大规模定制下基于本体的服务产品配置研究*

为了研究服务产品大规模定制的实现,探讨了服务产品大规模定制的实现机理和关键技术,指出模块化设计对服务产品族及服务产品平台的重要性。在服务产品模块化设计的基础上,为了提高服务产品配置知识的共享性和重用性,提出了一般服务产品配置的模型和概念,结构化了服务产品配置的构件和模块,建立了服务产品配置本体。采用网络本体语言OWL形式化服务产品配置的结构知识,采用语义网规则语言SWRL编码服务产品配置的约束知识,并分别映射到JESS推理系统的知识库和规则库中,使用JESS推理机执行推理过程,产生配置结果。为说明该方法的     

Automatically generating assembly tolerance types with an ontology-based approach

In most cases, designers have to manually specify both assembly tolerance types and values when they design a mechanical product. Different designers will possibly specify different assembly tolerance types and values for the same nominal geometry. Furthermore, assembly tolerance specification design of a complex product is a highly collaborative process, in which semantic interoperability issues significantly arise. These situations will cause the uncertainty in assembly tolerance specification design and finally affect the quality of the product. In order to reduce the uncertainty and to support the semantic interoperability in assembly tolerance specification design, an ontology-based approach for automatically generating assembly tolerance types is proposed. First of all, an extended assembly tolerance representation model is constructed by introducing a spatial relation layer. The constructed model is hierarchically organized and consists of part layer, assembly feature surface layer, and spatial relation layer. All these layers are defined with Web Ontology Language (OWL) assertions. Next, a meta-ontology for assembly tolerance representations is constructed. With this meta-ontology, the domain-specific assembly tolerance representation knowledge can be derived by reusing or inheriting the classes or properties. Based on this, assembly tolerance representation knowledge is formalized using OWL. As a result, assembly tolerance representation knowledge has well-defined semantics due to the logic-based semantics of OWL, making it possible to automatically detect inconsistencies of assembly tolerance representation knowledge bases. The mapping relations between spatial relations and assembly tolerance types are represented in Semantic Web Rule Language (SWRL). Furthermore, actual generation processes of assembly tolerance types are carried out using Java Expert System Shell (JESS) by mapping OWL-based structure knowledge and SWRL-based constraint knowledge into JESS facts and JESS rules, respectively. Based on this, an approach for automatically generating assembly tolerance types is proposed. Finally, the effectiveness of the proposed approach is demonstrated by a practical example.     

DLEJena: A practical forward-chaining OWL 2 RL reasoner combining Jena and Pellet

This paper describes DLEJena, a practical reasoner for the OWL 2 RL profile that combines the forward-chaining rule engine of Jena and the Pellet DL reasoner. This combination is based on rule templates, instantiating at run-time a set of ABox OWL 2 RL/RDF Jena rules dedicated to a particular TBox that is handled by Pellet. The goal of DLEJena is to handle efficiently, through instantiated rules, the OWL 2 RL ontologies under direct semantics, where classes and properties cannot be at the same time individuals. The TBox semantics are treated by Pellet, reusing in that way efficient and sophisticated TBox DL reasoning algorithms. The experimental evaluation shows that DLEJena achieves more scalable ABox reasoning than the direct implementation of the OWL 2 RL/RDF rule set in the Jena’s production rule engine, which is the main target of the system. DLEJena can be also used as a generic framework for applying an arbitrary number of entailments beyond the OWL 2 RL profile.     

知识网格中基于领域本体的智能检索

本文提出知识网格环境下基于领域本体的智能检索模型,采用OWL DL语言对领域知识进行形式化描述,支持推理和深层语义检索.＂标注＂和＂查询优化＂是检索的两个关键技术.通过规范的概念和概念间语义关系对文档片段进行标注,并针对＂一词多义＂问题提出＂主题-概念＂两阶段消歧算法.＂查询优化＂过程中,基于OWL DL推理的优化算法实现查询概念的自动扩展,提高了查全率和查准率.基于以上方法,建立航天领域本体,利用网上数据库开放资源作为测试集进行评测.实验显示,与传统基于     

Ontology-based assembly design and information sharing for collaborative product development

