11-1 Chapter 11 Expert system architecture, representation of knowledge, Knowledge Acquisition, and Reasoning Turban, Aronson, and Liang Decision Support Systems and Intelligent Systems, Seventh Edition

Learning Objectives Describe the knowledge management cycle Describe the technologies that can be used in a knowledge management system Describe the Chief Knowledge Officer CKO and others involved in knowledge management Describe the role of knowledge management in organizational activities Describe the different ways of evaluating intellectual (intelligent) capital in an organization 11-2

Learning Objectives Describe how KMS are implemented Describe the roles of technology, people, and management in knowledge management Describe the benefits and drawbacks of knowledge management initiatives Describe how knowledge management can transform the way an organization functions. 11-3

Opening Vignette:“MITRE Knows What It Knows Through Knowledge Management” 11-4

Opening Vignette:“MITRE Knows What It Knows Through Knowledge Management” 11-5

Opening Vignette:“MITRE Knows What It Knows Through Knowledge Management” 11-6

Opening Vignette:“MITRE Knows What It Knows Through Knowledge Management” 11-7

Opening Vignette:“MITRE Knows What It Knows Through Knowledge Management” 11-8

Opening Vignette:“MITRE Knows What It Knows Through Knowledge Management” 11-9

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang Knowledge Engineers Professionals who elicit knowledge from experts –Empathetic, patient –Broad range of understanding, capabilities Integrate knowledge from various sources –Creates and edits code –Operates tools Build knowledge base –Validates information –Trains users

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang Knowledge Engineering Process of acquiring knowledge from experts and building knowledge base –Narrow perspective Knowledge acquisition, representation, validation, inference, maintenance –Broad perspective Process of developing and maintaining intelligent system

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang Knowledge Engineering Process Acquisition of knowledge –General knowledge or metaknowledge –From experts, books, documents, sensors, files Knowledge representation –Organized knowledge Knowledge validation and verification Inferences –Software designed to pass statistical sample data to generalizations Explanation and justification capabilities

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 11-13

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 11-14

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang Development of a Real-Time Knowledge-lead to success. Problems with fermentation process –Quality parameters difficult to control –Many different employees doing same task –High turnover Expert system used to capture knowledge –Expertise available 24 hours a day Knowledge engineers developed system by: –Knowledge elicitation Interviewing experts and creating knowledge bases –Knowledge fusion Fusing individual knowledge bases –Coding knowledge base –Testing and evaluation of system

Introduction to Knowledge Management Knowledge management concepts and definitions. –Knowledge management The active management of the expertise in an organization. It involves collecting, categorizing, and disseminating knowledge. –Intellectual capital The invaluable knowledge of an organization’s employees

Introduction to Knowledge Management 11-17

Introduction to Knowledge Management 11-18

Introduction to Knowledge Management Characteristics of knowledge Knowledge-based economy The economic shift from natural resources to intellectual assets 11-19

Introduction to Knowledge Management 11-20

Introduction to Knowledge Management Knowledge management systems (KMS) A system that facilitates knowledge management by ensuring knowledge flow from the person(s) who know to the person(s) who need to know throughout the organization; knowledge evolves and grows during the process 11-21

Knowledge Management Activities Knowledge management initiatives and activities –Most knowledge management initiatives have one of three aims: 1. To make knowledge visible 2. To develop a knowledge-intensive culture 3. To build a knowledge infrastructure 11-22

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang Elicitation Methods Manual –Based on interview –Track reasoning process –Observation Semiautomatic –Build base with minimal help from knowledge engineer –Allows execution of routine tasks with minimal expert input Automatic –Minimal input from both expert and knowledge engineer

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang Manual Methods Interviews –Structured Goal-oriented Walk through –Unstructured Complex domains Data unrelated and difficult to integrate –Semistructured

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang Manual Methods Process tracking –Track reasoning processes Protocol analysis –Document expert’s decision-making –Think aloud process Observation –Motor movements –Eye movements

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang Manual Methods Case analysis Critical incident User discussions Expert commentary Graphs and conceptual models Brainstorming Prototyping Clustering of elements Iterative performance review

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang Semiautomatic Methods Repertory grid analysis –Personal construct theory Organized, perceptual model of expert’s knowledge Expert identifies domain objects and their attributes Expert determines characteristics and opposites for each attribute Expert distinguishes between objects, creating a grid Expert transfer system –Computer program that elicits information from experts –Rapid prototyping –Used to determine sufficiency of available knowledge

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 11-28

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang Semiautomatic Methods, continued Computer based tools features: –Ability to add knowledge to base –Ability to assess, refine knowledge –Visual modeling for construction of domain –Creation of decision trees and rules –Ability to analyze information flows –Integration tools

