于本体的知识集成系统,研究了基于本体的知识表达、规则推理和异构环境知识获取与重用等关键技术,实现了系统原型。该系统运用本体理论和方法对企业知识进行统一的描述与储存,采用面向服务架构实现了与知识源系统松散耦合的连接,并为企业用户和应用程序提供知识重用服务。该系统的研究与应用为模具全生命周期的知识管理提供了新的思路。     

语义网服务中的本体综述

作为实现下一代Web的集成方案,语义网服务将语义Web技术和Web服务技术相结合,以支持Web服务的自动发现、选择、合成和执行.本体是语义网服务的理论基础.介绍了Web资源描述本体OWL和Web服务描述本体的概况,并对3种主流的Web服务语义描述框架进行对比分析,为语义网服务中本体的创建提供基础知识.研究了语义网服务中存在的各层次的本体异构问题,明确了语义网服务中本体集成的主要研究方向.     

面向服务环境下基于本体映射的多领域业务过程协同*

为解决面向服务环境下多领域业务过程协同中的语义不一致问题,使不同领域提供的Web服务实现无障碍交互以及数据和知识共享,提出基于本体映射的多领域业务过程协同框架。首先根据领域知识建立参考本体,然后根据各领域的参考本体生成领域间的协同本体,最后将协同本体通过映射转换到BPEL和WSDL文件。该方法能够保留流程与Web服务中的原始语义信息,从而保证不同领域业务过程协同地顺利进行。原型系统在欧盟ImportNET项目中得到了验证。     

Agent与Web服务的集成研究

介绍Web服务和本体的概念,提出一个集成Agent和Web服务的基于本体的智能化Web服务（IWS）框架,通过SOAP协议实现两个Agent之间的KQML通信。     

基于本体映射的语义Web服务发现框架

分析了语义Web服务中存在的本体异构问题,讨论了语义Web服务发现中输入、输出匹配的方法,以及本体映射中概念相似度计算方法.在已有的Web服务模型基础上,引入语义Web服务发现的机制,提出了一个基于本体映射的语义Web服务发现框架,并阐述了其实现机制.该框架下的本体映射技术的应用能够增强语义Web服务发现的能力.     

一种基于本体的分布式面向服务的体系结构风格

语义Web服务,作为本体技术与面向服务的体系架构的完美结合,已经成为语义Web和面向服务的软件工程的一个重要研究方向。OWL-S和WSMF等语义Web服务方法,为Web服务描述标准（WSDL）,提供了基于本体的框架,从而实现服务的自动化发现、调用和组合。但基于原有语义Web服务方法,处理组合服务时,对子服务构件之间通信的建模缺乏实际意义上的语义。基于本体和P2P技术,建立一套通用的分布式的面向服务的体系结构——DisOntoSOA。DisOntoSOA不局限于任何一种编程语言和任何一种特定的Web服务技术。因此,只要是结合本体与SOA的方法,都适用于该体系。     

基于KBE的模具型腔设计系统

为了解决模具型腔设计过程中信息集成和知识经验共享问题,提出将基于知识的工程（knowledge basedengineering,KBE）技术引入模具的型腔设计过程,并构造了基于KBE的模具型腔设计系统,其中充分采用了特征技术和知识工程技术.特征技术主要解决模具型腔设计各阶段的信息集成问题,而知识工程技术则主要解决模具型腔设计过程中的知识共享问题.研究结果表明,通过在模具型腔设计过程中引入KBE技术,可以有效地提高模具型腔设计的设计效率,加快模具开发的进程.     

An ontology-based knowledge framework for engineering material selection

Engineering material selection intensively depends on domain knowledge. In the face of the large number and wide variety of engineering materials, it is very necessary to research and develop an open, shared, and scalable knowledge framework for implementing domain-oriented and knowledge-based material selection. In this paper, the fundamental concepts and relationships involved in all aspects of material selection are analyzed in detail. A novel ontology-based knowledge framework is presented. The ontology-based Semantic Web technology is introduced into the semantic representation of material selection knowledge. The implicit material selection knowledge is represented as a set of labeled instances and RDF instance graphs in terms of the concept model, which provides a formal approach to organizing the captured material selection knowledge. A knowledge retrieval and reasoning approach integrating ontology concepts, instances, knowledge rules, and semantic queries encoded with Query-enhanced Web Rule Language (SQWRL) is proposed. The presented knowledge framework can provide powerful knowledge services for material selection. Finally, based on this knowledge framework, a case study on constructing a mold material selection knowledge system is provided. This work is a new attempt to build an open and shared knowledge framework for engineering material selection.     

Concept maps as the first step in an ontology construction method

A method is proposed to be used as the first step in the ontology acquisition process. This method is based on the use of concept maps as a means of expression for the expert, followed by an application that assists the expert in detailing the structure of the knowledge represented in the map. This application analyses the concept map, taking into account the map topology and key words used by the expert. From this analysis a series of questions are presented to the expert that, when answered, reduce the map ambiguity and identify some common patterns in ontological representations, such as generalizations and mereologic relations. This information can be used by the knowledge engineer during further knowledge acquisition sessions or to direct the expert to a further improvement of the map. The method was tested by a group of volunteers, most of them engineers working at the aerospace sector, and the results suggest that both the use of concept mapping as well as the refining step are acceptable from the point of view of the end user, supporting the claim that this method is viable as an option to reduce some of the difficulties in large scale ontology construction.     

