CONSISTENCY CHECKING ALONG TIME

Most model-based approaches to diagnosis require a consistency-checking procedure to perform their task. When dealing with a dynamically changing system, such a procedure must take into account time-varying data. This requires suitable techniques for reasoning over time. Additional difficulties arise when delays are involved in interactions between variables. The worst case occurs when some of the delays are completely unspecified. This report presents an approach to consistency checking that handles qualitative models of dynamic systems exhibiting time lags. A component-centered ontology is adopted to model the structure of the physical system, and an episode-based approach is adopted for representing its behavior over time. An example consisting of a physical process exhibiting transportation lags is used to illustrate the power of the approach. We present algorithms and an implementation in PROLOG called C-CAT (consistency checking along time). Some meaningful outputs from the program are used as examples. The solution proposed represents an extension to B. C. Williams' temporal constraint propagation methodology. It also extends the applicability range of existing approaches to model-based diagnosis, permitting its use in tasks such as online diagnosis of dynamic systems.     

Learning the emergent knowledge from annotated blog postings

Emergent knowledge does not come from a particular document or a particular knowledge source, but comes from a collection of documents or knowledge sources. This paper proposes a system which combines social web content and semantic web technology to process the emergent knowledge from the blogosphere. The proposed system regards blog postings as experiences of people on particular topics. By annotating postings in the selected domains with ontology vocabularies, the system collects experiences from various people into an ontology about people and experiences. The system processes this ontology with semantic rules to find the emergent knowledge. Users can access previously unavailable facts, concepts and trends which are emerging from social web content by using the proposed system.     

Distinct and combined effects of disciplinary composition and methodological support on problem solving in groups

Several reasons for the use of multidisciplinary teams composed of individuals with natural science and engineering background in problem‐solving processes exist. The most important are the integration of science‐based technologies into products and processes, and benefits for the problem‐solving process thanks to new knowledge and new perspectives on problems. In this study we analyse the implications of interdisciplinary (science – engineering) group problem solving from a managerial as well as from a cognitive perspective. We then report on an experiment investigating the impact of problem‐relevant disciplinary group composition and methodological support on the problem‐solving process and its outcome. The findings of the experiment have managerial, theoretical, and pedagogical implications related to early phases of New Product/Process Design processes in high‐technology and scientific knowledge‐related domains.     

利用本体云影模型的本体进化进程

整个Web环境,本体之间存在复杂的交互关系,构成了本体进化的社会环境。在非集中、分布式环境中,成功的本体应用需要加强对本体改变的管理和本体的进化。文章基于本体云影模型提出了一种本体进化进程COEP,COEP包含复杂的操作,使用本体云影模型的操作和定义,不仅是一个技术过程,还是一个管理过程,充分考虑了群体性、社会性,其方法重视本体之间的关系,其过程比现有的本体进化进程丰富,具有很好的应用前景。     

Verifying ontological commitment in knowledge-based systems

An ontology defines the terminology of a domain of knowledge: the concepts that constitute the domain, and the relationships between those concepts. In order for two or more knowledge-based systems to interoperate—for example, by exchanging knowledge, or collaborating as agents in a co-operative problem-solving process—they must commit to the definitions in a common ontology. Verifying such commitment is therefore a prerequisite for reliable knowledge-based system interoperability. This article shows how existing knowledge base verification techniques can be applied to verify the commitment of a knowledge-based system to a given ontology. The method takes account of the fact that an ontology will typically be expressed using a different knowledge representation language to the knowledge base, by incorporating translation into the verification procedure. While the representation languages used are specific to a particular project, their features are general and the method has broad applicability.     

