A knowledge engineering approach to knowledge management

Knowledge management facilitates the capture, storage, and dissemination of knowledge using information technology. Methods for managing knowledge have become an important issue in the past few decades, and the KM community has developed a wide range of technologies and applications for both academic research and practical applications. In this paper, we propose a knowledge engineering approach (KMKE) to knowledge management. First, a knowledge modeling approach is used to organize and express various types of knowledge in a unified knowledge representation. Second, a verification mechanism is used to verify knowledge models based on the formal semantics of the knowledge representation. Third, knowledge models are classified and stored in a hierarchical ontology system. Fourth, a knowledge query language is designed to enhance the dissemination of knowledge. Finally, a knowledge update process is applied to modify the knowledge storage with respect to users’ needs. A knowledge management system for computer repair is used as an illustrative example.     

Constructing a hybrid Kansei engineering system based on multiple affective responses: Application to product form design

This study proposes an expert system, which is called hybrid Kansei engineering system (HKES) based on multiple affective responses (MARs), to facilitate the development of product form design. HKES is consists of two sub-systems, namely forward Kansei engineering system (FKES) and backward Kansei engineering system (BKES). FKES is utilized to generate product alternatives and BKES is utilized to predict affective response of new product designs. Although the idea of HKES and similar hybrid systems have already been applied in various fields, such as product design, engineering design, and system optimization, most of existing methodologies are limited by searching optimal design solutions using single-objective optimization (SOO), instead of multi-objective optimization (MOO). Hence the applicability of HKES is limited while adapting to real-world problems, such as product form design discussed in this paper. To overcome this shortcoming, this study integrates the methodologies of support vector regression (SVR) and multi-objective genetic algorithm (MOGA) into the scheme of HEKS. BKES was constructed by training SVR prediction model of every single affective response (SAR). The form features of these product samples were treated as input data while the average utility scores obtained from all the consumers were used as output values. FKES generates optimal design alternatives using the MOGA-based searching method according to MARs specified by a product designer as the system supervisor. A case study of mobile phone design was given to demonstrate the analysis results. The proposed HKES based on MARs can be applied to a wide variety of product design problems, as well as other MOO problems involving with subjective human perceptions.     

A user requirement driven framework for collaborative design knowledge management

In today’s competitive global marketplace and knowledge-based economy, user requirement becomes an important input information for enterprises to develop new product and a critical factor to drive product collaborative design evolution. Meanwhile, there remains no consensus on how best to support knowledge activities and significant challenges to establishing design information management facing to rapid collaborative product development with dynamic user requirement. This paper introduces solutions for designer to deal with dynamic user requirement information through requirement evaluation and prediction method. In this study, we propose a user requirements-oriented knowledge management concept that is based on a four level hierarchy map model with special regard to knowledge collaboration and information communication. Furthermore, a novel distributed concurrent and interactive user requirement database was constructed, and the framework driven by user requirement was put forward to support collaborative design knowledge management. Finally, the service robot design project of a start-up company is used as a case study to explain the implementation of proposed framework.     

Comparison of multi-objective evolutionary algorithms in hybrid Kansei engineering system for product form design

Understanding the affective needs of customers is crucial to the success of product design. Hybrid Kansei engineering system (HKES) is an expert system capable of generating products in accordance with the affective responses. HKES consists of two subsystems: forward Kansei engineering system (FKES) and backward Kansei engineering system (BKES). In previous studies, HKES was based primarily on single-objective optimization, such that only one optimal design was obtained in a given simulation run. The use of multi-objective evolutionary algorithm (MOEA) in HKES was only attempted using the non-dominated sorting genetic algorithm-II (NSGA-II), such that very little work has been conducted to compare different MOEAs. In this paper, we propose an approach to HKES combining the methodologies of support vector regression (SVR) and MOEAs. In BKES, we constructed predictive models using SVR. In FKES, optimal design alternatives were generated using MOEAs. Representative designs were obtained using fuzzy c-means algorithm for clustering the Pareto front into groups. To enable comparison, we employed three typical MOEAs: NSGA-II, the Pareto envelope-based selection algorithm-II (PESA-II), and the strength Pareto evolutionary algorithm-2 (SPEA2). A case study of vase form design was provided to demonstrate the proposed approach. Our results suggest that NSGA-II has good convergence performance and hybrid performance; in contrast, SPEA2 provides the strong diversity required by designers. The proposed HKES is applicable to a wide variety of product design problems, while providing creative design ideas through the exploration of numerous Pareto optimal solutions.     