军事领域本体构建研究

在仿真系统的研究过程中,面临军事领域知识获取及不同应用之间的互操作等问题.这些难题的背后实际是技术人员和军事人员之间理解一致性和机器之间理解一致性的问题.本体是关于事物本质的模型,已被广泛应用于知识分类、表达、共享及重用等方面.首先,讨论了本体的概念并给出了领域本体构建的一套通用原则和方法.然后详细分析了军事领域本体所包含的各方面知识元素,包括作战单元类、单元之间的关系、交战行为、交战规则、武器装备、作战计划等,并分别给出了它们的简单表示结构.最后概要讨论了多个本体合并时的本体检验问题.标准化的军事知识表示结构可以为技术人员和军事人员之间的交流提供统一的认识和参照,为机器之间的互操作提供一种"语言",从而消除理解上的冲突和混乱,为作战仿真系统的开发扫清了道路.     

基于Ontology知识库系统建模

根据KADS的知识模型探讨了领域知识库系统构建的分层模型,并基于Ontology对知识库进行了设计。通过C++和ASP技术对学习系统中的部分模块进行了实现。最后给出了原型系统的运行界面。     

Ontology learning: state of the art and open issues

Ontology is one of the fundamental cornerstones of the semantic Web. The pervasive use of ontologies in information sharing and knowledge management calls for efficient and effective approaches to ontology development. Ontology learning, which seeks to discover ontological knowledge from various forms of data automatically or semi-automatically, can overcome the bottleneck of ontology acquisition in ontology development. Despite the significant progress in ontology learning research over the past decade, there remain a number of open problems in this field. This paper provides a comprehensive review and discussion of major issues, challenges, and opportunities in ontology learning. We propose a new learning-oriented model for ontology development and a framework for ontology learning. Moreover, we identify and discuss important dimensions for classifying ontology learning approaches and techniques. In light of the impact of domain on choosing ontology learning approaches, we summarize domain characteristics that can facilitate future ontology learning effort. The paper offers a road map and a variety of insights about this fast-growing field.     

大学水平的“数学分析知识”的获取和分析研究

在过去的几十年里,数学软件系统变得越来越强大和复杂.各种数学系统的集成变得非常有意义.基于Web的分布式数学平台使得与数学相关的活动（称之为数学服务）都可以在Web上得以实现.所有这些都需要一个底层的、可共享的、面向内容的知识库作为支撑.本文介绍一个大学水平的、可共享的数学分析知识库的获取和分析方法.NKIMath采用基于本体、框架、逻辑和类型的形式表示,采用面向概念的知识获取方法,从一套大学本科数学分析课本（2本）中获取了所有主要概念和定理.本文还总结和分析了知识获取过程中出现的错误.     

Classification knowledge discovery in mold tooling test using decision tree algorithm

The scale of Taiwan’s mold industry was ranked the sixth in the world. But, under the global competitive pressure, Taiwan has lost its competitive advantage gradually. The only chance of Taiwan’s mold industry lies in improving the competitive abilities in product research, development and design. In mold manufacturing cycle, mold tooling test plays a very important role at accelerating the speed of production. An experienced engineer can minimize the error rate of mold tooling test according to his rich experiences in parameter adjustment. However, this experience is mostly implicit without theoretical basis and its knowledge is difficult to be transmitted. Benefiting from the well development of data mining technologies, this study aimed at constructing an intelligent classification knowledge discovery system for mold tooling test based on decision tree algorithm, so as to explore and accumulate the experimental knowledge for the use of Taiwan’s mold industry. This study took the only high-alloy steel manufacturer in Taiwan for case study, and performed system validation with 66 record data. The results showed the accuracy rates of prediction of training data and testing data are 97.6 and 86.9%, respectively. In addition, this study explored two classification knowledge rules and proposed concrete proposals for tooling test parameter adjustment. Moreover, this study provided two ways, rule verification and effectiveness comparison of four mining algorithms, to conduct model verification. The experimental results showed the decision tree algorithm has an excellent discriminatory power of classification and is able to provide clear and simple reference rules for decisions.     

Distributed fuzzy case based reasoning

This paper presents a framework for a distributed knowledge based system by integrating case based reasoning (CBR) and Fuzzy Logic. Fuzzy Logic gives CBR the power to deal with impreciseness and uncertainty. The framework for handling distributed case bases enables our system to construct solution based on collective experience distributed by discipline, time, and geography. In the proposed system the cases can be expressed in terms of attributes that can be crisp as well as fuzzy and appropriately similarity scores are computed. The cases can have attributes from a vocabulary, which can be defined with the constraint of global commitments so that the attributes can be shared and interpreted in a distributed setting. We have implemented a knowledge sharing protocol with common ontology as the repository of exchange vocabulary for knowledge sources with different Universe of Discourses (UOD). We have developed a shell for tailored application development in different domains. We have used RDBMS as the back end repository for cases, DAML + OIL for Ontology design, SAX and DOM for ontology access and RMI for remote procedure call. We have illustrated effectiveness of our approach by developing a travel planning and a help desk application.     

Situated cognition and knowledge acquisition research

In the early years of knowledge acquisition research there was considerable discussion about the possibility of obtaining knowledge from experts, based on the ideas of situated cognition. The central idea from situated cognition was that knowledge is only ever created in a context and so cannot be used reliably out of context. These ideas seem to have had little impact on most of the knowledge acquisition research that followed, probably because they seemed to be too negative about the possibilities for knowledge-based systems. This paper suggests that situated cognition can be reinterpreted as making the positive suggestion that if people distinguish between different conclusions in different contexts, they do so because they can identify features that distinguish the contexts. This type of case or context differentiation could be readily integrated with a range of knowledge acquisition frameworks, and experience with Ripple-Down Rules suggests that it provides a very simple way for domain experts and others to easily provide a large amount of knowledge for a system.