SEN-455Knowledge Based Management System

SEN-455

Knowledge Based

Management System

Chapter Objectives

•

What Does Knowledge Codification Involve?

•

Benefits of Knowledge Codification

•

Pre Knowledge Codification Questions

•

Tools and Procedures

Knowledge Codification in the

KM

System Life Cycle

What Does Knowledge Codification

Involve?

•

Converting “

tacit knowledge

” into “

explicit usable

form

”

•

Converting “

undocumented

” information into

“

documented

” information

•

Representing

and

organizing

knowledge before it

is accessed

•

Tacit knowledge (e.g., human expertise) is

identified

and

expressed

through a form that is

•

Explicit knowledge is

organized

,

categorized

,

indexed

and

accessed

.

•

It is making institutional knowledge

visible

,

accessible

, and

usable

for

decision making

•

The organizing often includes decision trees,

decision tables etc.

•

Codification must be done in a form/structure

which will eventually build the

knowledge base

.

What Does Knowledge Codification

Involve?

Benefits of Knowledge Codification

personnel based on captured knowledge of senior employees

• Prediction—inferring the likely outcome of a given situation and flashing a proper warning or suggestion

for corrective action

• Diagnosis—addressing identifiable symptoms of specific causal factors

• Planning/scheduling—mapping out an entire course of action before any steps are taken

Points before initiating knowledge

codification:

• Recorded knowledge is often difficult to access

• Diffusion of new knowledge is too slow.

• Knowledge is nor shared, but hoarded (this can involve political implications).

• Often knowledge is not found in the proper form. • Often knowledge is not available at the correct time

when it is needed.

• Often knowledge is not present in the proper location where it should be present.

Modes of Knowledge Conversion

socialization where knowledge developer looks for experience in case of knowledge capture.

• Conversion from tacit to explicit knowledge involves

externalizing, explaining or clarifying tacit knowledge via analogies, models, or metaphors.

• Conversion from explicit to tacit knowledge involves

internalizing

• Conversion from explicit to explicit knowledge involves

combining, categorizing, reorganizing or sorting different bodies of explicit knowledge to lead to new knowledge.

Codifying Knowledge

•

What organizational

goals

will the codified knowledge

serve?

•

Why is the knowledge

useful

?

•

How would one codify

Some Codification Tools

•

Knowledge Map

•

Decision Table

•

Decision Tree

•

Frames

•

Production Rules

•

Case-based Reasoning

•

Knowledge-Based Agents

Knowledge Map

• It is a visual representation of knowledge, not a repository • Knowledge maps originated from the belief that people

act on things that they understand and accept.

• Identify strengths to exploit and missing knowledge gaps to fill

• Can be applied in Knowledge Capture

• A straightforward directory that points people to where they can find certain expertise

• Capture both explicit and tacit knowledge in documents and in experts’ heads

Knowledge Map

• Knowledge mapping is very useful when it is required to visualize and explore complex systems.

• _{Examples of complex systems are ecosystems, the} internet, telecommunications systems, and

customer-supplier chains in the stock market. • Knowledge Mapping is a multi-step process.

• _{Key can be extracted from database or literature and} placed in tabular form as lists of facts.

• These tabled relationships can then be connected in networks to form the required knowledge maps.

Knowledge Map (Relationships among

Departments)

The Building Cycle

• Once where knowledge resides is known, simply point to it and add instructions on how to get there

• An intranet is a common medium for publishing knowledge maps

• Main criteria: clarity of

purpose, ease of use, accuracy of content

Decision Tables

•

More like a

spreadsheet

—divided into a list of

conditions

and their respective values and a

list of

conclusions

•

It consists of some conditions, rules, and

actions.

Decision Tables (Example I)

•

A phonecard company sends out monthly invoices

to permanent customers and gives them discount

if payments are made within two weeks. Their

discounting policy is as follows:

•

``If the amount of the order of phonecards is

greater than $35, subtract 5% of the order; if the

amount is greater than or equal to $20 and less

than or equal to $35, subtract a 4% discount; if the

amount is less than $20, do not apply any

discount.''

• We shall develop a decision table for their discounting decisions, where the condition alternatives are `Yes' and `No'.

