开发知识问题求解建模：1994年7月25日收到

开发知识级问题求解建模正在导致知识工程的重大变革，鉴于知识表示的本体论可以作为开发知识级建模的基础理论，我们提出描述问题解过程组织的本体论ＲＡ和刻划领域世界结构的本体论ＯＴ，并设计了基于这两个本体论的问题求解建模方法。     

基于知识的汽车后桥CAx集成技术与应用研究

针对制造企业在应用CAx系统过程中如何集成各种信息资源和知识的问题,以汽车后桥产品设计开发为研究对象,提出了一种基于知识的协同CAx集成系统平台框架。采用基于本体论的知识建模方法,提出一种特征参数度量的相似实例检索方法,实现了知识的重用,着重叙述了基于实例推理技术、面向设计流程的知识驱动设计计算、基于特征参数的CAD建模方法、基于模型共享的CAE分析以及产品虚拟装配等关键技术,并开发了此系统平台,实现了基于知识的CAD／CAE集成,有效地支持了汽车后桥产品设计。     

基于本体的机床高速主轴数字化集成技术研究

为提高机床高速主轴设计的效率,针对机床制造企业在数字化设计系统的过程中如何集成各种信息资源和知识的问题,以机床高速主轴系统设计开发为研究对象,提出了一种基于本体知识的机床高速主轴协同设计集成系统平台框架.系统采用基于本体论的知识建模方法,利用特征参数度量的相似实例检索方法,以及基于实例推理技术、面向设计流程的知识驱动设...     

面向工程应用的生物信息系统化建模研究

生物信息在工程中的应用是仿生方法研究的核心.已有方法缺少对生物知识在工程中系统转化的支持,难以分析复杂的生物信息,造成了仿生设计的效率低下.为解决这一问题,分析了现有理论中对生物信息的分析策略,结合生物耦合机制等新理论,以工程系统的建模为基础,提出了一套面向工程应用的符号化生物信息模型,能够有效地对生物原型中的功能特性和系统特性进行描述和分析,同时是面向工程设计人员的,与传统的产品功能特性和系统组成的建模策略具有很好的相容性,克服了传统方法部分局限性.最后通过生物信息建模实例,验证了本方法的实用性与可行性.     

积木式建模系统的研究

积木式建模系统（ＢＭＢＳ）是构造模型库管理系统的一种先进方法，它可以给用户提供一个模型库的原型系统，用户根据自己的需求，建造面向特定对象的模型库。由于ＢＭＢＳ必须以集成知识环境为支撑，所以它实际上又可以成为开发ＤＳＳ的原型系统。本文介绍了我们开发的ＢＭＢＳ。     

一种促进企业模型快速定制的制造企业元模型

从零开始建立特定企业模型需要耗费大量的资源与时间,基于高层元模型建立企业参考模型(通用模型或行业模型),基于参考模型建立特定企业的模型是建模专家公认的方法。本文分析了企业建模中的基本概念,讨论了作为企业建模高层元模型的本体论模型的应用,基于统一建模语言建立了企业的本体论模型,重点分析了过程本体论模型。     

一种基于本体的故障诊断知识库系统的构建研究

试图从近年来在知识工程及相关应用领域应用较广的"本体论"出发,提出一种基于本体的故障知识库系统的构建方法,该方法的知识库系统部分是采用本体论方法构建的,其研究是重点。     

面板堆石坝级配料开采爆破块度预报模型及爆破设计参数优化研究

本文介绍了爆破块度预报模型的建立方法，并结合天生桥一级水电站面板堆石坝级配料开采爆破试验，建立了适合于天生桥一级水电站级配料开采的爆破块度预报模型，提出了快速判断开采料是否满足设计要求的方法。本文还介绍了爆破参数优化的概念，并建立了适用于水电站开挖爆破的优化目标函数及约束条件。最后通过实例，介绍了建模方法以及最优解和可行解的求解方法，并对天生桥一级水电站的级配料开采的爆破参数进行了优化设计，提出了适合于天生桥一级水电站ⅢＡ、ⅢＢ料开采的最优爆破参数及可行爆破参数。这些方法和思路对其他类似工程的研究有着重要的参考价值。     

数字制造系统的建模理论及方法研究

阐述了基于数字化工厂的设备环境建模、面向数字化加工的过程建模和数字制造系统的信息建模等理论问题，为系统地描述整个数字制造系统提供了新的思路和方法。     

面向经营过程的虚拟企业建模与协同运作

聚焦于经营过程，对面向经营过程的虚拟企业建模与协同运作进行了一体化的研究，经营过程的执行必须依赖于一定的组织结构，为此，首先提出了基于智能体的虚拟企业框架结构，关于面向经营过程的虚拟企业建模，提出了建模的总体框架，描述了面向经营过程的虚拟企业模型的参考结构，建模方法和建模工具，关于面向经营过程的虚拟企业协同运作，给出了一种运作总体框架，提出了一种虚拟企业协同运作控制机制。     