Applying Kansei engineering to design logistics services – A case of home delivery service

Home delivery service (HDS) has grown fast in recent years because more and more customers engage in Internet and television shopping. Moreover, due to the rising competition in the HDS sector, service providers need to put more concentration on the continual improvement and differentiation of service to maintain the competitive advantage. After experiencing the current services, customers are always expecting the new ones. Thus, the idea of new service design never ends. Designers always have to insight into the real feelings of customers so that they could design the new service, which is able to meet customers' expectation. Kansei engineering is a means to transform real voices of customers into the product and service design. This method is able to quantify the relationship between feelings represented by simple words and design elements. In this study, the Partial Least Square (PLS) is used to analyze the relationships between the real feelings of customers and characteristics of HDS. Finally, this study combines those characteristics, which are strongly related to important feelings, to obtain the original idea of new design of HDS.Relevance to industryAs stated in this abstract, our study offers an exemplification on applying Kansei engineering to design service in service industries. Specifically, along with the application of Kansei engineering, the research findings offer a technique for service design of service industries.     

A meta-model for knowledge configuration management to support collaborative engineering

This paper focuses on industrial design and simulation processes especially in automotive and aerospace areas. Designers use business models (called expert models) such as CAD (computed aided design) and CAE (computed aided engineering) models to optimize and streamline the engineering process. Each expert model contains information such as parameters, expert rules, mathematic relations (parametric models, for example) which are shared by several users and in several different domains (mechanical, thermal, acoustic, fluid, etc.). This information is exploited at the same time in a concurrent engineering context. It is the basis of an imperfect collaboration process due to the fact that existing tools do not manage encapsulated information well and are unable to ensure that parameters and rules are consistent (same value of parameters for example) throughout different heterogeneous expert models. In this context, we propose an approach to manage knowledge using configurations synchronized with expert models which enable designers to use parameters consistently in a collaborative context. Our approach is called KCModel (knowledge configuration model): it allows acquisition, traceability, re-use and consistency of explicit knowledge used in configuration.     

A framework to integrate design knowledge reuse and requirements management in engineering design

This paper presents a framework to integrate requirements management and design knowledge reuse. The research approach begins with a literature review in design reuse and requirements management to identify appropriate methods within each domain. A framework is proposed based on the identified requirements. The framework is then demonstrated using a case study example: vacuum pump design. Requirements are presented as a component of the integrated design knowledge framework. The proposed framework enables the application of requirements management as a dynamic process, including capture, analysis and recording of requirements. It takes account of the evolving requirements and the dynamic nature of the interaction between requirements and product structure through the various stages of product development.     

Newsmap: a knowledge map for online news

Information technology has made possible the capture and accessing of a large number of data and knowledge bases, which in turn has brought about the problem of information overload. Text mining to turn textual information into knowledge has become a very active research area, but much of the research remains restricted to the English language. Due to the differences in linguistic characteristics and methods of natural language processing, many existing text analysis approaches have yet to be shown to be useful for the Chinese language. This research focuses on the automatic generation of a hierarchical knowledge map NewsMap, based on online Chinese news, particularly the finance and health sections. Whether in print or online, news still represents one important knowledge source that people produce and consume on a daily basis. The hierarchical knowledge map can be used as a tool for browsing business intelligence and medical knowledge hidden in news articles. In order to assess the quality of the map, an empirical study was conducted which shows that the categories of the hierarchical knowledge map generated by NewsMap are better than those generated by regular news readers, both in terms of recall and precision, on the sub-level categories but not on the top-level categories. NewsMap employs an improved interface combining a 1D alphabetical hierarchical list and a 2D Self-Organizing Map (SOM) island display. Another empirical study compared the two visualization displays and found that users' performances can be improved by taking advantage of the visual cues of the 2D SOM display.     

