语义集成:本体映射方法研究

本体是客观世界知识的表现形式,随着语义Web研究的深入,研究者们构建了越来越多的本体,如何实现本体之间的知识共享和重用,成为了语义Web发展的关键。文中对本体映射的方法进行了研究,系统阐述了本体及本体映射的定义、本体映射中的相似度计算和本体映射框架等。如何减少本体映射中的人工干预,实现本体的半自动化或自动化映射将是该领域的发展方向。     

语义集成:本体映射方法研究

本体是客观世界知识的表现形式，随着语义Web研究的深入．研究者们构建了越来越多的本体．如何实现本体之间的知识共享和重用，成为了语义Web发展的关键。文中对本体映射的方法进行了研究，系统阐述了本体及本体映射的定义、本体映射中的相似度计算和本体映射框架等。如何减少本体映射中的人工干预，实现本体的半自动化或自动化映射将是该领域的发展方向。     

本体协同演化冲突检测与消解方法研究

随着本体数量的不断增加和语义Web的发展,多用户本体协同操作成为发展的必然趋势,但是在共同协作的本体环境中总是充斥着各种冲突,所以协同演化冲突检测和消解是亟需解决的一个问题.提出了一个分类检测并消解演化冲突的结构化方法,根据演化冲突的复杂程度分为直接冲突、衍生冲突和不一致冲突,针对不同类型冲突的特点构建相应的协同演化冲突检测算法,最后通过判断演化冲突对中是否存在可删操作和寻求最大一致子序列进行冲突消解,使得多个本体演化意图并存,最终实现本体协同演化的目标.该算法在一定程度上提高了本体协同演化的效率,为开放环境下大规模本体演化的进一步发展奠定了基础.     

音乐领域本体的研究与构建

20世纪90年代初,本体概念被广泛地应用到计算机领域特别是人工智能领域(AI)和知识工程研究领域。本体作为一种能在语义和知识层次上描述信息系统概念模型,它已经成为知识工程、自然语言处理、协同信息系统、智能信息集成、Internet智能信息获取等方面普遍研究的热点。特别是语义Web的提出与发展,本体作为语义Web的核心使其受到了极大的重视。本文从音乐领域本体构建方面对音乐本体研究进行阐述。     

音乐领域本体的研究与构建

20世纪90年代初,本体概念被广泛地应用到计算机领域特别是人工智能领域(AI)和知识工程研究领域。本体作为一种能在语义和知识层次上描述信息系统概念模型,它已经成为知识工程、自然语言处理、协同信息系统、智能信息集成、Internet智能信息获取等方面普遍研究的热点。特别是语义Web的提出与发展,本体作为语义Web的核心使其受到了极大的重视。本文从音乐领域本体构建方面对音乐本体研究进行阐述。     

基于语义Web的常用软件领域知识发现系统研究

当前,在语义Web中,基于OWL的知识表示、知识推理成为了研究和应用的热点.给出了常用软件本体的设计方法、规则定义和谓词扩展,并介绍了常用软件领域知识发现系统的总体模型.     

基于OWL本体的语义Web知识表示

语义Web是对当前Web的一种扩充,它的发展正逐渐将Internet变成一个巨大的全球化的知识库,利用本体进行知识表示是实现知识管理、体现语义Web价值的一种有效途径.本文在研究OWL本体进行知识表示的机制后,阐述形式化表示及语义说明方法,并结合实例说明知识的表示.     