Hybrid Measure of Agreement and Expertise for Ontology Matching in Lieu of a Reference Ontology

Ontologies have been widely used as a knowledge representation framework, and numerous methods have been put forth to match ontologies. It is well known that ontology matchers behave differently in various domains, and it is a challenge to predict or characterize their behavior. Herein, a hybrid expertise‐agreement aggregation strategy is proposed. Although others rely on the existence of a reference ontology, this typically does not exist in the real world. In this article, the fuzzy integral (FI) is used to aggregate multiple ontology matchers in lieu of a reference ontology. Specifically, we present a measure of expertise and fuse it with our previous agreement measure that is motivated by crowd sourcing to improve recall. This way, any available domain knowledge, in terms of partial ordering of a subset of inputs, can be included in the decision‐making process. By adding the domain knowledge to the agreement model, we are able to reach the upmost performance. Preliminary results demonstrate the robustness of our approach across domains. Sensitivity analysis is also provided, which shows the limits to which extreme destructive expertise affects system performance.     

An ontology-oriented knowledge methodology for process planning in additive layer manufacturing

The goal of this research is the generation of a novel knowledge with process-oriented ontology and the informal model. With regard to the proposed ontology, it establishes an improvement to related ontologies because it involves the demands of fabrication engineering and, specifically, the layer-upon-layer manufacturing planning process with various AM systems. Generally, task of AM planning indicates to make repeated and essential decisions which are always on the basis of the engineers’ knowledge and experience in additive manufacturing. Hence, it is a suitable field towards the execution of a knowledge-based engineering system. To represent the knowledge at an upper tier, the IDEF0 diagrams is introduced for identifying the sequence of tasks contained in the AM planning. They are a vital resources for defining the sequence of tasks and the messages flow. Afterward, these messages are analyzed thoroughly by applying schematic graphs, and then they are categorized into knowledge segments. Eventually, each knowledge segment is further divided into knowledge entities. At the same time, the relationships among them are also defined.Meanwhile, knowledge modeling involved generating an ontology of design feature which is utilized as a general information model in both computer-aided design and process planning, expression of fabrication criteria that depict the basis and properties for picking fabrication parameters. In a first method, the ontology has been examined utilizing an essential activity in AM planning: the task related to the confirmation of parameters over component quality. In this task, decisions have to be made in the orientation, slicing and the other process parameters. In this research, the differences between geometric and dimensional tolerance fabrication is considered in the generated methodology. The knowledge needed to aid all decisions is expressed utilizing the proposed ontology.     

Domain ontology graph model and its application in Chinese text classification

This paper proposes an ontology learning method which is used to generate a graphical ontology structure called ontology graph. The ontology graph defines the ontology and knowledge conceptualization model, and the ontology learning process defines the method of semiautomatic learning and generates ontology graphs from Chinese texts of different domains, the so-called domain ontology graph (DOG). Meanwhile, we also define two other ontological operations—document ontology graph generation and ontology graph-based text classification, which can be carried out with the generated DOG. This research focuses on Chinese text data, and furthermore, we conduct two experiments: the DOG generation and ontology graph-based text classification, with Chinese texts as the experimental data. The first experiment generates ten DOGs as the ontology graph instances to represent ten different domains of knowledge. The generated DOGs are then further used for the second experiment to provide performance evaluation. The ontology graph-based approach is able to achieve high text classification accuracy (with 92.3 % in f-measure) over other text classification approaches (such as 86.8 % in f-measure for tf–idf approach). The better performance in the comparative experiments reveals that the proposed ontology graph knowledge model, the ontology learning and generation process, and the ontological operations are feasible and effective.     

Learning over subconcepts: Strategies for 1‐class classification

In machine learning research and application, multiclass classification algorithms reign supreme. Their fundamental property is the reliance on the availability of data from all known categories to induce effective classifiers. Unfortunately, data from so‐called real‐world domains sometimes do not satisfy this property, and researchers use methods such as sampling to make the data more conducive for classification. However, there are scenarios in which even such explicit methods to rectify distributions fail. In such cases, 1‐class classification algorithms become the practical alternative. Unfortunately, domain complexity severely impacts their ability to produce effective classifiers. The work in this article addresses this issue and develops a strategy that allows for 1‐class classification over complex domains. In particular, we introduce the notion of learning along the lines of underlying domain concepts; an important source of complexity in domains is the presence of subconcepts, and by learning over them explicitly rather than on the entire domain as a whole, we can produce powerful 1‐class classification systems. The level of knowledge regarding these subconcepts will naturally vary by domain, and thus, we develop 3 distinct methodologies that take the amount of domain knowledge available into account. We demonstrate these over 3 real‐world domains.     

