AI – CS289
Knowledge Acquisition
Knowledge Acquisition
11th September 2006
Dr Bogdan L. Vrusias
b.vrusias@surrey.ac.uk
AI – CS289
Knowledge Acquisition
Contents
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•
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Defining Knowledge Acquisition
Interviewing for Knowledge Acquisition
Case Study
Terminology
11th September 2006
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AI – CS289
Knowledge Acquisition
Knowledge Acquisition
• Knowledge acquisition can be regarded as a method by which a
knowledge engineer gathers information mainly from experts, but
also from text books, technical manuals, research papers and other
authoritative sources for ultimate translation into a knowledge base,
understandable by both machines and humans.
• The person undertaking the knowledge acquisition, the knowledge
engineer, must convert the acquired knowledge into an electronic
format that a computer program can use.
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AI – CS289
Knowledge Acquisition
The Process of Knowledge Acquisition
• In the process of Knowledge Acquisition for an Expert System Project,
the knowledge engineer basically performs four major tasks in
sequence:
– First, the engineer ensures that he or she understands the aims and
objectives of the proposed expert system to get a feeling for the potential
scope of the project.
– Second, the engineer develops a working knowledge of the problem
domain by mastering it's terminology by looking up technical dictionaries
and terminology data bases. For this task the key sources of knowledge
are identified: textbooks, papers, technical reports, manuals, codes of
practice, users and domain experts.
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AI – CS289
Knowledge Acquisition
The Process of Knowledge Acquisition
– Third, the knowledge engineer interacts with experts via meetings
or interviews to acquire, verify and validate their knowledge.
– Fourth, the knowledge engineer produces a "document knowledge
base"; a document or group of documents (nowadays in electronic
format) which form an intermediate stage in the translation of
knowledge from source to computer program. This comprises:
• the interview transcripts,
• the analysis of the information they contain
• and a full description of the major domain entities (e.g. tasks, rules
and objects).
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AI – CS289
Knowledge Acquisition
Interviewing Techniques for KA
• The Informal or Overview Interview
– To familiarise the knowledge engineer with the domain and the particular
problem which the proposed expert system is intended to solve.
• The Focused Interview
– Focused interviews are similar to ordinary "chat show" conversations or
discussions where the interviewer is interested in a topic of which the
interviewee is knowledgeable.
– It is normally conducted by following a pre-determined agenda. The
interviewee is initially prompted with the first topic or question, but is
given a great deal of freedom of expression thereafter.
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AI – CS289
Knowledge Acquisition
Interviewing Techniques for KA
• The Structured Interview
– Structured interviews normally occur well into the knowledge acquisition
phase.
– They are used when information is required in much greater depth and
detail than the other techniques can offer and is more interrogative than
conversational.
• 'Think aloud' Protocols
– A technique used by cognitive psychologists to study the strategies with
which people solve problems. Case studies are advantageous because the
end results are already known so the expert should repeat the strategy he
used for that problem when describing his solution.
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AI – CS289
Knowledge Acquisition
Interviewing Techniques – Do's and Don'ts
• It is essential to record and transcribe all the (video/audio-taped)
interviews.
• Transcripts should be clearly cross-referenced to (video/audio-tape)
recorder counter numbers.
• Include all the sketches, photocopies or reproductions of diagrams,
tables or the like, that were referred to during the interview(s).
• Once completed a copy should be sent to the interviewee for
comments, corrections and criticism. There is always the possibility of
misunderstanding by the knowledge engineer when interpreting a
statement or explanation.
• By involving the expert in validating his or her own transcript it
reduces the chance of erroneous information appearing in the
prototype's knowledge base.
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AI – CS289
Knowledge Acquisition
Knowledge Acquisition Tasks
Discovery Phase
Learn
Objectives
Revision Phase
Domain Terminology
Revise
Salient domain features
Outline
Scope of the problem
Tec hnique Used
Overview Interview
Consult Tex tbook s
Constrain
Knowledge Sourc es
Focussed
Interviews
Literature Review
Problem-solving tas ks
Verify
Specify
Domain objects
Structured
Interview
Paper Knowledge Base
Validate
Produc e
Rule Animation
Rules and Heuristic s
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AI – CS289
Knowledge Acquisition
A Case Study in Interview-based KA
• Project PLAIM (Platform Lifetime Assessment through Analysis,
Inspection and Maintenance) was sponsored by the European Union
during 1988-89.