基于关系数据库的模糊本体的存储方法

模糊本体被广泛用来描述语义Web上的模糊信息。如何对模糊本体进行有效的存储,逐渐成为语义Web领域一个重要的研究课题。鉴于关系数据库在数据组织与管理方面的优势,提出基于关系数据库的模糊本体的存储方法。首先,通过对RDF数据类型进行模糊扩展,给出模糊本体中模糊数据类型的表示方法;然后,提出了基于关系数据库的模糊本体的存储模式以及模糊本体的结构和实例在关系数据库中的存储方法;最后,证明了该存储方法的正确性。基于关系数据库的模糊本体的存储方法为语义Web中模糊数据的存储和管理提供了一个有效的解决方案。     

基于本体的Web分类技术研究

主要提出了一种基于本体的抽象的Web挖掘模型。首先利用本体的方法表示出要挖掘的领域,然后把从用户处收集来的数据转换成表格;最后再根据定义和公式来进行知识发现。抽象的Web挖掘模型可以提取出语义Web中隐藏在大量信息背后的近似概念,来实现知识发现。     

一种基于语义的Web服务动态组合及其实现

随着Web Services 的广泛流行以及Web环境下日益增加的Web服务,如何根据用户的需求自动地对Web服务进行有效的组合已经成为一种挑战.文中提出了一种基于领域本体的动态组合方法,该方法利用领域本体以及语义技术首先生成一个基于领域本体的Web服务组合图,根据该Web服务组合图可以进行服务的自动发现、调用、组合.该方法综合考虑了服务的语义支持,领域特性,服务的自动组合.最后通过实例验证了该方法的有效性.     

Semantic oriented ontology cohesion metrics for ontology-based systems

Ontologies play a core role to provide shared knowledge models to semantic-driven applications targeted by Semantic Web. Ontology metrics become an important area because they can help ontology engineers to assess ontology and better control project management and development of ontology based systems, and therefore reduce the risk of project failures. In this paper, we propose a set of ontology cohesion metrics which focuses on measuring (possibly inconsistent) ontologies in the context of dynamic and changing Web. They are: Number of Ontology Partitions (NOP), Number of Minimally Inconsistent Subsets (NMIS) and Average Value of Axiom Inconsistencies (AVAI). These ontology metrics are used to measure ontological semantics rather than ontological structure. They are theoretically validated for ensuring their theoretical soundness, and further empirically validated by a standard test set of debugging ontologies. The related algorithms to compute these ontology metrics also are discussed. These metrics proposed in this paper can be used as a very useful complementarity of existing ontology cohesion metrics.     

Ontologies: How can They be Built?

Ontologies are an important component in many areas, such as knowledge management and organization, electronic commerce and information retrieval and extraction. Several methodologies for ontology building have been proposed. In this article, we provide an overview of ontology building. We start by characterizing the ontology building process and its life cycle. We present the most representative methodologies for building ontologies from scratch, and the proposed techniques, guidelines and methods to help in the construction task. We analyze and compare these methodologies. We describe current research issues in ontology reuse. Finally, we discuss the current trends in ontology building and its future challenges, namely, the new issues for building ontologies for the Semantic Web.     

A hybrid ontology approach for integration of obsolescence information

Information sharing among distributed obsolescence management systems is a challenge because of the heterogeneity of data (data with different forms and representations). Indeed, this is the main hurdle that exists for current tools managing product obsolescence. This paper presents a hybrid ontology approach for the integration of obsolescence information that combines a global ontology that provides a shared vocabulary for the specification of the semantics of obsolescence domain knowledge, along with local ontologies that describe structures of multiple data sources distributed in various obsolescence management tools. A procedure is provided for mapping local ontologies to the global ontology by quantifying relationships between classes and identifying groups of classes with a clustering method. Ontologies and rules of identifying relationships are realized with OWL (Web Ontology Language) and SWRL (Semantic Web Rule Language). With the application of the hybrid ontology approach, a unified view of data is provided to support decision making for efficient obsolescence management and a structure where new sources of information can be easily added with little modification in the future.     