A knowledge based reliability engineering approach to manage product safety and recalls

PurposeManaging processed food products’ safety and recall is a challenge for industry and governments. Contaminated food items can create a significant public health hazard with potential for acute and chronic food borne illnesses. This industry study examines the challenges companies face while managing a processed food recall situation and devise a responsive and reliable knowledge management framework for product safety and recall supply chain for the focal global manufacturing and distribution enterprise.MethodDrawing upon the knowledge management and product safety and recall literature and reliability engineering theory, this study uses a holistic single case based approach to develop a knowledge management framework with Failure Mode Effects and Criticality Analysis (FMECA) decision model. This knowledge management decision framework facilitates analysis of the root causes for each potential major recall issue and assesses the reliability of the product safety and recall supply chain system and its critical components.ResultsThe main reasons highlighted for a recall and associated failure modes in a knowledge management framework are to devise appropriate deployment of resources, technology and procedures to recall supply chain. This study underscores specific information described by managers of a global processed food manufacturer and their perspectives about the product safety and recall process, and its complexities. Full scale implementation of product safety and recall supply chain in the proposed knowledge management framework after the current pilot study will be carried out eventually through expert systems. This operational system when fully implemented will capture the essence of decision making environments comprising goals and objectives, courses of action, resources, constraints, technology and procedures.ImplicationsThe study recognizes the significance of communication, integration, failsafe knowledge management process design framework, leveraging technology such as Radio Frequency Identification (RFID) within all levels of supply chain for product traceability and the proactive steps to help companies successfully manage a recall process and also reestablish trust among the consumers. The proposed knowledge management framework can also preempt product recall by acting as an early warning system. A formal knowledge management framework will enable a company’s knowledge be cumulative for product safety and recall and serve as an important integrating and coordinating role for the organization.     

Knowledge management systems and organizational knowledge processing challenges: A field experiment

This paper investigates the appropriateness of knowledge management system (KMS) designs for different organizational knowledge processing challenges. Building on the theory of task-technology fit (TTF), we argue that different KMS designs are more effective for different knowledge tasks. An exploratory field experiment was conducted in the context of Internet-based knowledge sharing services to provide empirical support for our hypotheses. The results of our experiment show that a KMS designed to support the goal GENERATE is more appropriate for divergent type knowledge problems because of its affordances for iterative brainstorming processes. Conversely, for convergent type knowledge processing challenges, a KMS with the goal CHOOSE that supports the ability to clarify and to analyze is more effective.     

基于犹豫模糊语言术语集和粒子群优化算法的产品造型设计感性评价方法

产品造型感性评价反映了用户的意象感知,具有模糊性与不确定性,用户常难以准确描述其感性偏好而表现出犹豫.针对该问题,引入犹豫模糊语言术语集(HFLTSs)描述用户感性评价,基于其数学算子构建犹豫模糊语言共识模型以测度用户认知一致性程度,借助粒子群优化算法(PSO)实现非共识条件下用户评价矩阵的优化与共识达成,通过逼近理想...     

A proposal of the event-related potential method to effectively identify kansei words for assessing product design features in kansei engineering research

The effective acquisition of kansei words for assessing product design features plays a decisive role in knasei engineering research, whereas previous studies rarely give a full understanding of how to effectively grasp the related kansei words. The existing findings revealed that traditional methods based on questionnaire surveys might have trouble and limitations in kansei words acquisition. The process requires the active involvement of users, which could be time-consuming, expensive, knowledge and labor intensive. Hence, there still remains a need for an efficient method that can effectively identify kansei words for assessing product design features in kansei engineering study. The N400, an event-related potential (ERP), can be triggered by semantic violations in visual stimulus materials. The present study investigates whether N400 can be used as an electrophysiological measurement to effectively identify kansei words for assessing product design features in kansei engineering research. This study takes the SUVs’ pictures as the prime stimulus and two relatedness-match levels of kansei words from a high level to a low level as the target stimulus, including the unrelated kansei words and related kansei words. It is shown that the related kansei words elicit a shorter N400 amplitude than the unrelated kansei words. Moreover, the N400 exhibits the different scalp distributions between related and unrelated kansei words. These findings indicate that the N400 could be used to compensate limitations of conventional questionnaire surveys in kansei words acquisition for assessing product design features.Relevance to industryWith the assistance of N400 neural measurement in kansei words acquisition, kansei engineering will advance further as a modernized technology in kansei product design. The method proposed in this study uses N400 to effectively identify kansei words, which can be applied to kansei engineering research and improve the product development process.     

