Artificial Intelligence And Knowledge Management
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Applying Knowledge Management
The wholesale capture and distribution of knowledge over the last thirty years has created an unprecedented need for organizations to manage their knowledge assets. Knowledge Management (KM) addresses this need by helping an organization to leverage its information resources and knowledge assets by "remembering" and applying its experience. KM involves the acquisition, storage, retrieval, application, generation, and review of the knowledge assets of an organization in a controlled way. Today, organizations are applying KM throughout their systems, from information management to marketing to human resources. Applying Knowledge Management: Techniques for Building Corporate Memories examines why case-based reasoning (CBR) is so well suited for KM. CBR can be used to adapt solutions originally designed to solve problems in the past, to address new problems faced by the organization. This book clearly demonstrates how CBR can be successfully applied to KM problems by presenting several in-depth case-studies. Ian Watson, a well-known researcher in case-based reasoning and author of the introductory book, Applying CBR: Techniques for Enterprise Systems has written this book specifically for IT managers and knowledge management system developers. * Provides 7 real-world applications of knowledge management systems that use case-based reasoning techniques. * Presents the technical information needed to implement a knowledge management system. * Offers insights into the development of commercial KM CBR applications * Includes information on CBR software vendors, CBR consultants and value added resellers
Digital Technology Advancements in Knowledge Management
Knowledge management has always been about the process of creating, sharing, using, and applying knowledge within and between organizations. Before the advent of information systems, knowledge management processes were manual or offline. However, the emergence and eventual evolution of information systems created the possibility for the gradual but slow automation of knowledge management processes. These digital technologies enable data capture, data storage, data mining, data analytics, and data visualization. The value provided by such technologies is enhanced and distributed to organizations as well as customers using the digital technologies that enable interconnectivity. Today, the fine line between the technologies enabling the technology-driven external pressures and data-driven internal organizational pressures is blurred. Therefore, how technologies are combined to facilitate knowledge management processes is becoming less standardized. This results in the question of how the current advancement in digital technologies affects knowledge management processes both within and outside organizations. Digital Technology Advancements in Knowledge Management addresses how various new and emerging digital technologies can support knowledge management processes within organizations or outside organizations. Case studies and practical tips based on research on the emerging possibilities for knowledge management using these technologies is discussed within the chapters of this book. It both builds on the available literature in the field of knowledge management while providing for further research opportunities in this dynamic field. This book highlights topics such as human-robot interaction, big data analytics, software development, keyword extraction, and artificial intelligence and is ideal for technology developers, academics, researchers, managers, practitioners, stakeholders, and students who are interested in the adoption and implementation of new digital technologies for knowledge creation, sharing, aggregation, and storage.
Artificial Intelligence and Knowledge Management
Artificial intelligence and knowledge management have transformed the process of knowledge circulation and database management in different business enterprises and corporate organizations. Some of the significant topics discussed in the chapters of this book are AI planning strategies and tools, AI tools for information processing, data mining, knowledge-based systems, etc. It explores the innovative concepts and advancements in these emerging fields. The book is an invaluable source of knowledge for students and researchers involved in this field at various levels.