基于Ontology的Web语言OIL及其应用

Ontology在网络信息交换中起着重大的作用，OIL是一种基于Ontology技术的Web环境下的领域模型语言。使用OIL定义的领域模型扩展了RDPS，可以实现在Web体系中的计算机可理解知识系统。将Onlology应用到Web上，就产生了语义化的Web。     

基于语义Web技术的上下文感知的智能移动服务

语义Web技术应用于上下文感知的智能移动服务，通过构建上下文信息本体，使得移动服务的实体之间可以进行上下文信息共享和语义互操作，并进行上下文信息推理，实现智能服务。本文首先介绍了语义Web及本体技术，其次阐述了语义Web技术应用于上下文感知的移动服务，然后详细分析了智能移动服务中的上下文信息本体构建，包括通用的上下文信息本体、用户概况本体、情境本体以及服务本体等，接着介绍了相关的研究项目，最后进行展望和总结。     

Learning concept hierarchies from textual resources for ontologies construction

Ontologies play a very important role in knowledge management and the Semantic Web, their use has been exploited in many current applications. Ontologies are especially useful because they support the exchange and sharing of information. Ontology learning from text is the process of deriving high-level concepts and their relations. An important task in ontology learning from text is to obtain a set of representative concepts to model a domain and organize them into a hierarchical structure (taxonomy) from unstructured information. In the process of building a taxonomy, the identification of hypernym/hyponym relations between terms is essential. How to automatically build the appropriate structure to represent the information contained in unstructured texts is a challenging task. This paper presents a novel method to obtain, from unstructured texts, representative concepts and their taxonomic relationships in a specific knowledge domain. This approach builds a concept hierarchy from a specific-domain corpus by using a clustering algorithm, a set of linguistic patterns, and additional contextual information extracted from the Web that improves the discovery of the most representative hypernym/hyponym relationships. A set of experiments were carried out using four different corpora. We evaluated the quality of the constructed taxonomies against gold standard ontologies, the experiments show promising results.     

基于语义Web的开放本体导航工具设计与实现

语义Web是分布式环境下构建复杂系统的一种新兴技术。针对独立开发的本体存在语义不匹配和逻辑不一致等问题，利用一种具有偏好的、公理半序的、模块化的本体表示语言OSHOQP（D），在开放本体需求分析的基础上，设计了基于语义Web技术的开放本体导航工具模型，使得本体能够并发地编辑和整合。系统由Wiki引擎、Agent、存储管理、本体管理器、导入／导出等模块构成。实现了基于语义Web技术的系统实验原型，为下一步研究开发较成熟的软件产品奠定了良好的基础。     

A software engineering approach to ontology building

Ontologies are the backbone of the Semantic Web, a semantic-aware version of the World Wide Web. The availability of large-scale high quality domain ontologies depends on effective and usable methodologies aimed at supporting the crucial process of ontology building. Ontology building exhibits a structural and logical complexity that is comparable to the production of software artefacts. This paper proposes an ontology building methodology that capitalizes the large experience drawn from a widely used standard in software engineering: the Unified Software Development Process or Unified Process (UP). In particular, we propose UP for ONtology (UPON) building, a methodology for ontology building derived from the UP. UPON is presented with the support of a practical example in the eBusiness domain. A comparative evaluation with other methodologies and the results of its adoption in the context of the Athena EU Integrated Project are also discussed.     

Semantic Web: A road to the knowledge infrastructure on the internet

In this article, I describe the basic technologies for Semantic Web and relationship between Semantic Web and Knowledge Representation in Artificial Intelligence. Semantic Web is planned as an extension of the current web in order to help cooperation between computers and humans, i.e., computers and humans are expected to understand each other in the knowledge level. I first describe the vision of the Semantic Web, then introduce the current Semantic Web technologies, i.e., RDF, RDFS, and OWL. I describe relationship between the trend of Semantic Web and Knowledge Representation, and clarify challenges and difficulties of Semantic Web from the point of view of Knowledge Representation. Hideaki Takeda: He is a professor at National Institute of Informatics (NII) and a professor in Department of Informatics at the Graduate University of Advanced Studies (Sokendai). He received his Ph.D. from the University of Tokyo in 1991. His research interest in computer science includes ontology engineering, community informatics and knowledge sharing systems.