To realize a truly collaborative product design and development process, effective communication among design collaborators is a must. In other words, the design intent that is imposed in a product design should be seized and interpreted properly; heterogeneous modeling terms should be semantically processed both by design collaborators and intelligent systems. Ontologies in the Semantic Web can explicitly represent semantics and promote integrated and consistent access to data and services. Thus, if an ontology is used in a heterogeneous and distributed design collaboration, it will explicitly and persistently represent engineering relations that are imposed in an assembly design. Design intent can be captured by reasoning, and, in turn, as reasoned facts, it can be propagated and shared with design collaborators. This paper presents a new paradigm of ontology-based assembly design. In the framework, an assembly design (AsD) ontology serves as a formal, explicit specification of assembly design so that it makes assembly knowledge both machine-interpretable and to be shared. An Assembly Relation Model (ARM) is enhanced using ontologies that represent engineering, spatial, assembly, and joining relations of assembly in a way that promotes collaborative assembly information-sharing environments. In the developed AsD ontology, implicit AsD constraints are explicitly represented using OWL (Web Ontology Language) and SWRL (Semantic Web Rule Language). This paper shows that the ability of the AsD ontology to be reasoned can capture both assembly and joining intents by a demonstration with a realistic mechanical assembly. Finally, this paper presents a new assembly design information-sharing framework and an assembly design browser for a collaborative product development.     

A Rule-Based Object-Oriented OWL Reasoner

In this paper, we describe O-DEVICE, a memory-based knowledge-based system for reasoning and querying OWL ontologies by implementing RDF/OWL entailments in the form of production rules in order to apply the formal semantics of the language. Our approach is based on a transformation procedure of OWL ontologies into an object-oriented schema and the application of inference production rules over the generated objects in order to implement the various semantics of OWL. In order to enhance the performance of the system, we introduce a dynamic approach of generating production rules for ABOX reasoning and an incremental approach of loading ontologies. O-DEVICE is built over the CLIPS production rule system, using the object-oriented language COOL to model and handle ontology concepts and RDF resources. One of the contributions of our work is that we enable a well-known and efficient production rule system to handle OWL ontologies. We argue that although native OWL rule reasoners may process ontology information faster, they lack some of the key features that rule systems offer, such as the efficient manipulation of the information through complex rule programs. We present a comparison of our system with other OWL reasoners, showing that O-DEVICE can constitute a practical rule environment for ontology manipulation.     

Salient object detection using biogeography-based optimization to combine features

In recent studies, ontology related concepts have been introduced into FIPA ACL content language to convey information for agent communication. However, these works have only applied ontology-based knowledge representation in communication message and then demonstrated the advantage of this association. In fact, although ontology can represent semantic implications needed for decidable reasoning support, it has no mechanism for defining complex rule-based representation to support inference. The motivation of this study is to address this issue by developing a semantic-based infrastructure to integrate Semantic Web technologies into ACL message contents. This semantic-based infrastructure defines two different semantic frameworks: the three-tier knowledge representation framework for message content and the Multi-layer Ontology Architecture for content language. The former is developed based on Semantic Web stack to support ontology-based reasoning and rule-based inference. The latter is adopted to develop a Lightweight Ontology-based Content Language (LOCL) to describe agent communication messages in an unambiguous and computer-interpretable way Jena reasoner is used in an application scenario that exploits agent communication with LOCL as content language, OWL as ontology language, and SWRL as rule language to demonstrate the feasibility of the proposed infrastructure.     

An MDE-based methodology for closed-world integrity constraint checking in the semantic web

Ontology-based data-centric systems support open-world reasoning. Therefore, for these systems, Web Ontology Language (OWL) and Semantic Web Rule Language (SWRL) are not suitable for expressing integrity constraints based on the closed-world assumption. Thus, the requirement of integrating the open-world assumption of OWL/SWRL with closed-world integrity constraint checking is inevitable. SPARQL, recommended by World Wide Web (W3C), is a query language for RDF graphs, and many research studies have shown that it is a perfect candidate for closed-world constraint checking for ontology-based data-centric applications. In this regard, many research studies have been performed to transform integrity constraints into SPARQL queries where some studies have shown the limitations of partial expressivity of knowledge bases while performing the indirect transformations, whereas others are limited to a platform-specific implementation. To address these issues, this paper presents a flexible and formal methodology that employs Model-Driven Engineering (MDE) to model closed-world integrity constraints for open-world reasoning. The proposed approach offers semantic validation of data by expressing integrity constraints at both the model level and the code level. Moreover, straightforward transformations from OWL/SWRL to SPARQL can be performed. Finally, the methodology is demonstrated via a real-world case study of water observations data.     

