Lecture 5 Knowledge Acquisition 2

8/13/2019 Lecture 5 Knowledge Acquisition 2

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Recap

Stages of Knowledge Acquisition Identification:

Conceptualization:

Formalization:

Implementation:

Testing:

Sources of Knowledge

Written sources

Experts Observation


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Outline

Knowledge acquisition methods TOP-Down Methods

Bottom-Up Methods

Formal Techniques Repertory Grids

Card Sort


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Knowledge Acquisition

Knowledge acquisition methods Top down (Deductive)

Starting from the general and overall concepts andgradually leading the expert to elicit details of a

topic. Bottom up (Inductive)

The KE focuses the experts attention on specific case. Thishelps the expert abstract the decision for resolving a specificcase to a more generalized rule or concept.


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Categories of KA methods

KA methods

Bottom upmethods

Top downmethods

Example basedmethods

Protocolanalysis

Observation

Groupingexamples

Walkthrough

examples

Quantitativeanalysis ofexamples

Statisticalmethods

Inductivemethods

Questioningmethods

Objectorientedmethods

QuantitativeMethods

Inventivemethods

Unstructured

Interviews

Structured

interviews

Questionnaire

Methods formeasuring

relationships

Methods formeasuringuncertainty


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Knowledge Acquisition Methods

Object Oriented Methods Knowledge engineer focuses the interview sessions on

discovering the objects within the domain.

This is by asking the expert to group the actualobjects in the field or domain in order to form a class

of objects that have a common set of attributes. i.e. KE asks expert to identify some classes of objects

that have distinguishing attributes.

Example For a loan applicant, one of the major objects in the

mortgage loans is the applicant.

The expert forms the class of applicants object and identifiesthe relevant attributes of this class e.g. Name, address, etc


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Object Oriented Methods

Further knowledge elicitation leads to thesubclass of individual applicantscommercialapplicants, etc

As an aid to discovering the classes of objects

and their attributes and subclasses some of theobject oriented methods use network graphs.

Each node represents an object with the attachedattributes.

Arcs of the network show the type of relationshipsamong the objects


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Applicant

Property Loan

Attribute

Address

ValueLocation

Amount

Duration

Mortgage is

Has aHas a

Has an

Has an

Has an

Has a


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Another way to depict the structure of objects is thehierarchy of the class of objects.

The top of the hierarchy shows he most general class ofobjects.

The lower levels contain increasingly more specificsubclasses of objects which may inherit some or allattributes of their immediate parent object.


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ApplicantClass

IndividualSubclass

CommercialSubclass

MarriedSubclass

SingleApplicantSubclass

Age

Name

Address

Type

Manager

Income

Spouses name

Family income

Job


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Quantitative Methods

Algorithmic / mathematical / statistical methods

Relationshipstrength

Uncertaintyhow sure

Are methods developed in cognitive science and decision analysis for

eliciting the degree of a decision makers preferences and utilities and

in grouping various objects and attributes.

These methods determine

The extent of relationships among objects (or concepts)

The degree of uncertainty about the domain knowledge.

The expert is asked to compare and express the extent of the

objects relationships on a numerical scale. Then a mathematicalalgorithm is used to compute and rank the degree of relationshipsamong the objects.


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Quantitative Methods

Elicitation of uncertainty is one of the areas ofknowledge-based systems that has not been fullydeveloped.

The elicitation of uncertainty depends on the selectedmethod of uncertainty representation


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Inventive Methods

Expert is allowed a more active role in the elicitationprocess. Roles include

Expert as a teacher

Expert responsible for teaching and transferring knowledge to theKE. Expert has responsibility of preparation and organisation of

the elicitation process. This method is efficient at the early stages of elicitation

Expert as a partner in a systematic innovation

This revolves around the expert and KE identifying pieces ofknowledge that are in contradiction and to discover solutionmethods for removing the contradiction

Expert as a Knowledge Engineer

Used in cases where the expert may have both technical interestin the system and the needed training in knowledge engineering.


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2. Bottom Up Methods

The KE focuses the experts attention on specific case. This helps

the expert abstract the decision for resolving a specific case to amore generalized rule or concept.

Example methods include

Example based methods

Protocol analysis

Observation of the experts decision making process


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Bottom Up Methods


This method constitutes the foundation of case based learningand learning by analogy.

The expert and KE work on a number of representative cases orexamples in one of the following ways.

Grouping examples

KE asks the expert to list samples based on their similarities anddifferences. KE then asks the expert to identify the common anddifferentiating attributes of the examples. This helps to determinecategories of examples and the development of general rules fore eachcategory.

Walk through methods

the KE selects a number of cases previously decided by the expert andasks the expert to walk through the decision process. The KE and expertrecognize the contributing factors and attributes and their role in thedecision


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Quantitative analysis of examples Statistical methodsThe examples must be from a random sample of

cases decided by the experts. The data on the examples is fed into astatistical technique such as regression analysis in order to discover theexperts decision criteria

Inductive methodsthe example set contains a representative set of allpossible cases the expert has encountered. The examples are fed into

an inductive method which produces a decision tree or a set of decisionrules.