Causal design knowledge: Alternative representation method for product development knowledge management

This paper presents a mathematical comparison of procedural knowledge and causal knowledge, and discusses the potential roles and feasibility of causal knowledge across product development knowledge management. Since reuse of knowledge is so important in product development, various knowledge management approaches have been introduced. Most of the product design knowledge is represented by procedural knowledge, which unfortunately requires cumbersome processes to define, and is typically inadequate for representing the kind of knowledge generated during the product development process. A causal knowledge representation, however, can help us to overcome this limitation and is an alternative formalism for representing product design knowledge. In this paper we compare the procedural and causal knowledge representations. We present the mathematical definitions of two knowledge paradigms, then mathematically describe the relationship between the two. Both knowledge paradigms are then compared based on the perspective of knowledge expression, decision alternative representation, reasoning capability, and knowledge cultivation. This paper concludes that causal knowledge representation is superior to procedural knowledge representation based on the four perspectives. Finally, the knowledge systems are modeled using Systems Modeling Language (SysML), and we present a case study that demonstrates the causal knowledge features using a realistic example from industry.     

Capturing and reusing knowledge in engineering change management: A case of automobile development

The development of complex products, such as automobiles, involves engineering changes that frequently require redesigning or altering the products. Although it has been found that efficient management of knowledge and collaboration in engineering changes is crucial for the success of new product development, extant systems for engineering changes focus mainly on storing documents related to the engineering changes or simply automating the approval processes, while the knowledge that is generated from collaboration and decision-making processes may not be captured and managed easily. This consequently limits the use of the systems by the participants in engineering change processes. This paper describes a model for knowledge management and collaboration in engineering change processes, and based on the model, builds a prototype system that demonstrates the model’s strengths. We studied a major Korean automobile company to analyze the automobile industry’s unique requirements regarding engineering changes. We also developed domain ontologies from the case to facilitate knowledge sharing in the design process. For achieving efficient retrieval and reuse of past engineering changes, we used a case-based reasoning (CBR) with a concept-based similarity measure.

Time identification of design knowledge push based on cognitive load measurement

The reuse of multidisciplinary design knowledge is pivotal in product development because of the increasingly fierce market competition. It can assist designers, particularly those who lack sufficient experience, in making correct decisions and achieving rapid design. Traditionally, designers primarily acquire design knowledge through information retrieval, which is typically time-consuming and inefficient. A solution that is widely considered to overcome the deficiencies of traditional knowledge retrieval approaches and actively provide designers with necessary knowledge is knowledge push. However, achieving the timely push of required knowledge to designers during the design process remains a challenging task. Accordingly, this paper presents a time identification method based on cognitive load measurement to identify the suitable time for design knowledge push. First, behavioral indicators related to the changes in cognitive load are identified by investigating the influence of the load on three types of behaviors: mouse dynamics, keystroke dynamics, and emotional states. Second, the possibility and efficacy of inferring the cognitive load by simultaneously and unobtrusively tracking the three aforementioned behaviors are considered through behavioral observations. Finally, predicting the knowledge push time based on the cognitive load using classification algorithms is investigated. The experimental results indicate that the accuracy of the proposed method in inferring the cognitive load is 55%, and that of push time is 83%.     

基于信息特征的需求开发与管理研究

如何荻取用户的真正需求是需求工程的难点.需求工程是创造性的高层次知识活动,根据知识等级层次,信息最接近于事实且涉及专业知识最少.以信息方式交流需求具有容易理解的优势,便于减少和消除人们因知识背景不同而产生的交流困难.应用信息特征探讨了几点有效提高需求开发与管理效率的对策.     

