Knowledge Management: Learning from Knowledge Engineering / Edition 1

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Overview

Knowledge Management (KM) is strongly rooted in the discipline of Knowledge Engineering (KE), which in turn grew partly out of the artificial intelligence field. Despite their close relationship, however, many KM specialists have failed to fully recognize the synergy or acknowledge the power that KE methodologies, techniques, and tools hold for enhancing the state of the art in Knowledge Management.

Knowledge Management: Learning from Knowledge Engineering addresses this vacuum. It gives concise, practical information and insights drawn from the author's many years of experience in the fields of expert systems and Knowledge Management. Based upon research, analyses, and illustrative case studies, this is the first book to integrate the theory and practice of artificial intelligence and expert systems with the current organizational and strategic aspects of Knowledge Management.

The time has come for Knowledge Management professionals to appreciate the synergy between their work and the work of their counterparts in Knowledge Engineering. Knowledge Management: Learning from Knowledge Engineering is the ideal starting point for those in KM to learn from and exploit advances in that field, and thereby advance their own.

Addresses gaps in the literature concerning the methodologies, techniques and tools of knowledge engineering that can enhance the state of the art in knowledge management. Includes illustrative case studies, research, analysis, and an integration of theory and practice of expert systems and artificial intelligence.

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Editorial Reviews

Booknews
Addresses the synergies between the disciplines of knowledge engineering and knowledge management, integrating the theory and practice of artificial intelligence and expert systems with the current organizational and strategic aspects of knowledge management. Appendices offer case studies, including a knowledge management strategy for the US Federal Communications Commission and a partial knowledge audit for the US Social Security Administration. Of interest to knowledge managers, knowledge engineers, and directors of intellectual capital, as well as students. Liebowitz teaches information systems at the University of Maryland-Baltimore County. Annotation c. Book News, Inc., Portland, OR (booknews.com)
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Product Details

  • ISBN-13: 9780849310249
  • Publisher: CRC Press
  • Publication date: 3/28/2001
  • Edition description: New Edition
  • Edition number: 1
  • Pages: 152
  • Product dimensions: 6.40 (w) x 9.70 (h) x 0.54 (d)

Meet the Author

Jay Liebowitz: University of Maryland, Baltimore, Maryland, USA.
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Table of Contents

Knowledge Management and Knowledge Engineering: Working Together
Knowledge Mapping and Knowledge Acquisition
Knowledge Taxonomy versus Knowledge Ontology and Representation
The Knowledge Management Life Cycle versus the Knowledge Engineering Life Cycle
Knowledge-Based Systems and Knowledge Management
Intelligent Agents and Knowledge Dissemination
Knowledge Discovery and Knowledge Management
People and Culture: Lessons Learned from AI to Help Knowledge Management
Implementing Knowledge Management Strategies
Expert Systems and AI: Integral Parts of Knowledge Management
Appendix A: A Knowledge Management Strategy for the U.S. Federal Communications Commission
Appendix B: Knowledge Management Receptivity
Appendix C: Modeling the Intelligence Analysis Process for Intelligent User Agent Development
Appendix D: Planning and Scheduling in the Era of Satellite Constellation Missions: A Look Ahead
Index

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