A framework for developing engineering design ontologies within the aerospace industry

This paper presents a framework for developing engineering design ontologies within the aerospace industry. The aim of this approach is to strengthen the modularity and reuse of engineering design ontologies to support knowledge management initiatives within the aerospace industry. Successful development and effective utilisation of engineering ontologies strongly depends on the method/framework used to develop them. Ensuring modularity in ontology design is essential for engineering design activities due to the complexity of knowledge that is required to be brought together to support the product design decision-making process. The proposed approach adopts best practices from previous ontology development methods, but focuses on encouraging modular architectural ontology design. The framework is comprised of three phases namely: (1) Ontology design and development; (2) Ontology validation and (3) Implementation of ontology structure. A qualitative research methodology is employed which is composed of four phases. The first phase defines the capture of knowledge required for the framework development, followed by the ontology framework development, iterative refinement of engineering ontologies and ontology validation through case studies and experts’ opinion. The ontology-based framework is applied in the combustor and casing aerospace engineering domain. The modular ontologies developed as a result of applying the framework and are used in a case study to restructure and improve the accessibility of information on a product design information-sharing platform. Additionally, domain experts within the aerospace industry validated the strengths, benefits and limitations of the framework. Due to the modular nature of the developed ontologies, they were also employed to support other project initiatives within the case study company such as role-based computing (RBC), IT modernisation activity and knowledge management implementation across the sponsoring organisation. The major benefit of this approach is in the reduction of man-hours required for maintaining engineering design ontologies. Furthermore, this approach strengthens reuse of ontology knowledge and encourages modularity in the design and development of engineering ontologies.     

Developing manufacturing ontologies for knowledge reuse in distributed manufacturing environment

The use of ontologies for knowledge sharing and distributed collaboration has been widely recognised in the knowledge modelling community, but the lack of a systematic and constructive methodology for developing manufacturing ontologies has impeded their wide usage for knowledge reuse in distributed manufacturing environments. This paper presents a constructive, two-level knowledge modelling approach to systematically develop manufacturing ontologies using both software engineering and Semantic Web paradigms. The UML/OCL (Unified Modeling Language/Object Constraint Language)-based object modelling is used first to serve as a graphical and structured basis for conceptual communication between domain experts and knowledge engineers. The OWL/SWRL (Web Ontology Language/Semantic Web Rule Language)-based ontology modelling then extends the UML/OCL-based object models with added semantics using a progressive, semantics-oriented knowledge acquisition method. An illustrative example for manufacturing ontology development in the manufacturing industry for producing electronic connectors is used to demonstrate the practicality of the proposed approach.     

Verification of knowledge shared across design and manufacture using a foundation ontology

Seamless computer-based knowledge sharing between departments of a manufacturing enterprise is useful in preventing unnecessary design revisions. A lack of interoperability between independently developed knowledge bases, however, is a major impediment in the development of a seamless knowledge-sharing system. Interoperability, being an ability to overcome semantic and syntactic differences during computer-based knowledge sharing, can be enhanced through the use of foundation ontologies. Foundation or core ontologies can be used to overcome differences existing in more specialised ontologies and to ensure seamless knowledge sharing. This is because these ontologies provide a common grounding for domain ontologies to be used by different functions or departments. This common base can be used by mediation and knowledge verification systems to authenticate the meaning of knowledge understood across different domains. For this reason, this research proposes a knowledge verification framework for developing a system capable of verifying knowledge between those domain ontologies which are developed out of a common core or foundation ontology. This framework makes use of ontology logic to standardise the way concepts from a foundation and core concepts ontology are used in domain ontologies and then, by using the same principles, the knowledge being shared is verified.     

PSOM<Superscript>2</Superscript>—partitioning-based scalable ontology matching using MapReduce

The growth and use of semantic web has led to a drastic increase in the size, heterogeneity and number of ontologies that are available on the web. Correspondingly, scalable ontology matching algorithms that will eliminate the heterogeneity among large ontologies have become a necessity. Ontology matching algorithms generally do not scale well due to the massive number of complex computations required to achieve matching. One of the methods used to address this problem is the use of partition-based systems to reduce the matching space. In this paper, we propose a new partitioning-based scalable ontology matching system called PSOM². We have designed a new neighbour-based intra-similarity measure to increase the quality of the cluster set formation for the partition-based ontology matching process. These sets of clusters or sub-ontologies are matched across the input ontologies to identify matchable cluster pairs, based on anchors that are efficiently discovered through a new light-weight linguistic matcher (EI-sub). However, in order to further increase the efficiency of the time-consuming anchor discovery process we have designed a MapReduce-based EI-sub process where anchors are discovered in distributed and parallel fashion. Experiments on benchmark OAEI (Ontology Alignment Evaluation Initiative) large scale ontologies demonstrate that the new PSOM² system achieves, on an average, 31% decrease in entropy of the clusters and 54.5% reduction in overall run time. Based on the experimental results, it is evident that the new PSOM² achieves better quality clusters and a major reduction in execution time, leading to an effective and scalable ontology matching system.     

