11-1 Chapter 11 Expert system architecture, representation of knowledge, Knowledge Acquisition, and...

Chapter 11Expert system architecture, representation

ofknowledge, 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

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.

Opening Vignette:“MITRE Knows What It Knows Through Knowledge

Management”

ENABLING TECHNOLOGIES FOR KNOWLEDGE MANAGEMENT

Expert Systems

DataMining

SearchEngine

Web 2.0

Databases

Portals

Internet

Collaboration

Webtechnologies

Intranet

Extranet

Knowledgerepresentation

Measurements

Machine Learning

Artificial Intelligence

Create

Identify

Act Apply

Modify

CULTURE PROCESS PRACTICE

KM LIFE-CYCLE

Communication

INFLUENCING FACTORS

feedback

Management”

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

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

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

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.

ProcessedRelevant and

Actionable

Relevant and actionable processed-data

Database PHASE 5

DEPT 4

DEPT 3

DEPT 2

DEPT 1

PHASE 4PHASE 3PHASE 2PHASE 1

DEPLOYMENT CHART

1 2 3 4 5

Information

Knowledge

• Characteristics of knowledge

• Knowledge-based economy

The economic shift from natural resources to intellectual assets 11-19

• 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

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

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

Manual Methods

• Interviews– Structured

• Goal-oriented• Walk through

– Unstructured• Complex domains• Data unrelated and difficult to integrate

– Semistructured

Manual Methods

• Process tracking– Track reasoning processes

• Protocol analysis– Document expert’s decision-making – Think aloud process

• Observation– Motor movements– Eye movements

Manual Methods

• Case analysis• Critical incident• User discussions• Expert commentary• Graphs and conceptual models• Brainstorming• Prototyping• Clustering of elements• Iterative performance review

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

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

Automatic Methods

• Data mining by computers

• Inductive learning from existing recognized cases

• Neural computing mimicking human brain

• Genetic algorithms using natural selection

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

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

– 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

• Interactive induction– Incrementally induced knowledge

• General models – Object Network

– Based on interaction with expert• interviews

– Computer supported• Induction tables• IF-THEN-ELSE rules

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

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

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.

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

Semantic Networks

• Graphical depictions

• Nodes and links • Hierarchical

relationships between concepts

• Reflects inheritance

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

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

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

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

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

Uncertainty

• Widespread• Important component• Representation

– Numeric scale• 1 to 100

– Graphical presentation• Bars, pie charts

– Symbolic scales• Very likely to very unlikely

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

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 toKnowledge 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

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

Knowledge Management - A Demand Led Business

Activity• Supply-driven vs. demand-driven KM

Technology approach

Knowledge

Information Action

Results

Supply-driven: DIKAR

Demand-driven: RAKIDBusiness-value approach

summarize

contextulize utilize

obtain

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

A Comprehensive View to Knowledge RepositoryKNOWLEDGE MANAGEMENT PLATFORM (KMP)

Human Experts

KNOWLEDGE PORTAL (Web-based End User Interface)

Intelligent Broker

KNOWLEDGE REPOSITORY (Knowledge / Information / Data Nuggets)

Web Crawler Data/Text Mining ToolsManualEntries

DIVERSE INFORMATION / DATA SOURCES(Weather / Medical Info / Finance / Agriculture / Industrial)

Ad hocSearch

E UTIL

IZATIO

• 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

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

Capture Knowledge

Refine Knowledge

Store Knowledge

Manage Knowledge

Disseminate Knowledge

Create Knowledge

Management The Cyclic Model of Knowledge ManagementThe Cyclic Model of Knowledge Management

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)

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

Management • AI methods used in KMS:

– Scan e-mail, 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

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

Management • Knowledge discovery in databases

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)

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

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

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…

• 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

• Software development tools– Knowledge harvesting tools – Search engines – Knowledge management suites – Knowledge management consulting firms – Knowledge management ASPs

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

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

• 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

• 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

• 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

• 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

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

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

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. …

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

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

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

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…

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

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

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

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

• END

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