Quantitative techniques are tools for helping the expert discoverthe relations among various attributes of the decision cases.

The outcomes should not be used without consultation with the

expert. Quantitative methods may not be able to discover all the

qualitative aspects of the decision process


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Protocol Analysis

Expert is asked to think aloud and verbalize their thought

process or thinking process while solving a set of actualproblems and making decisions.

The KE recodes the process and later analyzes the largevolume of information produced from this method to

discover the general rules the expert uses in solvingproblems

Useful for non-procedural type of problem solving wherethe expert applies a great deal of mental creative and

intellectual effort to arrive at a decision in each case.


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Observation

Involves observing the expert while solving a problem

Useful when solution to problem is procedural and takesplace in a sequence of steps through time.

The absence of bias and intrusion inherent in the KEquestions makes this approach more useful.


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Formal Techniques

Most of the formal techniques currently in use forknowledge acquisition have their origin in Kellystheory of the psychology of personal constructs.

The basic idea behind it is that people perceive

the world in terms of their own constructs A construct is a specialized form of

conceptualization

Two techniques Repertory Grids

Card Sort


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Summary

Knowledge acquisition methods

TOP-Down Methods

Bottom-Up Methods

Formal Techniques

Repertory Grids Card Sort

Next lecture

Repertory Grids

Card Sort


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Recap

Knowledge acquisition methods TOP-Down Methods

Bottom-Up Methods

Formal Techniques Repertory Grids

Card Sort


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Outline

Formal Techniques of KnowledgeAcquisition

Repertory Grids

Card Sort

Inference and Knowledge Processing

Reasoning

Inference

Inference Methods


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Formal Techniques

Most of the formal techniques currently ins usefor knowledge acquisition have their origin inKellys theory of the psychology of personalconstructs.

The basic idea behind it is that people perceivethe world in terms of their own constructs

A construct is a specialized form ofconceptualization

Two techniques

Repertory Grids

Card Sort


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Repertory Grids

Uses a two-dimensional matrix to display a

picture of the relationships between variousobjects and concepts from the problem domain.

Along one axis are placed a list of Elements

Objects, people, or situations familiar to the individual. The other axis consists of a set of elicited

ConstructsProperty under investigation

C1 C2 C3 C4

E1 xxxxx

E2 xxxxx

E3 xxxxx

E4 xxxxx

LightHeavy

LargeSmall

ShortTall


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Repertory Grid: Constructs

Generally elicited by means of a repertory test.

This consists of presenting three randomlychosen elements to the expert and asking inwhat way two of them are similar. The response forms one pole of a construct.

The opposite of that pole characterizes the thirdelement.

This would then form the other pole of the construct.

This process is repeated for different groups ofthree elements until the options are exhausted.

Each element is rated on each of the constructs. This is on a scale with an odd number of points.


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Example on Repertory Grids

Example

Selection of a computer language for a certainsituation

Solution

Identification of the important objects in the domainof expertise Objects: The computer languages, LISP, C++, COBOL,

PROLOG

Identification of the important attributes that are

considered in making decisions in the domain e.g. Availability of commercial packages

Ease of programming

Training time

Orientation


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The KE picks any three objects and asks the DE to

distinguish attributes and traits that distinguish anytwo from the third.

E.g. if the set includes LISP, PROLOG, and COBOL, the expertmay point to orientationi.e. LISP and PROLOG are symbolicwhile COBOL is numeric.

This is as shown in the table

For each attribute, the DE is asked to establish a bipolar scale(13 or 15).

Attribute Symbol Trait Opposite

Orientation

Ease of programming

Training timeAvailability

C1

C2

C3C4

Symbolic

High

LowWidely available

Numeric

Low

HighNot available


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This step continues for several triplets of objects.

The answers are recorded in a grid as shown belowwith the numbers in the grid designating pointsassigned to each attribute for each object. Once the grid is completed, the expert may change the

ratings in the boxes.

From the table, in a simplistic sense, if a numeric orientation is veryimportant, then COBOL will be the recommended language.

Choice of Programming Language

C1 C2 C3 C4

E1 LISP 3 3 1 1

E2 Prolog 3 2 2 2

E3 C++ 2 2 2 3E4 COBO

L1 2 1 3


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Card Sort or Concept Sorting

When the major concepts have been isolated

from the interview transcript, they are eachwritten on a separate card, and given to the DEin a totally random order. To understand the associations between elements,

then the DE could be asked to group the cards intopiles, according to a criterion of his or her choice.

The process can then be repeated until the expert isunable to provide any more dimensions.

Technique can also be used to obtain a binary tree. The DE sorts the cards into two piles and then subdivides

each pile in turn until no other divisions are possible.

The process can be done in reverse, whereby the expert hasto form as many piles as possible and then determine reasonsas to why piles should be consolidated.

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Lecture 5 Knowledge Acquisition 2

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