A comprehensive framework for the evaluation of ontology modularization

The Semantic Web and ontologies have received increased attention in recent years. The delivery of well-designed ontologies enhances the effect of Semantic Web services, but building ontologies from scratch requires considerable time and effort. Modularizing ontologies and integrating ontology modules to a given context help users effectively develop ontologies and revitalize ontology dissemination. Therefore, various tools for modularizing ontologies have been developed. However, selecting an appropriate tool to fit a given context is difficult because the assumptions for the approaches greatly vary. Therefore, a suitable framework is required to compare and help screen the most suitable modularization tool.In this research, we propose a new evaluation framework for selecting an appropriate ontology modularization tool. We present three aspects of tool evaluation as the main dimensions for the assessment of modularization tools: tool performance, data performance, and usability.This study provides an implicit evaluation and an empirical analysis of three modularization tools. It also provides an evaluation method for ontology modularization, enabling ontology engineers to compare different modularization tools and easily choose an appropriate one for the production of qualifying ontology modules.The experimental results indicate that the proposed evaluation criteria for ontology modularization tools are valid and effective. This research provides a useful method for assessing and selecting ontology modularization tools. Modularization performance, data performance, and usability are the three modularization aspects designed and applied to the context of ontology. We provide a new focus on the comprehensive framework to evaluate the performance and usability of ontology modularization tools. The proposed framework should be of value to both ontology engineers, who are interested in ontology modularization, and to practitioners, who need information on how to evaluate and select a specific type of ontology tool in accordance with the requirements of the individual environment.     

OntoQualitas: A framework for ontology quality assessment in information interchanges between heterogeneous systems

Nowadays, Internet technologies and standards are being systematically used by enterprises as tools to provide an infrastructure to connect people, enterprises, and applications they are using. In such complex networked enterprises, it is increasingly challenging to interchange, share, and manage internal and external digital information. In this context, to achieve interoperability between information systems is a challenging task. In order to solve the interoperability problem at semantic level, several ontology-based approaches have emerged. Although methodologies, methods, techniques, and tools to support the ontology building process were proposed, there are no mature models to measure this process, and the quality of implemented ontologies remains a major concern. This paper presents a framework, OntoQualitas, for evaluating the quality of an ontology whose purpose is the information interchange between different contexts. OntoQualitas includes previous and new measures to evaluate the ontology considering its specific purpose. Additionally, an empirical validation of OntoQualitas is presented.     

一个基于语义模块的交互式本体匹配框架

本体匹配是用来解决异质本体间互操作问题的一种技术手段。目前,大多数关于本体匹配的研究都集中在了如何提高匹配结果的质量上。然而,一方面,在许多情况下,匹配结果的正确与否直接依赖于用户的判断,另一方面,由于一些描述现实世界的本体十分庞大,匹配工具往往不能及时为用户提供可供确认的匹配对。为此,提出了一种基于语义模块的交互式本体匹配框架。借助信息论的相关知识,将本体聚类成语义模块。用户利用模块核心结点信息对模块的内容进行推断,从而将大规模的本体匹配任务转换为数个规模较小的语义模块间的匹配任务。通过合理地增大用户在匹配过程中的作用,试图在保证匹配质量的同时提高匹配效率。已获得的实验结果表明该方法能显著提高本体匹配任务的效率。     

A framework and ontology for dynamic Web services selection

Current Web services standards lack the means for expressing a service's nonfunctional attributes - namely, its quality of service. QoS can be objective (encompassing reliability, availability, and request-to-response time) or subjective (focusing on user experience). QoS attributes are key to dynamically selecting the services that best meet user needs. This article addresses dynamic service selection via an agent framework coupled with a QoS ontology. With this approach, participants can collaborate to determine each other's service quality and trustworthiness.     

粗糙本体支持的知识推理框架

基于精确本体的推理机不能够直接应用到粗糙本体的知识推理中,因此目前还没有适合粗糙本体的推理机。根据粗糙本体的特点,将其中的粗糙集、粗糙描述逻辑、粗糙包含和知识推理作为研究对象,在此基础上将基于描述逻辑的推理方法与基于规则的推理方法相结合,提出一种粗糙本体支持的知识推理框架,实现了粗糙本体的推理功能,解决了针对不确定信息的知识推理问题。     

一个优化的本体自学习构建模型及其应用*

针对我国出口产品技术性贸易壁垒（TBT）信息获取和搜索的准确性、针对性、及时性的迫切需求,设计了面向TBT电子产品领域的本体自学习构建模型,给出了模型的体系结构,介绍了此框架主要模块的基本功能、实现的关键技术,以及构建中遇到的问题和对策。此模型可使计算机理解提取现有的信息描述方式,半自动化地高效率构建高质量的本体库,具有良好的实用性。将此模型应用于TBT电子产品本体建设领域,并证明此模型具有很好的通用性和可行性。     

分布式本体集成框架下虚拟本体集合动态生成算法

为了有效组织和管理网格环境内存在的各类分布异构的本体资源,提出了基于网关服务的分布式本体集成框架.从体系结构角度对本体集成过程中涉及到的功能模块进行分析和划分,并基于网格服务设计了关键组件.在此基础上从提高计算资源利用率和满足用户多样性需求的角度出发,提出了虚拟本体集合动态生成算法.该算法将缓存技术与虚拟本体相结合,为本体数据的自动生成和再生成提供了较完整的系统方法,增强了本体资源的复用性.实验结果表明,该算法可以提高计算资源利用率,实现本体资源的复用率.     

