Collaborative Knowledge Management Through Product Lifecycle: A Computational Perspective

ISBN-13: 9789811996252 / Angielski / Twarda / 2023 / 349 str.

Hongwei Wang; Gongzhuang Peng

Opis

This book not only presents the state-of-the-art research on knowledge modelling, knowledge retrieval and knowledge reuse, but also elaborates the Collaborative Knowledge Management (CKM) paradigm and the architecture for the next generation of knowledge management systems. Although knowledge management has been extensively studied, particularly in the fields of business management and engineering design, there is a lack of systematic methodologies for addressing the integrated and collaborative dimension of knowledge management during the collaborative process of designing and developing complex systems, products, processes and services. The rapid development of information technologies, together with their applications in engineering and management, has laid the foundation for a Collaborative Knowledge Management (CKM) paradigm. The book specifically discusses this paradigm from a computational perspective.By exploring specific research findings underpinning further CKM research and applications and describing methods related to hot research topics and new research areas, the book appeals to professionals, researchers and graduate students who are interested in knowledge management and related topics and who have a basic understanding of information technologies, computational methods, and knowledge management.

Spis treści

Chapter 1: Knowledge management through product lifecycle

The role of knowledge management through product lifecycle

The essence of knowledge and its relationships with data and information

The forms and characteristics of different kinds of knowledge

The variety of knowledge needs in the modern context

Knowledge management methodologies

 

Chapter 2: The collaborative knowledge management paradigm

2.1 Collaboration and knowledge management in modern product development

2.2 Distinctive features of collaborative knowledge management

2.3 A computational system framework for collaborative knowledge management

2.4 The key methods involved in the framework

2.5 The key enabling technologies for the next-generation of CKM systems

 

Chapter 3: Knowledge modelling methods

3.1 The role of knowledge modelling in a knowledge management

3.2 General features and structure of a knowledge model

3.3 Different knowledge modelling methods

3.4 Integrated knowledge models

3.5 Knowledge modelling for collaborative knowledge management

 

Chapter 4: Advanced knowledge retrieval methods

3.1 Differences between information retrieval and knowledge retrieval

3.2 The structure of a knowledge retrieval tool

3.3 Keyword-based retrieval methods

3.4 Retrieval of structured knowledge

3.5 Semantic retrieval methods

3.6 Development of retrieval algorithms and methods

 

Chapter 5: Knowledge reuse in collaborative knowledge management

5.1 Different levels of knowledge reuse

5.2 Reuse of knowledge objects

5.3 Knowledge reuse in an effective community

5.4 Automatic recommendation of knowledge records

5.5 Knowledge reuse in collaborative knowledge management

 

Chapter 6: Discussion

6.1: Features of collaborative knowledge management

6.2: Key methods and techniques required for such a paradigm

6.3: Further development in collaborative knowledge management

6.4: Recommendations for researchers and practitioners

O autorze

Dr. Hongwei Wang is a tenured full professor with Zhejiang University and the University of Illinois at Urbana–Champaign Joint Institute where he serves as the vice dean in research and academic lead in intelligent manufacturing. He received the B.S. degree in information technology and instrumentation from Zhejiang University, China, in 2004, the M.S. degree in control science and engineering from Tsinghua University, China, in 2007, and completed the Ph.D. degree in engineering design from the University of Cambridge, in 2010. Prio to joining Zhejiang University, he was a Senior Lecturer in engineering design with the University of Portsmouth, the United Kingdom. His research interests include knowledge engineering, industrial knowledge graph, intelligent and collaborative systems, and data-driven fault diagnosis. His research in these areas has been published in over 120 peer-reviewed papers in well-established journals and international conferences. He has delivered two keynote speeches and has won four best paper awards in international conferences.

Dr. Gongzhuang Peng is an assistant professor with the Engineering Research Institute, University of Science and Technology Beijing. He received the B.S. degree from School of Automation Science and Electrical Engineering, Beihang University, Beijing, China, in 2012, and the Ph.D. degree from the Department of Automation, Tsinghua University, Beijing, China, in 2018. His research interest concerns knowledge management and smart manufacturing. He has published near 40 peer-reviewed papers in international journals and international conferences, and has won two best paper awards in international conferences.