Expressing inter-link constraints in OWL knowledge bases

Abstract: In this paper the Web Ontology Language (OWL) is examined to instantiate expert system knowledge bases intended for semantic Web applications. In particular, OWL is analyzed for expressing Unified Modeling Language (UML) representations that have been augmented with propositional logic asserted as inter‐link constraints. The motivation is ultimately to provide declarative propositional logic constraints that can be represented in UML and declaratively implemented using OWL and other constructs to realize semantic Web knowledge base repositories and databases to facilitate expert system applications. The results of this paper show that OWL is sufficient for capturing most inter‐link constraints asserted on generalization/specialization instances; however, OWL alone is inadequate for representing some inter‐link constraints asserted on associations. We propose enhancements to OWL via RDF extensions for the reification of associations into classes. These extensions mitigate all concerns that were identified in OWL as part of this study. The result is increased support of declarative constraint representations, which can be expressed in knowledge bases in the context of the semantic Web.     

基于路径的网络本体语言存储模型

为提高信息检索效率,提出基于路径的网络本体语言(OWL)存储模型,首先设计了转换和存储OWL数据的方法,实现构建包含有类和属性层次结构关系的数据图,然后通过深度优先搜索(DFS)算法建立从根节点的类和属性信息到每个节点的类和属性信息的路径,再将这些信息存储到设计的关系数据库表中。通过实验与现有方法进行了比较,在查询处理时间和本体更新时间性能方面都有改进,方案具有可行性。     

Using the CARMEN's framework to model and acquire knowledge

The manner in which CARMEN (Constraints And Rules Management ENgine), an operational expert system generator developed as part of the ESPRIT project ITHACA, models and acquires knowledge is discussed. Central to CARMEN are three types of control entities (TASKs, MKSs and BKSs) which are used to describe problem-solving at the knowledge-use level. Modeling of domain knowledge is done both at the deep and surface levels. The Integrity Checking Task (or ICT) is used to illustrate the manner in which CARMEN works. We conclude the paper with a comparison of CARMEN with other approaches in knowledge modeling.     

基于OWL的软件工程数据建模

网络本体语言(Web ontology language,OWL)是语义网技术的一个重要组成部分,适合于对复杂的数据进行语义描述和建模.在软件系统的开发过程中通常会产生大量结构复杂、语义丰富的数据,而建立一个灵活的语义模型是对各类软件工程数据进行统一管理的基础.从设计和实现海量软件工程数据管理平台的需求出发,提出了一种基于OWL的软件工程数据描述模型.该模型不仅能够对源代码、需求、测试、版本和缺陷数据进行描述,同时还能对这些数据之问的语义关联进行描述.通过案例分析对模型的有效性进行了讨论.     

LUBM: A benchmark for OWL knowledge base systems

We describe our method for benchmarking Semantic Web knowledge base systems with respect to use in large OWL applications. We present the Lehigh University Benchmark (LUBM) as an example of how to design such benchmarks. The LUBM features an ontology for the university domain, synthetic OWL data scalable to an arbitrary size, 14 extensional queries representing a variety of properties, and several performance metrics. The LUBM can be used to evaluate systems with different reasoning capabilities and storage mechanisms. We demonstrate this with an evaluation of two memory-based systems and two systems with persistent storage.     

Using a simulation model for knowledge elicitation and knowledge management

The work reported in this paper is part of a project simulating maintenance operations in an automotive engine production facility. The decisions made by the people in charge of these operations form a crucial element of this simulation. Eliciting this knowledge is problematic. One approach is to use the simulation model as part of the knowledge elicitation process.This paper reports on the experience so far with using a simulation model to support knowledge management in this way. Issues are discussed regarding the data available, the use of the model, and the elicitation process itself.     

