CSIT600G: Knowledge Management (3 credits)
Human Language Technology Center
Department of Computer Science
Hong Kong University of Science & Technology
HKUST, Clear Water Bay, Hong Kong
dekai@cs.ust.hkhttp://www.cs.ust.hk/~dekai
© 2007.06.04 Dekai Wu
Introduction
Welcome to CSIT600G for Summer 2007! We meet Tuesdays and Thursdays in Room 6580 (lifts 27-28) on the following dates:
- June 5, 7, 12, 14, 19
- July 17, 19, 24, 26, 31
- August 2, 7, 9
Track 8 (FT class admitted Fall 2006) meets 15:00--17:50, and Track 9 (PT class admitted Fall 2006) meets 19:30--22:20.
Course home page: http://www.cs.ust.hk/~dekai/600G/
Forum: http://comp151.cse.ust.hk/~dekai/content/?q=forum/1
Course description
Thorough coverage of the latest theory and practice of Knowledge Management (KM), with an integrated interdisciplinary presentation that makes sense of the confusingly wide variety of computer science and business KM perspectives arising simultaneously from artificial intelligence, information systems, and organizational behavior. Solidly covers the "hard" technical components of computer tools and technology for managing knowledge, without losing sight of the "soft" management needs and challenges in leveraging knowledge effectively within an organization. Critically evaluates the nature, computer representation, access, and utilization of knowledge versus information within a human context. Essential preparation for managerial, technical, and systems workers alike in today's modern knowledge-based economy.
Course objective
The goal of this course is to give you a solid foundation covering the major problems, challenges, concepts, and techniques dealing with the organization and management of knowledge with the help of computers. Upon satisfactory completion of this course, you can expect to:
- Understand the fundamental concepts in the study of knowledge and its creation, acquisition, representation, dissemination, use and re-use, and management.
- Appreciate the role and use of knowledge in organizations and institutions, and the typical obstacles that KM aims to overcome.
- Know the core concepts, methods, techniques, and tools for computer support of knowledge management.
- Understand how to apply and integrate appropriate components and functions of various knowledge management systems.
- Be prepared for further study in knowledge generation, engineering, and transfer, and in the representation, organization, and exchange of knowledge.
- Critically evaluate current trends in knowledge management and their manifestation in business and industry.
You will understand how to exploit systems to offer support to modern knowledge workers, in particular with respect to the rapidly increasing overload of knowledge and information that is available and necessary to stay competitive for many tasks. This includes support for traditional Knowledge Management tasks, such as the grouping of related documents into categories or hierarchies, the generation of dictionaries and ontologies, or the construction of knowledge networks through references and citations. We also study how methods and techniques that rely on computers can be used to augment the human-centered tasks. Examples for such approaches are automatic content- and usage-based categorization of documents, collaborative filtering, or the extraction of relevant key phrases from documents, case-based reasoning, data and text mining, and information extraction and summarization.
In contrast to knowledge-based systems, where computers manipulate and generate knowledge as standalone agents, the goal of Knowledge Management is to use computers as practical tools for activities mainly performed and directed by humans within real modern-day knowledge-based organizations.
"If we only knew what we know, we'd all be a lot smarter." - Lew Platt, CEO, Hewlett Packard
Textbook
- Irma Becerra-Fernandez, Avelino Gonzalez, Rajiv Sabherwal (2004). Knowledge Management Challenges, Solutions, and Technologies (edition with accompanying CD). Prentice Hall. ISBN: 0-13-109931-0.
- Supplementary notes and papers.
Reference books/materials
- Elias M. Awad, Hassan M. Ghaziri (2004). Knowledge Management. Prentice Hall. ISBN: 0-13-034820-1.
- Ian Watson (2002). Applying Knowledge Management: Techniques for Building Corporate Memories. Morgan Kaufmann. ISBN: 1558607609.
- Madanmohan Rao (2004). Knowledge Management Tools and Techniques: Practitioners and Experts Evaluate KM Solutions. Butterworth-Heinemann. ISBN: 0750678186.
- Amrit Tiwana (2002). The Knowledge Management Toolkit: Orchestrating IT, Strategy, and Knowledge Platforms (2nd Edition). Prentice Hall. ISBN: 013009224X.
- Stuart Barnes (ed) (2002). Knowledge Management Systems Theory and Practice. Thomson Learning.
- Stuart Russell, Peter Norvig (2003). Artificial Intelligence: A Modern Approach (2nd Edition). ISBN: 0-13-790395-2.
- Ian H. Witten, Alistair Moffat, Timothy C. Bell (1994). Managing Gigabytes. Van Nostrand Reinhold. ISBN: 0-442-01863-0.
- Christopher D. Manning, Hinrich Schuetze (1999). Foundations of Statistical Natural Language Processing. MIT Press. ISBN: 0262133601.
- Robert Dale, Hermann Moisl, Harold Somers (eds) (2000). Handbook of Natural Language Processing. Marcel Dekker. ISBN: 0824790006.
- Dan Sullivan (2001). Document Warehousing and Text Mining. Wiley. ISBN: 0-471-39959-0.
- David M. Levy (2001). Scrolling Forward: Making Sense of Documents in the Digital Age. Arcade Publishing. ISBN: 1559705531.
- Chris Collison, Geoff Parcell (2001). Learning to Fly: Practical Lessons from one of the World's Leading Knowledge Companies. Capstone. ISBN: 1-84112-124-X.
- Peter F. Drucker, David Garvin, Leonard Dorothy, Straus Susan, John Seely Brown (1998). Harvard Business Review on Knowledge Management. Harvard Business School Press. ISBN: 0875848818.
- Thomas H. Davenport, Laurence Prusak (2000). Working Knowledge. Harvard Business School Press. ISBN: 1578513014.
Grading scheme
- 60% Assignments / quizzes
- 40% Project
Project
In your final group project, you will take the case your group has constructed up to Chapter 5, and extend it so that your recommendation also includes detailed specifications incorporating one or more of the most appropriate technologies after Chapter 6. You should include sufficient detail that it is clear what implementation would require. This includes, but is not limited to, your methodology for knowledge engineering, capture, and acquisition; the types of engines needed; the types of features, attributes, rules, chaining, cases, indexes, metrics, mining; etc.
Note that the final project is due on or before Dec 14. Your group should turn in a written report, much as if you were a consulting group delivering the final report to the CEO. (In general, PowerPoint presentations are not adequate, unless they are truly exceptional, and read just as clearly and detailed as a normal report would.)