Discount Policy (Example II)

Condition Stub Condition Entry 1 2 3 4 5 6

Customer is bookstore Order size > 6 copies

Customer is librarian/individual

IF Order size 50 copies or more (condition) Order size 20-49 copies

Order size 6-19 copies

Y Y N N N N Y N N N N N Y Y Y Y Y N N N Y N N Y N Allow 25% discount Allow 15% discount Allow 10% discount

THEN Allow 5% discount (action) Allow no discount X X X X X X

Decision Trees

• A decision tree is usually a hierarchically arranged semantic network.

• Composed of nodes representing goals and links representing decisions or outcomes

• All nodes except the root node are instances of the primary goal.

• Often a step before actual codification

• Ability to verify logic graphically in problems

involving complex situations that result in a limited number of actions

Discount Policy Customer is library or individual Less than 6 6-19 copies 20-49 copies 50 or more copies Discount is 5% Discount is 10% Discount is 15% Customer is bookstore Less than 6 copies Discount is NIL 6 or more copies Discount is 25% Discount ? Discount ? Discount ? Discount ? Discount ? Discount ? Order size ? Order size ? Bookstore Not a bookstore

Frames

• Represent knowledge about a particular idea in a

data structure

• Handle a combination of declarative and

operational knowledge, which make it easier to understand the problem domain

• Have a slot (a specific object or an attribute of an

entity) and a facet (the value of an object or a slot)

• When all the slots are filled with values, the frame is considered instantiated

Generic AUTOMOBILE Frame Specialization: VEHICLE Generalization: (STATION-WAGON, COUPE, SEDAN) . . . Year: Range: (1940 – 1990) If-Changed: (ERROR:

Value cannot be modified)

Specialization: AUTOMOBILE Generalization: (SMITH’S AUTOMOBILE, HANSON’S AUTOMOBILE) Doors: 2 SMITH’S AUTOMOBILE Frame Specialization: COUPE . . . Year: 1990

An Automobile Example

Production Rules

• Tacit knowledge codification in the form of premise-action pairs

• _{The premise is a Boolean expression that should} evaluate to be true for the rule to be applied. • Rules are conditional statement that specify an

action to be taken if a certain condition is true • _{The action part of the rule is separated from the}

premise by the keyword THEN.

Production Rules

• In case of knowledge-based systems, rules are based on heuristics or experimental reasoning.

• Rules can incorporate certain levels of uncertainty. • The action clause consists of a statement or a series

of statements separated by AND's or comma's and is executed if the premise is true.

•

Example:

IF

Production Rules (Role of inferencing)

•

Inferencing implies the process of deriving a

conclusion based on statements that only

imply that conclusion.

•

An inference engine is a program that manages

the inferencing strategies.

•

Reasoning is the process of applying

knowledge to arrive at the conclusion.

– Reasoning depends on premise as well as on general knowledge.

Case-Based Reasoning (CBR)

• CBR is reasoning from relevant past cases in a

manner similar to humans’ use of past experiences

to arrive at conclusions

• Goal is to bring up the most similar historical cases

that match the current case

• More time savings than rule-based systems • _{Requires strong initial planning of all possible}

Generic CBR Process

Role of Planning (Earlier Steps)

•

Breaking

the KM system into

modules

•

Looking at

partial

solutions

•

Linking partial solutions via rules and

procedures to

arrive

at the

final solution

Role of Planning (Latter Steps)

•

Deciding on the

programming language

•

Selecting the right

software package

•

Developing

user interface

and

consultation

facilities

•

Arranging for the

verification

and

validation

Knowledge-Based Agents

•

An

intelligent age

nt is a program code which is

capable of performing

autonomous

action in a

timely fashion.

•

They can

exhibit

goal directed

behavior

by taking

initiative.

•

They can be programmed to interact with other

agents or humans by using some

agent

communication language.

•

In terms of knowledge-based systems, an agent

can be programmed

to learn from the user

behavior

and

deduce future behavior

for assisting

the user.

Knowledge Developer's Skill Set

Knowledge Requirements:

•

Computing technology

and

operating

systems

.

•

Knowledge repositories

and

data mining

.

•

Domain specific knowledge

.

Knowledge Developer's Skill Set

Skills Requirements:

•

Interpersonal

Communication.

•

Ability to

articulate

the project's rationale.

•

Rapid Prototyping skills

.

•

Attributes related to

personality

.

– Physical (Physique, Gender, Age, health)

– Psychological (Origin, social status, social values, attitude, occupation, group membership)