Codification Table

6: Knowledge Codification

Virach Sornlertlamvanich

1 Knowledge Management System, Virach Sornlertlamvanich ([email protected])

Review of Lecture 5

On-site Observation (Action Protocol)

Brainstorming (Conventional & Electronic)

Consensus Decision Making


Review of Lecture 5

Repertory Grid

Construct T1 T2 T3

1 Inexperience 3 3 1 2 Appearance 3 2 1

… … … … 5 Late 2 3 2

Delphi Method

Nominal Group Technique 3 Knowledge Management System, Virach Sornlertlamvanich ([email protected])

Review of Lecture 5

helper of

Beard

White horse

Birthday

At chimneys On roofs

Spain climbs

listens

lives in

brings gives not same as

has

has

lives in

rides

SAINT NICOLAS BLACK

PETER

Presents Santa Clause Blackboarding

Concept Map


This Week’s Objectives   What Does Knowledge Codification Involve?   Benefits of Knowledge Codification   Pre Knowledge Codification Questions   Tools and Procedures   The Role of Planning


Knowledge Codification   Modes of Knowledge Conversion   Codifying Knowledge   Codification Tools/Procedures

  Knowledge Maps   Decision Table   Decision Tree   Frames   Production Rules   Case-Based Reasoning   Knowledge-Based Agents

  Knowledge Developer's Skill Set   Knowledge Requirements   Skills Requirements


Knowledge Codification in the KM System Life Cycle

KNOWLEDGE CAPTURE (Creation)

KNOWLEDGE TRANSFER

KNOWLEDGE SHARING

TESTING AND DEPLOYMENT

KNOWLEDGE CODIFICATION

KNOWLEDGE BASE

DATABASES

Decision tables, Decision trees, frames maps, rules

Capture Tools Programs, books, articles, experts

Intelligence gathering

GOAL

Explicit Knowledge


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   It is making institutional knowledge visible, accessible, and

usable for decision making


Benefits of Knowledge Codification   Instruction/training—promoting training of junior 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


The knowledge developer should note the following points before initiating knowledge codification:

  Recorded knowledge is often difficult to access (because it is either fragmented or poorly organized).

  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.   Often the knowledge is found to be incomplete.


Pre-KC Questions   What organizational goals will the

codified knowledge serve?   Why is the knowledge useful?   How would one codify knowledge?


Modes of Knowledge Conversion   Conversion from tacit to tacit knowledge produces

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 (or fitting explicit knowledge to tacit knowledge.

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


Nonaka's Model of Knowledge Creation & Transformation (SECI Model)

Codifying Knowledge   An organization must focus on the following before

codification:   What organizational goals will the codified knowledge serve?   What knowledge exists in the organization that can address these

goals?   How useful is the existing knowledge for codification?   How would someone codify knowledge?

  Codifying tacit knowledge (in its entirety) in a knowledge base or repository is often difficult because it is usually developed and internalized in the minds of the human experts over a long period of time.


Some Codification Tools   Knowledge Map   Decision Table   Decision Tree   Frames   Production Rules   Case-based Reasoning   Knowledge-Based Agents


Knowledge Map   Visual representation of knowledge, not a repository   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 (Relationships among Departments)

www.nwlnk.com ©Copyright 2004


Knowledge Map A popular knowledge map used in human resources is a skills planner in which employees are matched to jobs. Steps to build the map:

 A structure of the knowledge requirements should be developed.  Knowledge required of specific jobs must be defined.  You should rate employee performance by knowledge competency.  You should link the knowledge map to some training program for career development and job advancement.


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 and currency of content


Decision Trees   Composed of nodes representing goals and links representing

decisions or outcomes   All nodes except the root node are instances of the primary

goal. (See next figure)   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 (A Decision Tree)

Discount Policy

Customer is library or individual

Less than 6 copies

6-19 copies

20-49 copies

50 or more copies

Discount is NIL

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


Decision Tables   More like a spreadsheet—divided into a list of conditions and

their respective values and a list of conclusions   Conditions are matched against conclusions (See next table)   It is another technique used for knowledge codification.   It consists of some conditions, rules, and actions.


Discount Policy (A Decision Table)

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

Action Stub Action Entry


Decision Table (an example)   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'.


Decision Table (an example)


Decision Table (an example)   It is also a knowledge codification technique.   A decision tree is usually a hierarchically arranged semantic

network.

A decision tree for the phonecard company discounting policy (as discussed above) is shown next.


Decision Table (an example)


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

  Key elements of frames:   Slot: A specific object being described/an attribute of an entity.   Facet: The value of an object/slot.

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


An Automobile Example Generic AUTOMOBILE Frame

Specialization: VEHICLE

Generalization: (STATION-WAGON, COUPE, SEDAN)

.

.

. Year: Range: (1940 – 1990) If-Changed: (ERROR: Value cannot be modified)

.

.

.

Generic COUPE Frame

Specialization: AUTOMOBILE

Generalization: (SMITH’S AUTOMOBILE, HANSON’S AUTOMOBILE)

Doors: 2

SMITH’S AUTOMOBILE Frame

Specialization: COUPE

.

.

. Year: 1990

Doors: ( )


Production Rules   Tacit knowledge codification in the form of premise-

action pairs   Rules are conditional statement that specify an action to

be taken if a certain condition is true   The form is IF… THEN, or IF…THEN…ELSE   Example: IF income is “average” and pay_history is “good” THEN recommendation is “approve loan”


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 rigorous initial planning of all possible variables


Generic CBR Process

User Partial Description of a

New Problem Specify Attributes of Problem

Match Attributes to Those in Case

Base

User

Case Base

Submits

Similar Cases


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   Making rules easier to review and understand


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 of the system


Knowledge-Based Agents   An intelligent agent is a program code which is capable of

performing autonomous action in a timely fashion.   They can exhibit goal directed behaviour 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 behaviour and deduce future behaviour for assisting the user.


Knowledge Developer's Skill Set   Knowledge Requirements

  Computing technology and operating systems.   Knowledge repositories and data mining.   Domain specific knowledge.   Cognitive psychology.

  Skills Requirements   Interpersonal Communication.   Ability to articulate the project's rationale.   Rapid Prototyping skills.   Attributes related to personality.   Job roles.


Documents

Codification Table