Ontology-based automated support for goal–use case model analysis

Combining goal-oriented and use case modeling has been proven to be an effective method in requirements elicitation and elaboration. To ensure the quality of such modeled artifacts, a detailed model analysis needs to be performed. However, current requirements engineering approaches generally lack reliable support for automated analysis of consistency, correctness and completeness (3Cs problems) between and within goal models and use case models. In this paper, we present a goal–use case integration framework with tool support to automatically identify such 3Cs problems. Our new framework relies on the use of ontologies of domain knowledge and semantics and our goal–use case integration meta-model. Moreover, functional grammar is employed to enable the semiautomated transformation of natural language specifications into Manchester OWL Syntax for automated reasoning. The evaluation of our tool support shows that for representative example requirements, our approach achieves over 85 % soundness and completeness rates and detects more problems than the benchmark applications.     

装配公差综合领域本体知识库的构建

为了减少机械产品设计过程中的不确定性,解决装配公差信息在异构CAX系统中共享性差和传递不畅的问题,利用本体丰富的语义知识和语法结构,通过分析装配公差综合领域相关知识,采用网络本体语言OWL定义其中的概念和关系,并采用语义网规则语言SWRL定义其中的约束条件和分配经验。将基于OWL的结构化知识转换成事实、基于SWRL的约束化知识转换成规则,并在推理引擎的基础上构建装配公差综合领域本体知识库。同时开发了基于本体的装配公差综合原型系统,实现了装配公差类型和装配公差值的自动生成。     

Kumbang: A domain ontology for modelling variability in software product families

Variability is the ability of a system to be efficiently extended, changed, customised or configured for use in a particular context. There is an ever-growing demand for variability of software. Software product families are an important means for implementing software variability. We present a domain ontology called Kumbang for modelling the variability in software product families. Kumbang synthesises previous approaches to modelling variability in software product families. In addition, it incorporates modelling constructs developed in the product configuration domain for modelling the variability in non-software products. The modelling concepts include components and features with compositional structure and attributes, the interfaces of components and connections between them, and constraints. The semantics of Kumbang is rigorously described using natural language and a UML profile. We provide preliminary proof of concept for Kumbang: the domain ontology has been provided with a formal semantics by implementing a translation into a general-purpose knowledge representation language with formal semantics and inference support. A prototype tool for resolving variability has been implemented.     

语义Web规则标记语言OWLRule+的设计与实现

语义Web是对未来Web体系结构的一个伟大设想，其研究分层次进行．目前足够成熟的最高层是以OWL语言为代表的ontology．但它的语义仅限于描述逻辑，该逻辑主要表示对象和类的层次结构，而规则的表达能力弱，因此在其之上需要一种表达力更丰富的逻辑语言．设计了一种新的语义Web规则标记语言OWLRule ：其语法扩展了OWL；语义基于CARIN，一种结合描述逻辑和Horn规则的表示语言；实现基于Jess规则推理机．实例学习展示了它对目前的Web ontology语言在规则表示和推理能力上的扩展．     

故障树领域本体及SWRL规则的构建方法研究

故障树(Fault Tree,FT)被广泛应用于系统故障的快速定位,但其因缺乏精确的语义信息而存在重复构建问题。将本体引入到故障树领域中,并对如何构建故障树本体及相应的SWRL规则进行了研究:首先采用本体描述语言 (Web Ontology Language,OWL)对故障树中的概念及概念之间的关系进行知识表示,构建了一个可共享、可重用、可扩展的故障树领域本体;然后将故障树中事件之间的逻辑关系转化成语义 Web 规则语言 (Semantic Web Rule Language,SWRL);最后将构建的故障树领域本体和SWRL规则放入JESS推理机中进行推理,产生新的知识,用于系统故障的快速定位。实验证明,使用所提出的方法能在解决故障树重复构建问题的同时,不对系统故障的快速定位产生影响。     