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang Automatic Methods Data mining by computers Inductive learning from existing recognized cases Neural computing mimicking human brain Genetic algorithms using natural selection

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang Multiple Experts Scenarios –Experts contribute individually –Primary expert’s information reviewed by secondary experts –Small group decision –Panels for verification and validation Approaches –Consensus methods –Analytic approaches –Automation of process through software usage –Decomposition

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang Automated Knowledge Acquisition Induction –Activities Training set with known outcomes Creates rules for examples Assesses new cases –Advantages Limited application Builder can be expert –Saves time, money

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang Automated Knowledge Acquisition –Difficulties Rules may be difficult to understand Experts needed to select attributes Algorithm-based search process produces fewer questions Rule-based classification problems Allows few attributes Many examples needed Examples must be cleansed Limited to certainties Examples may be insufficient

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang Automated Knowledge Acquisition Interactive induction –Incrementally induced knowledge General models –Object Network –Based on interaction with expert interviews –Computer supported Induction tables IF-THEN-ELSE rules

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang Evaluation, Validation, Verification Dynamic activities –Evaluation Assess system’s overall value –Validation Compares system’s performance to expert’s Concordance and differences –Verification Building and implementing system correctly Can be automated

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 11-36

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang Production Rules IF-THEN Independent part, combined with other pieces, to produce better result Model of human behavior Examples –IF condition, THEN conclusion –Conclusion, IF condition –If condition, THEN conclusion1 (OR) ELSE conclusion2

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang Artificial Intelligence Rules Types –Knowledge rules Declares facts and relationships Stored in knowledge base –Inference Given facts, advises how to proceed Part of inference engines.

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang Artificial Intelligence Rules Advantages –Easy to understand, modify, maintain –Explanations are easy to get. –Rules are independent. –Modification and maintenance are relatively easy. –Uncertainty is easily combined with rules. Limitations –Huge numbers may be required –Designers may force knowledge into rule-based entities –Systems may have search limitations; difficulties in evaluation

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang Semantic Networks Graphical depictions Nodes and links Hierarchical relationships between concepts Reflects inheritance

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang Frames All knowledge about object Hierarchical structure allows for inheritance Allows for diagnosis of knowledge independence Object-oriented programming –Knowledge organized by characteristics and attributes Slots Subslots/facets –Parents are general attributes –Instantiated to children Often combined with production rules

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang Knowledge Relationship Representations Decision tables –Spreadsheet format –All possible attributes compared to conclusions Decision trees –Nodes and links –Knowledge diagramming Computational logic –Propositional True/false statement –Predicate logic Variable functions applied to components of statements

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 11-43

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang Reasoning Programs Inference Engine –Algorithms –Directs search of knowledge base Forward chaining –Data driven –Start with information, draw conclusions Backward chaining –Goal driven –Start with expectations, seek supporting evidence –Inference/goal tree Schematic view of inference process –AND/OR/NOT nodes –Answers why and how Rule interpreter

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang Explanation Facility Justifier –Makes system more understandable –Exposes shortcomings –Explains situations that the user did not anticipate –Satisfies user’s psychological and social needs –Clarifies underlying assumptions –Conducts sensitivity analysis Types –Why –How –Journalism based Who, what, where, when, why, how Why not

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang Generating Explanations Static explanation –Preinsertion of text Dynamic explanation –Reconstruction by rule evaluation Tracing records or line of reasoning Justification based on empirical associations Strategic use of metaknowledge

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang Uncertainty Widespread Important component Representation –Numeric scale 1 to 100 –Graphical presentation Bars, pie charts –Symbolic scales Very likely to very unlikely

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang Uncertainty Probability Ratio –Degree of confidence in conclusion –Chance of occurrence of event Bayes Theory –Subjective probability for propositions Imprecise Combines values Dempster-Shafer –Belief functions –Creates boundaries for assignments of probabilities Assumes statistical independence

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang Certainty Certainty factors –Belief in event based on evidence –Belief and disbelief independent and not combinable –Certainty factors may be combined into one rule –Rules may be combined

Approaches to Knowledge Management Process approach to knowledge management attempts to organize organizational knowledge through formalized controls, processes and technologies –Focuses on explicit knowledge and IT Practice approach focuses on building the social environments or communities of practice necessary to facilitate the sharing of tacit understanding –Focuses on tacit knowledge and socialization 11-50

