Knowledge Management : Learning From Knowledge Engineering

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. 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) Front cover......Page 1 Preface......Page 4 Author's bio......Page 6 Contents......Page 8 chapter one. Knowledge management and knowledge engineering: working together......Page 10 chapter two. Knowledge mapping and knowledge acquisition......Page 16 chapter three. Knowledge taxonomy vs. knowledge ontology and representation......Page 24 chapter four. The knowledge management life cycle vs, the knowledge engineering life cycle......Page 30 chapter five. Knowledge-based systems and knowledge management......Page 46 chapter six. Intelligent agents and knowledge dissemination......Page 52 chapter seven. Knowledge discovery and knowledge management......Page 58 chapter eight. People and culture: lessons learned from AI to help knowledge management......Page 66 chapter nine. Implementing knowledge management strategies......Page 72 chapter ten. Expert systems and AI: integral parts of knowledge management......Page 78 appendix A: A knowledge management strategy for the U.S. Federal Communications Commission*......Page 84 appendix B*: Partial knowledge audit for the U.S. Social Security Administration......Page 102 appendix C: Modeling the intelligence analysis process for intelligent user agent development*......Page 112 appendix D: Planning and scheduling in the era of satellite constellation missions:a look ahead*......Page 122 Index......Page 142 Back cover......Page 150 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 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 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. Ch. 1. Knowledge management and knowledge engineering: working together. 1. Ch. 2. Knowledge mapping and knowledge acquisition. 7. Ch. 3. Knowledge taxonomy vs. knowledge ontology and representation. 15. Ch. 4. The knowledge management life cycle vs. the knowledge engineering life cycle. 21. Ch. 5. Knowledge-based systems and knowledge management. 37. Ch. 6. Intelligent agents and knowledge dissemination. 43. Ch. 7. Knowledge discovery and knowledge management. 49. Ch. 8. People and culture: lessons learned from AI to help knowledge management. 57. Ch. 9. Implementing knowledge management strategies. 63. Ch. 10. Expert systems and AI: integral parts of knowledge management. 69. App. A.A knowledge management strategy for the U.S. Federal Communications Commission. 75. App. B. Partial knowledge audit for the U.S. Social Security Administration. 93. App. C. Modeling the intelligence analysis process for intelligent user agent development. 103. App. D. Planning and scheduling in the era of satellite constellation missions: a look ahead. 113. . Index. 133 Knowledge Management: Learning from Knowledge Engineering helps knowledge managers and those involved in knowledge management initiatives improve the current state-of-the-art in developing knowledge management systems. The book explores the need for applying knowledge engineering techniques to knowledge management. The focus is on sharing and leveraging knowledge internally and externally The text attempts to integrate the foundation theory and practice in knowledge engineering, expert systems, and artificial intelligence with the latest thinking on organizational and strategic aspects of the emerging discipline of "knowledge management."