Transcript Slide 1

Knowledge Engineering
and Acquisition
Chapter 6 Supplement
Knowledge Acquisition
Knowledge acquisition is the
extraction of knowledge from sources
of expertise and its transfer to the
knowledge base and sometimes to the
inference engine
What are some of the Difficulties
in Knowledge Acquisition
Expressing the knowledge:

Human knowledge exists in a compiled format. A human doesn’t
remember all the intermediate steps used to in transferring and
processing knowledge – representation mismatch
Number of participants
Structuring the knowledge:

We must elicit not only the knowledge but also its structure; rules
“Knowers” lack time and unwilling to help
Testing and refining knowledge is hard
Collect knowledge from one source but relevant knowledge is
dispersed
Important knowledge may be mixed up with irrelevant
information
Incomplete knowledge (use one source only)
“Knowers” may change their behavior when observed
Problematic interpersonal factors
Knowledge Engineering Process
Activities
Knowledge Acquisition

Acquisition of knowledge from human experts, books,
documents, or computer files
Knowledge Validation

Knowledge is validated and verified (using test cases) until the
quality is acceptable
Knowledge Representation

Organized knowledge; creation of a knowledge map and the
encoding of knowledge into a knowledge base
Inferencing

Design of software to enable the software to make inferences
based on the knowledge and the specifics of the a problem
Explanation and Justification

The design and programming of an explanation capability. Why
is this piece of information needed? How was a certain
conclusion derived.
Knowledge Engineering Process
Knowledge
validation
(test cases)
Sources of knowledge
(experts, others)
Knowledge
Acquisition
Knowledge
base
Explanation
justification
Inferencing
Encoding
Knowledge
Representation
Knowledge Sources
Documented (books, manuals, etc.)
Undocumented (in people's minds)
 From people, from machines
Knowledge Acquisition from Databases
Knowledge Acquisition Via the Internet
Knowledge Acquisition
Methods: An Overview
Manual :the knowledge engineer interacts directly with the experts

Interviews, tracking the reasoning process (protocol analysis), observing,
brainstorming, conceptual graphs and models
Semiautomatic (Expert-driven): the expert encodes his or her
expertise directly into the computer system or the developer uses
technology to facilitate the knowledge acquistion

Expert’s self reports, computer aided approaches (visual modeling);
graphical development environment where the initial knowledge domain can
be modeled and manipulated (decision trees based on business process
logic) ex. REFINER+ patient manager
Automatic (Computer Aided - Induction driven)


Minimize or eliminate the role of the KE and/or the expert
inference engines extract the knowledge from a set of examples
Manual Methods of
Knowledge Acquisition
Experts
Knowledge
engineer
Documented
knowledge
Coding
Knowledge
base
Expert-Driven
Knowledge Acquisition
Expert
Computer-aided
(interactive)
interviewing
Knowledge
engineer
Coding
Knowledge
base
Induction-Driven
Knowledge Acquisition
Case histories
and examples
Induction
system
Knowledge
base
Manual Acquisition Techniques
Interviewing: two common types are unstructured
(conversational) and structured (interrogation/using a
script)
Verbal Protocol Analysis:
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Most of the information necessary to model knowledge is found in the
cognitive process the knower uses to solve a problem/do a task
Document the step-by-step information processing and decision making
behavior by the knower
Concurrent: Think aloud or verbalize thoughts while doing task
Repertory Grid Method:
 Maybe manual or computerized
Expert Driven/Computer Aided
Reparatory Grid Analysis
 May also be employed by the KE
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Developed by Kelly (1955) who conceived humans as
”personal scientist” each with their own model of the world.
the expert compares successive groups of three objects and
tells why two differ from the third
Also used to infer similarities in construct beliefs held by
multiple experts
Knowledge and perceptions about the world are classified
and categorized by each individual as a personal, perceptual
model.
Machine learning/Automated
Rule Induction
Training set: example of a problem for which the
outcome is known
After given enough examples, the rule induction
system can create rules that fit the example cases.
The rules can be used to assess new cases for which
the outcome is not known.
For Example: Loan Officer’s tasks: Requests for
loans include information about the applicants such
as income, assets, age and number of dependents
TABLE 13.6 Case for Induction - A Knowledge Map
(Induction Table)
Attributes
Annual
Applicant
Income ($) Assets ($)
Age
Dependents
Decision
Mr. White
50,000
100,000
30
3
Yes
Ms. Green
70,000
None
35
1
Yes
Mr. Smith
40,000
None
33
2
No
Ms. Rich
30,000
250,000
42
0
Yes
From this case, it is easy to
derive the following three rules:
If Income is $70,000 or more approve the
loan
If income is $30,000 or more, age is at least
40, assets are above $249,000 and there are
no dependents approve the loan
If income is between $30,000 and $50,000
and assets are at least $100,000, approve
the loan
Multisource Knowledge Acquisition
It is likely that multiple sources will be needed to fully
acquire the knowledge for a problem and conflicting
views and opinions often arise.
Brainstorming/Electronic Brainstorming
 Goal is to come up with creative solutions. Idea
generation and evaluation
Consensus Decision
NGT
Delphi Method
Concept Mapping
Blackboarding
Validation and Verification of
the Knowledge Base
Quality Control
 Evaluation
 Validation
 Verification
Evaluation
 Assess an expert system's overall value
 Analyze whether the system would be usable,
efficient and cost-effective
Validation
 Deals with the performance of the system
(compared to the expert's)
 Was the “right” system built (acceptable level
of accuracy?)
Verification
 Was the system built "right"?
 Was the system correctly implemented to
specifications?
To Validate an ES
Test
 1. The extent to which the system and
the expert decisions agree
 2. The inputs and processes used by an
expert compared to the machine
 3. The difference between expert and
novice decisions
Some validation measures
Accuracy
Adaptability
Adequacy
Breadth
Depth
Face Validity
Generality
Precision
Realism
Reliability
Robustness
Usefulness