基于OWL的企业网络知识管理系统实现

为了减少传统网络知识管理系统检索时存在的大量无关知识，提出了运用本体描述语言OWL构造企业网络竺孽亏理系统的方法。该系统利用了OWL本体描述语言，采用了面向对象的构建方法，改变了以往企业网络知识管理系统架构由设计者确定的模式，构建了知识之间的联系。在此基础之上，讨论了利用本体描述语言增强企业网络知识管理系统的语义性。最后设计了一个企业网络知识管理实验系统，并给出了基于OWL的本体实例。     

基于角色的对OWL本体的访问控制模型

本体是对共享概念明确的形式化规范说明,是语义Web实现的关键技术。当前语义Web缺乏对本体有效的访问控制手段,因此本体的发布必然会导致相关领域敏感信息的泄露。提出了一个基于角色的OWL本体访问控制模型,该模型充分利用了本体元素之间的语义关联性,对传统的RBAC模型进行了扩展,能够对OWL本体以及本体元素的访问实施有效地控制,同时也解决了OWL本体访问控制中推理泄露的问题。     

一种利用模糊关系数据库构建模糊OWL本体的方法

经典OWL本体不能直接表示和处理语义Web应用中广泛存在的模糊知识,鉴于模糊关系数据库在模糊数据表示与处理方面的优势,提出利用模糊关系数据库来构建模糊OWL本体.通过对RDF数据类型进行模糊扩展,并从模糊数据类型角度扩展OWL,解决了模糊OWL本体的表示问题;在此基础上,研究了以模糊关系数据库为数据源的模糊OWL本体的构建方法,该方法为语义Web中模糊本体的构建提供了一个有效的解决方案.     

基于OWL的鱼病诊断本体模型

由语义不一致性导致的知识库难以复用与共享是影响专家系统开发与应用的关键问题.针对鱼病诊断专家系统中诊断知识的特点,根据鱼病诊断知识模型,分析了鱼病诊断知识模式,定义了鱼病诊断知识基本单元,构建了鱼病诊断知识本体模型,并在此基础上采用OWL语言对其进行了形式化描述,为促进鱼病诊断知识共享、复用提供参考.     

Using knowledge to control tree searching

PARADISE (PAttern Recognition Applied to DIrecting SEarch) uses a knowledge-based analysis and little searching to find the correct move in chess middle game positions. PARADISE's search does not have a depth limit or any other artificial effort limit. This paper describes the methods used to constrain the search. The ideas of using different strategies to show that one move is best and using ranges to express the values of moves (first developed in Berliner's B1 search), are extended and clarified. PARADISE combines these ideas with the use of plans, a threshold, and various measures of possibility. Examples are presented, including one in which PARADISE uses an indirect strategy to prove that one move is best without finding the winning line (a first for a chess program).     

Using background knowledge to rank itemsets

Assessing the quality of discovered results is an important open problem in data mining. Such assessment is particularly vital when mining itemsets, since commonly many of the discovered patterns can be easily explained by background knowledge. The simplest approach to screen uninteresting patterns is to compare the observed frequency against the independence model. Since the parameters for the independence model are the column margins, we can view such screening as a way of using the column margins as background knowledge. In this paper we study techniques for more flexible approaches for infusing background knowledge. Namely, we show that we can efficiently use additional knowledge such as row margins, lazarus counts, and bounds of ones. We demonstrate that these statistics describe forms of data that occur in practice and have been studied in data mining. To infuse the information efficiently we use a maximum entropy approach. In its general setting, solving a maximum entropy model is infeasible, but we demonstrate that for our setting it can be solved in polynomial time. Experiments show that more sophisticated models fit the data better and that using more information improves the frequency prediction of itemsets.     