Ontologies in the context of product lifecycle management: state of the art literature review

The use of ontologies in the context of product lifecycle management (PLM) is gaining importance and popularity, while at the same time it generates a lot of controversy in discussions within scientific and engineering communities. Yet, what is ontology? What challenges have been addressed so far? What role does ontology play? Do we really need ontology? These are the core questions this paper seeks to address. We propose to conduct a comprehensive study of the concept of Ontology firstly in its domain of origin, Philosophy, and secondly in information science. Based on the understanding of this concept and an in-depth analysis of the state of the art, seven key roles of ontology are defined. These roles serve as a framework describing the general state of research on the use of ontologies in the context of PLM.     

The integrated model of the Superhard Materials knowledge domain

An integrated model for the Superhard Materials knowledge domain that includes a system of multilevel ontologies has been developed. The system of the Superhard Materials ontology of the science of materials uses a terminological thesaurus and contains notions of task-level knowledge, which correspond to the line of investigations developed at the Bakul Institute for Superhard Materials, National Academy of Sciences of Ukraine.     

A Prototype for Diagnosis of Psoriasis in Traditional Chinese Medicine

Psoriasis is a chronic, non-communicable, painful, disfiguring and disabling disease for which there is no cure, with great negative impact on patients’ quality of life (QoL). Diagnosis and treatment with traditional Chinese medical technique based on syndrome differentiation has been used in practice for a long time and proven effective, though, up to now, there are only a few available studies about the use of semantic technologies and the knowledge systems that use Traditional Chinese Medicine (TCM)-syndrome differentiation for information retrieval and automated reasoning. In this paper we use semantic techniques based on ontologies to develop a prototypical system for the diagnosis of Psoriasis. For this purpose, a domain ontology is developed for syndrome differentiation of psoriasis vulgaris (PV). This ontology is founded on an adapted version of the general formal ontology (GFO), with the evidence-based clinical practice guideline of TCM for psoriasis vulgaris (Guideline 2013) as the primary data sources. The implemented prototype, called ONTOPV, contains this domain ontology and is aimed at a decision support system for diagnosis and treatment of PV. This system uses a case-database for Case Based Reasoning (CBR), combined with fuzzy pattern recognition. Experimental results show that the ONTOPV realizes the basic functionalities of data collection, querying, browsing and navigation, and supports rule-based knowledge reasoning, and integrates fuzzy pattern recognition. It can provide users with clinical decision support for TCM syndrome differentiation in diagnosis of psoriasis.     

上下文感知计算中的时序表示与推理方法研究

分析了基于本体论的上下文建模方法,对现有上下文本体进行了分类和比较,并针对其在时序表示方面存在的问题,提出了基于资源描述框架(RDF)具体化的上下文时序信息表示方法,扩展了上下文本体的描述能力.同时针对上下文感知中的时序推理问题,提出了一个持续时间推理算法;另外还通过整合Allen区间代数和本体推理实现了时序关系推理.最后通过实验验证了上述两种时序推理方法的效率.     

Ontology Based Ocean Knowledge Representation for Semantic Information Retrieval

The drastic growth of coastal observation sensors results in copious data that provide weather information. The intricacies in sensor-generated big data are heterogeneity and interpretation, driving high-end Information Retrieval (IR) systems. The Semantic Web (SW) can solve this issue by integrating data into a single platform for information exchange and knowledge retrieval. This paper focuses on exploiting the SW base system to provide interoperability through ontologies by combining the data concepts with ontology classes. This paper presents a 4-phase weather data model: data processing, ontology creation, SW processing, and query engine. The developed Oceanographic Weather Ontology helps to enhance data analysis, discovery, IR, and decision making. In addition to that, it also evaluates the developed ontology with other state-of-the-art ontologies. The proposed ontology’s quality has improved by 39.28% in terms of completeness, and structural complexity has decreased by 45.29%, 11% and 37.7% in Precision and Accuracy. Indian Meteorological Satellite INSAT-3D’s ocean data is a typical example of testing the proposed model. The experimental result shows the effectiveness of the proposed data model and its advantages in machine understanding and IR.     

Learner Achievement and Attitudes under Different Paces of Transitioning to Independent Problem Solving

This study examined the effect of the pace of transitioning from worked examples to independent problem solving for students with three different levels of prior knowledge. Three paces of transitioning were examined: immediate transitioning, fast fading, and slow fading. The study was conducted with engineering college freshmen in the engineering knowledge domain of introductory electrical circuit analysis and found a significant interaction between the particpants' prior knowledge and the pace of transitioning to independent problem solving on retention posttest performance. The high prior knowledge participants achieved significantly higher retention scores under the fast and immediate transitioning than under the slow transitioning, whereas the low prior knowledge participants achieved significantly higher retention scores under the slow transitioning. The interaction result for retention indicates that by selectively employing slow fading for low prior knowledge learners and fast fading or immediate transitioning for high prior knowledge learners, significant improvements in learning may be achieved.