Research on the Approaches of Knowledge Integration in Team
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In knowledge management research field, knowledge characteristic and knowledge subject are two important dimensions. This article presents a model of knowledge integration approaches based on the two dimensions. We discuss the approaches which the elementary knowledge transforms into structural knowledge in team. Base on this model, we put forward two approaches of knowledge integration in team-knowledge connection approach and personal interaction approach.Knowledge management is a relatively young discipline. It has accumulated a valuable body-of-knowledge on how to structure and represent knowledge, or how to design socio-technical knowledge management systems. A wide variety of approaches and systems exist that are often not interoperable, and hence, prevent an easy exchange of the gathered knowledge. Industry standards, which have been accepted and are in widespread use are missing, as well as general concepts to describe common, recurring patterns of how to describe, structure, interrelate, group, or manage knowledge elements. In this chapter, we introduce the concepts “knowledge pattern” and “knowledge anti-pattern” to describe best and worst practices in knowledge management, “knowledge refactoring” to improve or change knowledge antipatterns, and “quality of knowledge” to describe desirable characteristics of knowledge in knowledge management systems. The concepts are transferred from software engineering to the field of knowledge management based on our experience from several knowledge management projects.
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Mathematical knowledge management
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Tacit Knowledge
Knowledge modeling
Mathematical knowledge management
Explicit knowledge
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The Digital Age has brought not only new tools but also several new methods. A Collaborative Knowledge Platform with a hybrid intelligent system may be the appropriate base of a knowledge management system to ensure inspiration and new knowledge for a professional group of individuals. The introduced concept contributes to Knowledge Collaboration and Knowledge Engineering. The method is a special form of Knowledge Engineering which involves combining machine learning algorithms with cased-based reasoning and the result is the transformation of personal knowledge to widely adaptable explicit knowledge. Individuals can learn informally while their learning route automatically generates data for reductive reasoning process, which finally leads to the opportunity of experience mining. A concept and an approach are suggested to improve the knowledge collaboration in innovative communities, and a creative problem solving process delivers the outcome in the development of a Knowledge Management System. Finally, some partial results of the design phase of an application are presented.
Explicit knowledge
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Abstract Collaborative engineering is knowledge work in the sense that engineers and managers must apply their knowledge to solve their problems and proceed with their work. The knowledge that is applied spans from the basic knowledge they learned from schools, application knowledge acquired from the industry, and experiential knowledge obtained from years of working experience. Most of the current research on collaborative engineering support focuses on providing communication and data sharing support for effective coordination. We argue that in order to increase the productivity of current practice of collaborative engineering, we need mechanisms that can not only facilitate information flow, but also provide active knowledge level support for engineers. Our research on KICAD — a Knowledge Infrastructure for Collaborative and Agent-based Design — attempts to develop a network of intelligent agents that capture knowledge from their associated human engineers and provide knowledge level support to them when needed. Among the issues involved in developing such a framework is the issue of knowledge management — how can we model knowledge, how can agents capture, update, manage, and utilize the knowledge for human support? In this paper, we first briefly introduce the KICAD research program and describe the issue of knowledge management in KICAD. After that we present a general knowledge application model (GKAM), the basic conceptual framework of knowledge management in KICAD. An example of applying GKAM in a prototype system will also be discussed.
Knowledge Sharing
Collaborative engineering
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The authors believe that current knowledge management practice significantly under-utilizes knowledge engineering technology, despite recent efforts to promote its use. They focus on two knowledge engineering processes: using knowledge acquisition processes to capture structured knowledge systematically; and using knowledge representation technology to store the knowledge, preserving important relationships that are far richer than those possible in conventional databases. To demonstrate the usefulness of these processes, we present a case study in which the drilling optimization group of a large oil and gas service company uses knowledge engineering practices to support the three facets of the knowledge management task: knowledge capture; knowledge storage; and knowledge deployment.
Knowledge Acquisition
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Knowledge is an important resource for successful decision-making process in the whole society today. The special procedures of control and management of knowledge therefore have to be used. In the area of knowledge management and knowledge engineering basic terms of these disciplines are data, information, knowledge and knowledge transformation. The knowledge can be defined as a dynamic human process of justifying personal beliefs. Knowledge is a product of successful decision-making process. Knowledge transformation is a spiralling process of interactions between explicit and tacit knowledge that leads to the new knowledge. Nonaka and all show, that the combination of these two categories makes possible to conceptualise four conversion steps: Socialisation, Externalisation, Combination and Internalisation (SECI model). Another model of knowledge creation is the Knowledge Transformation Continuum (BCI Knowledge Group) that begins with the articulation of a specific instruction representing the best way that a specific task, or series of tasks, should be performed. Knowledge modelling and knowledge representation is an important field of research also in Computer Science and Artificial Intelligence. The definition of knowledge in Artificial Intelligence is a noticeable different, because Artificial Intelligence is typically dealing with formalized knowledge (e.g. ontology). The development of knowledge-based systems was seen as a process of transferring human knowledge to an implemented knowledge base. Decision Support Systems (DSS), Geographical Information Systems (GIS) and Operations Research/Management Science (OR/MS) modelling process support decision-making process, therefore they also produce a new knowledge. A Decision Support Systems are an interactive computer-based systems helping decision makers complete decision process. Geographic Information Systems provide essential marketing and customer intelligence solutions that lead to better business decisions. Operational Research and Management Science (OR/MS) is methodology based on system theory and theory of modelling. The OR/MS models serve for better quantification and precision of decision-making process. In this contribution the role of DSS, GIS and OR/MS models in the process of knowledge creation will be explained. The tacit or explicit character of this knowledge and the process of its creation will be explained and discussed.
Tacit Knowledge
Commonsense knowledge
Explicit knowledge
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