Approaches to Knowledge Management Hybrid approaches to knowledge management –The practice approach is used so that a repository stores only explicit knowledge that is relatively easy to document –Tacit knowledge initially stored in the repository is contact information about experts and their areas of expertise –Increasing the amount of tacit knowledge over time eventually leads to the attainment of a true process approach 11-51

Knowledge Management - A Demand Led Business Activity Supply-driven vs. demand-driven KM 11-52

Approaches to Knowledge Management Best practices In an organization, the best methods for solving problems. These are often stored in the knowledge repository of a knowledge management system Knowledge repository is the actual storage location of knowledge in a knowledge management system. Similar in nature to a database, but generally text-oriented 11-53

Approaches to Knowledge Management A Comprehensive View to Knowledge Repository 11-54

Approaches to Knowledge Management Developing a knowledge repository –Knowledge repositories are developed using several different storage mechanisms in combination –The most important aspects and difficult issues are making the contribution of knowledge relatively easy for the contributor and determining a good method for cataloging the knowledge 11-55

Information Technology (IT) in Knowledge Management The KMS cycle –KMS usually follow a six-step cycle: 1. Create knowledge 2. Capture knowledge 3. Improve (refine) knowledge 4. Store knowledge 5. Manage knowledge 6. Distribute (disseminate) knowledge 11-56

Information Technology (IT) in Knowledge Management The Cyclic Model of Knowledge Management 11-57

Information Technology (IT) in Knowledge Management Components of KMS –KMS are developed using three sets of core technologies: 1. Communication 2. Collaboration 3. Storage and retrieval –Technologies that support KM Artificial intelligence Intelligent agents Knowledge discovery in databases Extensible Markup Language (XML) 11-58

Information Technology (IT) in Knowledge Management Artificial intelligence –AI methods used in KMS: Assist in and enhance searching knowledge Help for knowledge representation (e.g., ES) Help establish knowledge profiles of individuals and groups Help determine the relative importance of knowledge when it is contributed to and accessed from the knowledge repository 11-59

Information Technology (IT) in Knowledge Management AI methods used in KMS: –Scan , documents, and databases to perform knowledge discovery, determine meaningful relationships and rules –Identify patterns in data (usually through neural networks and other data mining techniques) –Forecast future results by using data/knowledge –Provide advice directly from knowledge by using neural networks or expert systems –Provide a natural language or voice command– driven user interface for a KMS 11-60

Information Technology (IT) in Knowledge Management Intelligent agents –Intelligent agents are software systems that learn how users work and provide assistance in their daily tasks –They are used to cause and identify knowledge See ibm.com, gentia.com for examples –Combined with enterprise knowledge portal to proactively disseminate knowledge 11-61

Information Technology (IT) in Knowledge Management Knowledge discovery in databases (KDD) A machine learning process that performs rule instruction, or a related procedure to establish (or create) knowledge from large databases –a.k.a. Data Mining (and/or Text Mining) 11-62

Information Technology (IT) in Knowledge Management Model marts Small, generally departmental repositories of knowledge created by employing knowledge- discovery techniques on past decision instances. Similar to data marts Model warehouses Large, generally enterprise-wide repositories of knowledge created by employing knowledge-discovery techniques. Similar to data warehouses 11-63

Information Technology (IT) in Knowledge Management Extensible Markup Language (XML) –XML enables standardized representations of data structures so that data can be processed appropriately by heterogeneous information systems without case-by-case programming or human intervention Web 2.0 –The evolution of the Web from statically disseminating information to collaboratively creating and sharing information 11-64

KM System Implementation Knowledge management products and vendors –Knowware Technology tools (software/hardware products) that support knowledge management –Software development companies / vendors Collaborative computing tools Knowledge servers Enterprise knowledge portals (EKP) An electronic doorway into a knowledge management system… 11-65

KM System Implementation Software development companies / vendors –Electronic document management (EDM) A method for processing documents electronically, including capture, storage, retrieval, manipulation, and presentation –Content management systems (CMS) An electronic document management system that produces dynamic versions of documents, and automatically maintains the current set for use at the enterprise level 11-66

KM System Implementation Software development tools –Knowledge harvesting tools –Search engines –Knowledge management suites –Knowledge management consulting firms –Knowledge management ASPs 11-67

KMS Implementation Integration of KMS with other business information systems –With DSS/BI Systems –With AI –With databases and information systems –With CRM systems –With SCM systems –With corporate intranets and extranets 11-68

Roles of People in Knowledge Management Chief knowledge officer (CKO) The person in charge of a knowledge management effort in an organization –Sets KM strategic priorities –Establishes a repository of best practices –Gains a commitment from senior executives –Teaches information seekers how to better elicit it –Creates a process for managing intellectual assets –Obtain customer satisfaction information –Globalizes knowledge management 11-69