本体映射综述

作为不同本体之间知识共享和互操作的一种方法,本体映射受到越来越多的重视。根据本体映射过程将本体映射系统划分为五大功能组件,总结了本体映射系统中常用的相似度算法。通过梳理本体映射领域的最新发展成果,从不同层次、不同维度构建本体映射系统分类体系。 介绍并比较了一些经典的本体映射系统,并对这些本体映射系统进行评价。最后指出本体映射将面临的挑战。     

Knowledge acquisition and ontology modelling for construction of a control and monitoring expert system

This paper presents the processes of knowledge acquisition and ontology development for structuring the knowledge base of an expert system. Ontological engineering is a process that facilitates construction of the knowledge base of an intelligent system. Ontology is the study of the organization and classification of knowledge. Ontological engineering in artificial intelligence has the practical goal of constructing frameworks for knowledge that allow computational systems to tackle knowledgeintensive problems and it supports knowledge sharing and reuse. To illustrate the process of conceptual modelling using the Inferential Modelling Technique as a basis for ontology construction, the tool and processes are applied to build an expert system in the domain of monitoring of a petroleum-production facility.     

A case study on business process recovery using an e‐government system

Business processes have become one of the key assets of organization, since these processes allow them to discover and control what occurs in their environments, with information systems automating most of an organization's processes. Unfortunately, and as a result of uncontrolled maintenance, information systems age over time until it is necessary to replace them with new and modernized systems. However, while systems are aging, meaningful business knowledge that is not present in any of the organization's other assets gradually becomes embedded in them. The preservation of this knowledge through the recovery of the underlying business processes is, therefore, a critical problem. This paper provides, as a solution to the aforementioned problem, a model‐driven procedure for recovering business processes from legacy information systems. The procedure proposes a set of models at different abstraction levels, along with the model transformations between them. The paper also provides a supporting tool, which facilitates its adoption. Moreover, a real‐life case study concerning an e‐government system applies the proposed recovery procedure to validate its effectiveness and efficiency. The case study was carried out by following a formal protocol to improve its rigor and replicability. Copyright © 2011 John Wiley & Sons, Ltd.     

A semantic interoperability approach to health‐care data: Resolving data‐level conflicts

The integration of data from various electronic health record (EHR) systems presents a critical conflict in the sharing and exchanging of patient information across a diverse group of health‐oriented organizations. Patient health records in each system are annotated with ontologies utilizing different health‐care standards, creating ontology conflicts both at the schema and data levels. In this study, we introduce the concept of semantic ontology mapping for the facilitation and interoperability of heterogeneous EHR systems. This approach proposes a means of detecting and resolving the data‐level conflicts that generally exist in the ontology mapping process. We have extended the semantic bridge ontology in support of ontology mapping at the data level and generated the required mapping rules to reconcile data from different ontological sources into a canonical format. As a result, linked‐patient data are generated and made available in a semantic query engine to facilitate user queries of patient data across heterogeneous EHR systems.     