OntoSTEP: Enriching product model data using ontologies

The representation and management of product lifecycle information is critical to any manufacturing organization. Different modeling languages are used at different lifecycle stages, for example STEP’s EXPRESS may be used at a detailed design stage, while UML may be used for initial design stages. It is necessary to consolidate product information created using these different languages to build a coherent knowledge base. In this paper, we present an approach to enable the translation of STEP schema and its instances to Ontology Web Language (OWL). This gives a model–which we call OntoSTEP–that can easily be integrated with any OWL ontologies to create a semantically rich model. As an example, we combine geometry information represented in STEP with non-geometry information, such as function and behavior, represented using the NIST’s Core Product Model (CPM). A plug-in for Protégé is developed to automate the different steps of the translation. As additional benefits, reasoning, inference procedures, and queries can be performed on enriched legacy CAD models. We describe the rules for the translation from EXPRESS to OWL, and illustrate the benefits of OWL translation with an example. We will also describe how these mapping rules can be implemented through meta-model based transformations, which can be used to map other languages to OWL.     

A UML/OCL framework for the analysis of graph transformation rules

In this paper we present an approach for the analysis of graph transformation rules based on an intermediate OCL representation. We translate different rule semantics into OCL, together with the properties of interest (like rule applicability, conflicts or independence). The intermediate representation serves three purposes: (1) it allows the seamless integration of graph transformation rules with the MOF and OCL standards, and enables taking the meta-model and its OCL constraints (i.e. well-formedness rules) into account when verifying the correctness of the rules; (2) it permits the interoperability of graph transformation concepts with a number of standards-based model-driven development tools; and (3) it makes available a plethora of OCL tools to actually perform the rule analysis. This approach is especially useful to analyse the operational semantics of Domain Specific Visual Languages. We have automated these ideas by providing designers with tools for the graphical specification and analysis of graph transformation rules, including a back-annotation mechanism that presents the analysis results in terms of the original language notation.     

基于本体论的语义建模研究

本体(Ontology)是下一代互联网(SemanticWeb)的基础,OWL语言是W3C组织定义的本体描述语言。鉴于当前互联网的规模越来越庞大,如何准确快速地获取信息正变得至关重要,而基于本体论的语义模型为信息的表示、交换和处理提供了一个较为合理的标准,从而使得网上信息的完全共享成为可能。文中阐述了本体的概念,重点探讨了基于本体论的语义建模方法和OWL语言对本体表示的支持,并且具体给出了一个基于OWL语言的建模实例。     

Toward an OSGi-based infrastructure for context-aware applications

Applications and services must adapt to changing contexts in dynamic environments. However, building context-aware applications is still complex and time-consuming due to inadequate infrastructure support. We propose a context-aware infrastructure for building and rapidly prototyping such applications in a smart-home environment. This OSGi-based infrastructure manages context-aware services reliably and securely and efficiently supports context acquisition, discovery, and reasoning. A formal, ontology-based context model enables semantic context representation, reasoning, and knowledge sharing. We propose an ontology-based context model that leverages Semantic Web technology and OWL (Web Ontology Language). OWL is an ontology markup language that enables context sharing and context reasoning. Based on our context model, we also propose a service-oriented context-aware middleware (SOCAM) architecture, including a set of independent services that perform context discovery, acquisition, and interpretation.     

Clustering rule bases using ontology-based similarity measures

Rules are increasingly becoming an important form of knowledge representation on the Semantic Web. There are currently few methods that can ensure that the acquisition and management of rules can scale to the size of the Web. We previously developed methods to help manage large rule bases using syntactical analyses of rules. This approach did not incorporate semantics. As a result, rule categorization based on syntactic features may not be effective. In this paper, we present a novel approach for grouping rules based on whether the rule elements share relationships within a domain ontology. We have developed our method for rules specified in the Semantic Web Rule Language (SWRL), which is based on the Web Ontology Language (OWL) and shares its formal underpinnings. Our method uses vector space modeling of rule atoms and an ontology-based semantic similarity measure. We apply a clustering method to detect rule relatedness, and we use a statistical model selection method to find the optimal number of clusters within a rule base. Using three different SWRL rule bases, we evaluated the results of our semantic clustering method against those of our syntactic approach. We have found that our new approach creates clusters that better match the rule bases’ logical structures. Semantic clustering of rule bases may help users to more rapidly comprehend, acquire, and manage the growing numbers of rules on the Semantic Web.