Modeling the Effects of Existing Knowledge on the Creation of New Knowledges

Abstract

Advancing technologies and constantly changing market needs induce competition between organizations and force enterprises to adopt better management methods to improve operational performance to survive and make profits. Knowledge management has been widely recognized as one of the effective methods to achieve the above objective. Previous researches of knowledge management chiefly focused on qualitative approaches, and largely stressed key success factors of knowledge management, such as the infrastructure of information technology, the design of the knowledge management system, deployment of motivation schemes, and the like. Among them, the knowledge spiral of socialization, externalization, connection, and internalization are the core of researches and discussions. However, early studies only disclosed the necessary cyclical phenomenon of knowledge management. A quantitative method revealing how the existing knowledge can support the creation of new knowledge or upgrade current knowledge remains non-existent. To bridge this gap, this study proposes a mathematical model which can quantify the supporting effects of the existing knowledge on the creation of new knowledge. The model evaluates knowledge from the perspectives of complexity (knowledge type/knowledge area) and depth (knowledge level), and the results of the example illustrate that the proposed model can be an effective method of measuring the usefulness of the existing knowledge.

Design and development of Knowledge Based System for Integrated Maintenance Strategy and Operations

“…Decision-making requires knowledge and expertise retrieved from the experts on that particular area. Unfortunately, such knowledge might be lost when they leave the organisation (Korposh et al, 2011; Turban et al, 2008). Therefore, a KB system is proposed to imitate the thinking process of experts to generate a reasonable conclusion.…”

Description logic–based knowledge merging for concrete- and fuzzy-domain ontologies

“…Proliferation of data has created a completely new and different area of knowledge management 17 requiring the extraction of knowledge from abundant data and the organization and merging of this knowledge with existing knowledge. Existing knowledge supports organizations in creating new knowledge and updating the overall knowledge base 18 .…”

“…Existing knowledge supports organizations in creating new knowledge and updating the overall KB. 18 Knowledge discovery includes discovering implicit knowledge from the data, often using data mining techniques to extract knowledge from data sources. Exhaustive literature surveys illustrate that KM frameworks, knowledge-based systems (KBS), ICT, artificial intelligence and expert systems, database technology and so on have all been adopted by enterprises to exploit knowledge in order to solve their current problems and enhance their expertise.…”

Intelligent knowledge recommending approach for new product development based on workflow context matching

“…Collaborative new product development (NPD) is a complex, geographically distributed, and highly intellectual process that provides automation challenges in the areas of process and knowledge management (Chandrasegaran et al, 2013). Many enterprises have accumulated large amount of product development cases, which are actually of great value to new projects (Korposh et al, 2011). Unfortunately, due to the knowledge-intensive nature of NPD processes, there is a virtual explosion in the amount of raw data available to the designers (Alexopoulos et al, 2011; Harmon, 2010; Vanderfeesten et al, 2011).…”