Roles of People in Knowledge Management Skills required of a CKO include: –Interpersonal communication skills –Leadership skills –Business acumen –Strategic thinking –Collaboration skills –The ability to institute effective educational programs –An understanding of IT and its role in advancing knowledge management 11-70

Roles of People in Knowledge Management The CEO, other chief officers, and managers –The CEO is responsible for championing a knowledge management effort –The officers make available the resources needed to get the job done CFO ensures that the financial resources are available COO ensures that people begin to embed knowledge management practices into their daily work processes CIO ensures IT resources are available –Managers also support the KM efforts by providing access to sources of knowledge 11-71

Roles of People in Knowledge Management Community of practice (CoP) A group of people in an organization with a common professional interest, often self-organized for managing knowledge in a knowledge management system –See Application Case 11.7 as an example of how Xerox successfully improved practices and cost savings through CoP 11-72

Roles of People in Knowledge Management KMS developers –The team members who actually develop the system –Internal + External KMS staff –Enterprise-wide KMS require a full-time staff to catalog and manage the knowledge 11-73

Ensuring the Success of Knowledge Management Efforts Success stories of knowledge management –Implementing a good KM strategy can: Reduce… –loss of intellectual capital –costs by decreasing the number of times the company must repeatedly solve the same problem –redundancy of knowledge-based activities Increase… –productivity –employee satisfaction 11-74

Ensuring the Success of Knowledge Management Efforts MAKE: Most Admired Knowledge Enterprises “Annually identifying the best practitioners of KM” –Criteria (performance dimensions): 1. Creating a knowledge-driven corporate culture 2. Developing knowledge workers through leadership 3. Fostering innovation 4. Maximizing enterprise intellectual capital 5. Creating an environment for collaborative knowledge sharing 6. Facilitating organizational learning 7. Delivering value based on stakeholder knowledge 8. Transforming enterprise knowledge into stakeholders’ value 11-75

Ensuring the Success of Knowledge Management Efforts MAKE: Most Admired Knowledge Enterprises “Annually identifying the best practitioners of KM” –2008 Winners: 1. McKinsey & Company 2. Google 3. Royal Dutch Shell 4. Toyota 5. Wikipedia 6. Honda 7. Apple 8. Fluor 9. Microsoft 10. PricewaterhouseCoopers 11. Ernst & Young 12. IBM 13. Schlumberger 14. Samsung Group 15. BP 16. Unilever 17. Accenture 18. … 11-76

Ensuring the Success of Knowledge Management Efforts Useful applications of KMS –Finding experts electronically and using expert location systems Expert location systems (know-who) Interactive computerized systems that help employees find and connect with colleagues who have expertise required for specific problems—whether they are across the county or across the room—in order to solve specific, critical business problems in seconds 11-77

Ensuring the Success of Knowledge Management Efforts Knowledge management valuation –Financial metrics for knowledge management valuation Focus knowledge management projects on specific business problems that can be easily quantified When the problems are solved, the value and benefits of the system become apparent 11-78

Ensuring the Success of Knowledge Management Efforts Knowledge management valuation –Nonfinancial metrics for knowledge management valuation—new ways to view capital when evaluating intangibles: Customer goodwill External relationship capital Structural capital Human capital Social capital Environmental capital 11-79

Ensuring the Success of Knowledge Management Efforts Causes of knowledge management failure –The effort mainly relies on technology and does not address whether the proposed system will meet the needs and objectives of the organization and its individuals –Lack of emphasis on human aspects –Lack of commitment –Failure to provide reasonable incentive for people to use the system… 11-80

Ensuring the Success of Knowledge Management Efforts Factors that lead to knowledge management success –A link to a firm’s economic value, to demonstrate financial viability and maintain executive sponsorship –A technical and organizational infrastructure on which to build –A standard, flexible knowledge structure to match the way the organization performs work and uses knowledge 11-81

Ensuring the Success of Knowledge Management Efforts Factors that lead to knowledge management success –A knowledge-friendly culture that leads directly to user support –A clear purpose and language, to encourage users to buy into the system –A change in motivational practices, to create a culture of sharing –Multiple channels for knowledge transfer 11-82

Ensuring the Success of Knowledge Management Efforts Factors that lead to knowledge management success –A significant process orientation and valuation to make a knowledge management effort worthwhile –Nontrivial motivational methods to encourage users to contribute and use knowledge –Senior management support 11-83

Last words on KM Knowledge is an intellectual asset IT is “just” an important enabler Proper management of knowledge is a necessary ingredient for success Key issues: –Organizational culture –Executive sponsorship –Measurement of success 11-84

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