支持复杂产品系统设计的虚拟知识流生成算法

复杂产品系统设计流程环节多,设计人员不仅需要掌握本环节的知识,也需要了解相关环节的概要知识,采用本体表示的复杂产品系统知识资源对设计人员构成艰巨的挑战。结合工作流与本体技术,设计了满足设计人员知识需求的虚拟知识流生成算法。算法允许设计人员指定工作流各个节点的起始概念、属性关系以及知识深度,然后遍历各个节点生成各节点本体视图,形成支持设计人员知识需求的虚拟知识流,从而促进设计人员的知识共享。算法具有起始概念顺序无关性、属性指令累加性、完备性,计算时间随本体中概念数量线性增加。最后以飞机设计中的虚拟知识流为例说明了算法应用。     

A Longitudinal Study of Workspace Design for Knowledge Exploration and Exploitation in the Research and Development Process

In order to leverage organizational learning, scholars have already defined but are still discussing the interpretation of two different learning types, exploration and exploitation. Exploring new frontiers across knowledge domains and maintaining the balance with exploiting the existing knowledge is critical for the prosperity of an organization. The spatial dimension of organizational learning considers that proximity of employees has an influence on their learning activities, but from a rather macro perspective without taking workspace design into account. We account for these issues by examining the impact of workspace design on knowledge exploration and exploitation on the micro level at distinct stages along the value chain (i.e., the research, development and project market team unit) of Novartis, a pharmaceutical company. In a longitudinal study, employees of the three cases have been interviewed and observed over the course of three years, before and after workspace redesign. With the change from a cellular to an open workspace, employees become closer and highly visible to each other, which influences knowledge work. As the cases occurred sequentially in time, design principles were derived. The findings suggest that exploitation is supported by workspace design that leads to high proximity inducing faster feedback cycles and first‐hand information. Exploration, however, is supported by workspace design that leads to high visibility triggering more cross‐functional interactions and thereby the variability of knowledge. The later the stage in the research and development process, the higher the need for balanced learning activities. This balance is well reflected in a ‘multi‐space’ workspace consisting of shared meeting areas, quiet zones, central staircases and integrated laboratories and desk areas.     

Ontology of systems and software engineering

Systems and software engineering have evolved in the last years in a process of consolidation of their specificities as engineering disciplines. This has resulted in research and inquiry on ontological and epistemological issues related to them. Due to the supporting nature of systems and software engineering with regards to other engineering disciplines, these aspects are also relevant to researchers and practitioners in other engineering domains. The contents of this issue provide a selection of research aimed at consolidating ontology of systems and software engineering as a useful and consistent body of knowledge.     

安全本体研究进展

本体概念已经被广泛地应用到人工智能系统、计算机科学、信息技术、生物医学等众多学科领域。对不同领域的本体分析构成了该领域知识表示体系的核心,同时本体研究促进了知识的共享和重用。将本体的概念应用在信息安全领域就形成了安全本体。主要从以下几个方面描述了安全本体:安全本体研究的现状及发展过程,构建安全本体的原则、方法和意义,现有的安全本体及分类,安全本体的描述语言,安全本体的应用。接着依据对现有的安全本体及其应用的分析,总结了安全本体的结构体系,描述了安全本体的知识表示及推理能力,并指出在构建安全本体、本体评估、本体学习和安全本体的应用等方面依然有待完善。     

Integrated Vehicle Configuration System—Connecting the domains of mass customization

Configuration design for mass customized vehicles necessitates the coordination of customer requirements, product characteristics, production processes, and logistics networks, in order to achieve rapid response to customer orders. Existing product configurators are mainly used as sales tools, and fail to account for the requirements of the entire customer order fulfillment process. In this regard, this paper proposes an Integrated Vehicle Configuration System (IVCS) to facilitate customer order processing based on information from multiple domains in a mass customization environment. An IVCS business model is proposed to incorporate the decision factors for configuration design related to different planning stages. The business model is supported by a comprehensive ontology framework, which enhances communications between different stakeholders involved in the order fulfillment process. The configuration approach is based on combinations of selective and generative rules and can be integrated with existing ERP systems. It also provides mechanisms to handle configuration rules that allow users to convert soft preferences into product specifications, bill-of-materials, and, furthermore, into logistics configurations. An example of a computerized configuration system showcases the process from customer engineering to design and production.