Using OWL-VisMod through a decision-making process for reusing OWL ontologies

Knowledge representation (KR) can be defined as a set of ontological commitments, provided with the capabilities of performing inference. The knowledge can be represented using an ontology, which provides a shared insight into a certain domain. The use of ontologies to represent knowledge also allows interoperation among knowledge-based systems. The process of building ontologies can be tedious and sometimes exhaustive. A possible solution in order to avoid this problem would be to reuse the ontologies previously created by others. This paper describes a case study of reusability using OWL-VisMod, a tool designed for developing ontological engineering based on visual conceptual modelling for OWL ontologies. A workflow performed with OWL-VisMod is described; including a decision-making process in order to decide whether or not it could be desirable to reuse an ontology, according to the requirements of a certain project.     

Using domain knowledge to optimize the knowledge discovery process in databases

Modern database technologies process large volumes of data to discover new knowledge. Some large databases make discovery computationally expensive. Additional knowledge, known as domain or background knowledge, can often guide and restrict the search for interesting knowledge. This paper discusses mechanisms by which domain knowledge can be used effectively in discovering knowledge from databases. In particular, we look at the use of domain knowledge to reduce the size of the database for discovery, to optimize the hypotheses which represent the interesting knowledge to be discovered, to optimize the queries used to prove the hypotheses, and to avoid possible redundant and contradictory rule discovery. Some experimental results using the IDIS knowledge discovery tool is provided. ©2000 John Wiley & Sons, Inc.     

OWL 2: The next step for OWL

Since achieving W3C recommendation status in 2004, the Web Ontology Language (OWL) has been successfully applied to many problems in computer science. Practical experience with OWL has been quite positive in general; however, it has also revealed room for improvement in several areas. We systematically analyze the identified shortcomings of OWL, such as expressivity issues, problems with its syntaxes, and deficiencies in the definition of OWL species. Furthermore, we present an overview of OWL 2—an extension to and revision of OWL that is currently being developed within the W3C OWL Working Group. Many aspects of OWL have been thoroughly reengineered in OWL 2, thus producing a robust platform for future development of the language.     

Using approximate reasoning to represent default knowledge

We discuss the issue of default inference rules. We introduce the reasoning mechanism of the theory of approximate reasoning. We show how we can represent default knowledge in the framework of this theory.     

Using knowledge anchors to reduce cognitive overhead

Cognitive overhead, a well-known Web design problem, was defined by Conklin as the additional effort and concentration necessary to maintain several tasks or trails at one time. Cognitive overhead can be reduced using bidirectional knowledge anchors, points within a frame that have significance to the author-and later to the reader. A knowledge anchor is the point within the frame from which the user can trigger a link. An anchor designates either an area from which a reader may want to branch to investigate other related material or an area to which a reader may want to jump.     

Using Wikipedia knowledge to improve text classification

Text classification has been widely used to assist users with the discovery of useful information from the Internet. However, traditional classification methods are based on the “Bag of Words” (BOW) representation, which only accounts for term frequency in the documents, and ignores important semantic relationships between key terms. To overcome this problem, previous work attempted to enrich text representation by means of manual intervention or automatic document expansion. The achieved improvement is unfortunately very limited, due to the poor coverage capability of the dictionary, and to the ineffectiveness of term expansion. In this paper, we automatically construct a thesaurus of concepts from Wikipedia. We then introduce a unified framework to expand the BOW representation with semantic relations (synonymy, hyponymy, and associative relations), and demonstrate its efficacy in enhancing previous approaches for text classification. Experimental results on several data sets show that the proposed approach, integrated with the thesaurus built from Wikipedia, can achieve significant improvements with respect to the baseline algorithm.

OWL-S过程模型的形式化描述与相容性分析

构建组合服务的形式化模型是对其进行验证的前提与基础,然而缺乏统一的构建框架使得建模过程变得难以把握且无法实现自动化。通过对确定型有限自动机的扩展,建立了用于描述OWL-S过程模型的有限迁移系统——服务过程自动机,为组合服务的形式化建模提供了统一框架和自动化基础。同时,通过分析服务过程自动机的可接受位置及其在组合过程中可能产生的约束,将相容性划分为三个等级,从而能够在不同强度的可靠性要求下进行组合服务的验证工作。