Knowledge Management Systems: Development and Applications Part I: Overview and Related Fields Hsinchun Chen, Ph.D. McClelland Professor, Director, Artificial Intelligence Lab The University of Arizona Founder, Knowledge Computing Corporation Acknowledgement: NSF DLI1, DLI2, NSDL, DG, ITR, IDM, CSS, NIH/NLM, NCI, NIJ, CIA, DHS, NCSA, HP, SAP 美國亞歷桑那大學, 陳炘鈞 博士
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Knowledge Management Systems:
Development and Applications
Part I: Overview and Related Fields
Hsinchun Chen, Ph.D.
McClelland Professor,
Director, Artificial Intelligence
Lab
The University of Arizona
Founder, Knowledge
Computing Corporation
Acknowledgement: NSF DLI1, DLI2,
NSDL, DG, ITR, IDM, CSS, NIH/NLM,
NCI, NIJ, CIA, DHS, NCSA, HP, SAP
美國亞歷桑那大學, 陳炘鈞 博士
• My Background: ( A Mixed Bag!) • BS NCTU Management Science, 1981
• MBA SUNY Buffalo Finance, MS, MIS
• Ph.D. NYU Information System, Minor: CS, 1989
• Dissertation: “An AI Approach to the Design Of Online Information Retrieval Systems” (GEAC Online Cataloging System)
• Assistant/Associate/Full/Chair Professor, University of Arizona, MIS Department
• Scientific Counselor, National Library of Medicine USA), National Library of China, Academia Sinica
• My Background: (A Mixed Bag!) • Founder/Director, Artificial Intelligent Lab, 1990
• Industry Consulting: HP, IBM, AT&T, SGI, Microsoft, SAP
• Founder, Knowledge Computing Corporation, 2000
Knowledge Management:
Overview
Knowledge Management Overview
• What is Knowledge Management
• Data, Information, and Knowledge
• Why Knowledge Management?
• Knowledge Management Processes
Unit of Analysis • Data: 1980s
– Factual
– Structured, numeric Oracle, Sybase, DB2
• Information: 1990s
– Factual Yahoo!, Excalibur,
– Unstructured, textual Verity, Documentum
• Knowledge: 2000s
– Inferential, sensemaking, decision making
– Multimedia ???
• According to Alter (1996), Tobin (1996), and Beckman (1999):
– Data: Facts, images, or sounds (+interpretation+meaning =)
– Information: Formatted, filtered, and summarized data (+action+application =)
– Knowledge: Instincts, ideas, rules, and procedures that guide actions and decisions
Data, Information and Knowledge:
Application and Societal Relevance :
• Ontologies, hierarchies, and subject headings
• Knowledge management systems and
practices: knowledge maps
• Digital libraries, search engines, web mining,
text mining, data mining, CRM, eCommerce
• Semantic web, multilingual web, multimedia
web, and wireless web
1965
1975
1985
1995
2000
2010
ARPANET Internet “SemanticWeb”
Company IBM ??? Microsoft/Netscape
The Third Wave of Net Evolution
Function Server Access Knowledge Access Info Access
Unit Server Concepts File/Homepage
Example Email Concept Protocols WWW: “World Wide Wait”
Knowledge Management
Definition
“The system and managerial approach to
collecting, processing, and organizing
enterprise-specific knowledge assets for
business functions and decision making.”
Knowledge Management Challenges
• “… making high-value corporate information and knowledge easily available to support decision making at the lowest, broadest possible levels …”
– Personnel Turn-over
– Organizational Resistance
– Manual Top-down Knowledge Creation
– Information Overload
Knowledge Management Landscape
• Research Community
– NSF / DARPA / NASA, Digital Library Initiative I &
II, NSDL ($120M)
– NSF, Digital Government Initiative ($60M)
– NSF, Knowledge Networking Initiative ($50M)
– NSF, Information Technology Research ($300M)
• Business Community
– Intellectual Capital, Corporate Memory,
– Knowledge Chain, Competitive Intelligence
• Enabling Technologies:
– Information Retrieval (Excalibur, Verity, Oracle Context)
– Electronic Document Management (Documentum, PC
DOCS)
– Internet/Intranet (Yahoo!, Google)
– Groupware (Lotus Notes, MS Exchange)
• Consulting and System Integration:
– Best practices, human resources, organizational
development, performance metrics, methodology,
framework, ontology (Delphi, E&Y, Arthur Andersen, AMS,
KPMG)
Knowledge Management
Foundations
Knowledge Management Perspectives:
• Process perspective (management and behavior): consulting practices, methodology, best practices, e-learning, culture/reward, existing IT new information, old IT, new but manual process
• Information perspective (information and library sciences): content management, manual ontologies new information, manual process
• Knowledge Computing perspective (text mining, artificial intelligence): automated knowledge extraction, thesauri, knowledge maps new IT, new knowledge, automated process
KMS
Analysis
Consulting
Methodology
Databases
ePortals
Email
Notes
Search
Engine
User
Modeling
Content
Mgmt
Ontology
Content/Info
Structure
Data/Text
Mining Web Mining
Cultural
Learning /
Education
Best
Practices
Human
Resources
Tech
Foundation
Infrastructure
KM Perspectives
KM, Emergence of a Discipline
(Ponzi, 2004):
• Influences from three disciplines: Management and Policy (40%), Computer Science (30%), Information/Library Science (20%)
• Continuous, steady growth since 1990: academic publications and industry articles; not a fad (unlike BPR, TQM)
• Seminal books and articles in Knowledge Management (e.g., Drucker, Davenport, Nonaka): the 50 most-cited KM articles
KM Thoughts and Thinkers:
• Future organizations are information-based organizations of knowledge workers; Specialization, cross-discipline task teams, disappearance of middle managers (Drucker, “The Coming of the New Organization”)
• The Japanese Management Style: Tacit knowledge, redundancy, slogans, metaphors; the “Ba”; the SECI Model – Socialization, Externalization, Combination, and Internalization (Nonaka, “The Knowledge-Creating Company)
KM Thoughts and Thinkers: (cont’d)
• Knowledge generation (acquisition, dedicated resources, fusion, adaptation, knowledge networking); Knowledge codification (mapping and modeling knowledge); Knowledge transfer; Technologies for KM; Learning from experiments (Davenport, “Working Knowledge”)
• Deep Smart: Seeing the big picture and knowing the skills; learning from experience (Leonard, “Deep Smart”)
KM Thoughts and Thinkers: (cont’d)
• Teaching smart people how to learn; Defensive reasoning and doom loop; Learning how to reason productively (Argyris, “Teaching Smart People How to Learn”)
• Technology gets in the way; Research on work practices; Harvesting local innovation and innovating with customer; PARC anthropologists (John Seely Brown, “Research that Reinvents the Corporation”)