Understanding the effect of knowledge management strategies on knowledge management performance: A contingency perspective

The universalistic perspective research on employing a unidimensional knowledge management (KM) strategy has yielded conflicting findings and recommendations in different contexts. This study proposes a contingency model for investigating the effects of KM strategies on KM performance to resolve these contradictions. Drawing on the knowledge-based view (KBV) of the firm, which identifies knowledge type and origin as two key KM dimensions, this study first defines four KM strategies: external codification, internal codification, external personalization, and internal personalization. A multiple contingency model of KM strategy is then developed based on a technology–organization–environment framework. This study proposes that the effectiveness of each KM strategy depends on both external and internal contextual conditions, namely, environmental knowledge intensity and organizational information systems (IS) maturity. To test and validate the contingency model, we analyze data from 141 firms to explain the effects of KM strategies on KM performance. Our results reveal three KM strategies, not including the internal personalization strategy, which have a significant association with KM performance in their hypothesized contexts. This study expands KM strategy research by theoretically developing an advanced contingency model aligned with external and internal contexts and by providing valuable practical suggestions to managers for selecting a KM strategy based on multiple contingencies related to the external and internal conditions of a firm.     

Knowledge representation framework for interactive capture and management of reflection process in product concepts development

A design process can be characterized by reflection-in-action; that is, the process consists of a series of problem solving activities and each is embodied with a problem and a solution. In this process, a designer represents a hypothetical concept on each design alternative, deploys and verifies the concept from multiple viewpoints considering other alternatives, and modifies it. An advanced integrated design environment should be based on a representation framework that embodies this process of reflection in concept development, which usually remains in the realm of the designer’s tacit knowledge. This paper proposes a knowledge representation framework for an integrated design environment, named DRIFT (Design Representation Integration Framework of Three layers), which interactively captures and manages reflection processes of generating and verifying design concepts. The core of DRIFT is a three-layered design process model of actions, operations, and argumentation. This model integrates various design tools and captures performed design activities. The action level captures the sequence of design operations. The model operation level captures the transition of design states, recording a design snapshot over design tools, which are integrated through ontology-based representation of design concepts. The argumentation level captures the process of defining problems and corresponding alternative solutions. Integration of three levels with a template of design operation extracted from Design-For-X approaches enables a proposed system to interactively and efficiently capture and manage the process of design concept development through operations over design tools. A design operation template works to limit the number of links between the three levels remaining easy to manage its semantics. This paper also demonstrates a prototype implementation of DRIFT and its application to conceptual design of a small mechatronic system with a system modeling method. The paper concludes with a discussion of some future issues.     

School knowledge management framework and strategies: The new perspective on teacher professional development

During the epoch of knowledge-based economy and knowledge management, teachers must learn in order to improve professional development. The paper discusses on the problems of teacher professional development in China, and analyze on the relationship among teacher learning, teacher professional development and school knowledge management. From the perspective of schools, this study develops school knowledge management framework to facilitate teacher learning and improve teacher professional development, in the basis of this framework, schools’ knowledge management strategies are presented, and provide references for administrators of schools. The result reveals the knowledge management strategies to improve teacher professional development, including school organizational reforger and knowledge leaders, constructing learning school and organization learning culture, establishing teacher knowledge management system of teacher professional development, encouraging team learning, teaching cooperation and knowledge sharing, establishing performance assessment mechanism of knowledge applications and development.     

ConKMeL: a contextual knowledge management framework to support multimedia e-Learning

As the latest stage of learning and training evolution, e-Learning is supposed to provide intelligent functionalities not only in processing multi-media education resources but also in supporting context-sensitive pedagogical education processes. Towards providing an integrated solution for intelligent multimedia e-Learning, this paper presents a context-aware knowledge management framework named ConKMeL. Proposed framework features a semantic context-based approach for representing and integrating information and knowledge in e-Learning. Requirement analysis in university e-Learning environments shows that knowledge communications are usually in a hybrid mode across different conceptual levels. Based on the fact, a multi-layer contextual knowledge representation model called KG (knowledge graph) is presented. Corresponding key issues in development such as context-based knowledge retrieval and logical knowledge interpretation are discussed. On the application side, a scenario-based learning case study is shown to demonstrate the concepts and techniques developed in the ConKMeL framework.