Towards an ontology based framework for searching multimedia contents on the web

Multimedia Tools and Applications - We live in a world where there are huge number of consumers and producers of multimedia content. In this sea of information, finding the right content is like...     

融合CBR和本体的决策支持系统框架及应用

分析了将本体与CBR方法结合开发智能决策支持系统的优点,设计了基于该方案的决策支持系统的总体框架,着重介绍了基于本体的案例组织和表示方法以及引入本体后案例检索过程中的相似度计算方法,基于该方案,设计和开发了电子政务项目审批决策支持系统。应用结果表明,利用本体对案例包含的知识进行提取和表示,再利用这些知识对案例进行组织和检索,有效地提高了案例检索的查准率和查全率。     

ADOL: a novel framework for automatic domain ontology learning

Ontology, as a semantic representation of a shared conceptualization, makes knowledge machine-readable and easy to spread. One of its typical applications is used to develop e-learning systems with Educational Ontology. Ontology can help students master knowledge architecture of required subjects and make scattered courseware more systematic. A big challenge is how to construct Educational Ontology to describe systematic knowledge of different subjects automatically. Currently, most of the ontologies are developed and extended manually, which requires the developers to possess certain professional knowledge and is time-consuming. In this paper, a framework to construct and extend Educational Ontology automatically is proposed.2 The proposed ontology learning framework, called ‘ADOL,’ can convert domain textbooks into a corresponding ontology automatically and efficiently. A case study on High School Physics shows that our approach is feasible and efficient.

     

Ontology versioning in an ontology management framework

Ontologies have become ubiquitous in information systems. They constitute the semantic Web's backbone, facilitate e-commerce, and serve such diverse application fields as bioinformatics and medicine. As ontology development becomes increasingly widespread and collaborative, developers are creating ontologies using different tools and different languages. These ontologies cover unrelated or overlapping domains at different levels of detail and granularity. A uniform framework, which we present here, helps users manage multiple ontologies by leveraging data and algorithms developed for one tool in another. For example, by using an algorithm we developed for structural evaluation of ontology versions, this framework lets developers compare different ontologies and map similarities and differences among them. Multiple-ontology management includes these tasks: maintain ontology libraries, import and reuse ontologies, translate ontologies from one formalism to another, support ontology versioning, specify transformation rules between different ontologies and version, merge ontologies, align and map between ontologies, extract an ontology's self-contained parts, support inference across multiple ontologies, support query across multiple ontologies.     

基于知识本体的语义信息检索框架设计

基于关键词匹配的信息检索方式不能反映出被检关键字在现实世界中的语义,因此这种检索方式不可避免地导致查准率和查全率低的缺陷,而概念检索中的主体词典表达领域知识的能力有限。提出了一个基于知识本体的能够实现语义信息检索的多主体系统,它包括描述信息收集、存储、语义匹配和语义相关性扩展等几个主要部分,可以在很大程度上提高检索结果的查准率和查全率。     

An ontology,intelligent agent-based framework for the provision of semantic web services

Intelligent agents and semantic web services are two technologies with great potential. Striking new applications can be developed by using the tools and techniques they provide. However, semantic web services need for an upper software entity able to deal with them and, on the other hand agent technology has historically suffered from a number of drawbacks that must be addressed. Integrating these two technologies in a joint environment can overcome their problems while strengthening their advantages. In this paper, the necessity for integrating these technologies and the potential benefits of their combination are analyzed. Based on this study, we present SEMMAS, an ontology-based framework for seamlessly integrating Intelligent Agents and Semantic Web Services. The basics of the framework are detailed and a proof-of-concept implementation described.     

Agent based intelligent search framework for product information using ontology mapping

The Semantic Web and Web services provide many opportunities in various applications such as product search and comparison in electronic commerce. We implemented an intelligent meta-search and recommendation system for products through consideration of multiple attributes by using ontology mapping and Web services. Under the assumption that each shopping site offers product ontology and product search service with Web services, we proposed a meta-search framework to configure a customer’s search intent, make and dispatch proper queries to each shopping site, evaluate search results from shopping sites, and show the customer the relevant product list with associated rankings. Ontology mapping is used for generating proper queries for shopping sites that have different product categories. We also implemented our framework and performed empirical evaluation of our approach with two leading shopping sites in the world.     

A multi-agent-based framework for cloud service discovery and selection using ontology

As cloud computing is getting matured day by day, there has been overwhelming interest among the users to avail a plethora of cloud services. Often, these services appear identical in terms of their functionality though they differ in pricing models, computational power, storage policies and Quality-of-Service parameters making the process of service discovery and selection an intricate task. In the absence of any standard specifications, cloud service providers continue to use their own vocabulary and this further complicates the selection process. Even popular search engines like Google and MSN are not efficient enough to properly identify the most appropriate cloud service that can meet customer requirements. Thus, in the presence of multiple selection parameters and constraints, selecting a required cloud service is a daunting task. In order to address this issue, we work toward developing a reasoning mechanism to optimally resolve the similarities across cloud services by using cloud ontology. A multi-agent-based framework has been proposed for effective cloud service discovery and selection with the help of a standardized service registry and by employing semantically guided searching process.