• The project had two major objectives: first to collate, analyze and
archive the inspection and maintenance related data. And, the second
aim is to establish a computer program which will:
– allow access, and guide the user to the appropriate data (or data files);
– provide an 'intelligent' interface to mathematical models, industrystandard simulation programs and empirical equations
– acquire, formalise and disseminate the experiential (and previously
undocumented) knowledge of inspecting and maintaining off-shore
structures.
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AI – CS289
Knowledge Acquisition
A Case Study – The Interviews
• A total of three knowledge elicitation interviews were conducted
lasting over 5 hours and covering a broad range of topics relevant to
the target problem:
– The first interview provided the overview.
– The second being much more focused on domain description and
terminology.
– The third interview was the only formally conducted, structured interview.
• Regular prototype revision meetings were conducted in a similar
interrogative style inspired by a demonstration of the prototype and
review of the current knowledge base.
• All but one of the interviews were recorded using a video-cassette
recorder; all were transcribed and, where considered useful, the
transcripts were sent to or discussed with the interviewee.
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AI – CS289
Knowledge Acquisition
A Case Study – The Interviews
Interviewee
Departmental Manager, AME Ltd
Departmental Manager, AME Ltd.
Senior Structural Engineer, UK
Offshore Operator
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Subjects Covered
Interview Technique
Overview and explanation of idea
behind PLAIM
Overview Interview
How an expert system is expected to
fit in and what it was expected to do.
(followed by structured interviews at
prototype demonstrations)
General introduction to terminology,
design practice;
Focused
design for fatigue, classification of
members, nodes, joint types,
construction, practice, welding and
fabrication.
Think aloud
Current inspection, repair and
maintenance. Assessment of AME
proposed approach to IRM; Opinion
of where expert systems would be
useful generally and specifically to
the operator practice.
Structured
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AI – CS289
Knowledge Acquisition
A Case Study – Overview Interview
• The overview interview requires the preparation of a well targeted set
of questions. The interviewee, the PLAIM project manager, was video
taped and a transcript of his interview was produced.
– The interview began with a discussion of a 'flow chart' for conducting
fatigue analysis of offshore structures.
• The interviewer, who already had access to a variety of contract
documents related to PLAIM, asked the expert to explain the 'flow
chart'. This led to a set of well focused questions such as:
– Please outline algorithms, data input and output, data requirements.
– What sort of knowledge and expertise is expected to be included in this
prototype?
– Please give your view on judgments on accuracy and calibration with real
data?
– How do you tell from residual strength and reliability index the lifetime of
the structure or cracked joint? i.e. how long before the crack causes
failure?
– Please suggest further information sources.
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AI – CS289
Knowledge Acquisition
A Case Study – Overview Interview
• The result of the overview interview led to the identification of the
broad scope of the project and in cataloguing important technical
documentation as textual knowledge sources.
• The preparation of the questionnaire for the interview helped the
knowledge engineer to learn much about the expert's impression of the
problem and his understanding of how an expert system could be
applied.
• Some key phrases of the domain terminology were also introduced and
explained.
• A number of knowledge sources were identified by the domain experts
ranging from research papers in learned journals to textbooks and
repair and maintenance manuals.
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AI – CS289
Knowledge Acquisition
A Case Study – Focused Interview
• The purpose of this interview was to cover two broad topics. Firstly, to
describe a typical oil production platform and secondly to outline
fatigue damage design, analysis and repair practices. The help of a
second domain expert, who has hands-on experience of designing such
structures, was enlisted. His reply comprised the following topics (The
numbers on the right are video-recorder counters):
000
063
125
140
157
170
222
254
273
313
357
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Major Components of a Typical Platform (Figure 1)
A Barge Launched Jacket (Figure 2)
Fatigue Problem Areas
Pile Sleeves
Nodes
Importance of Various Members in a Jacket
Scour problems
Anodes and Corrosion Protection
Defects
Fatigue Analysis: Procedure and Calculations
Wave Data
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AI – CS289
Knowledge Acquisition
A Case Study – Focused Interview
• 000 Major components of a Typical rig (Diagram 1)
– The diagram shows the topside, consisting of the cellar deck to support the
drilling rig, accommodation module, helideck etc. Also shown are the
flare boom and other crane booms. The jacket supports the topside fixing
it securely to the sea-bed above the level of the highest waves likely to be
encountered in the North Sea. Piles are driven through guides in the legs
of the jacket into the bed rock to ensure the rig position is solid. As the
jacket structure is a group of frames made up of tubular steel sections and
linked together by other frames, a method of identifying individual
members and nodes at which groups of members coincide is required.
The convention used on engineering drawings to identify the frame
structure in plan view at each level or staging is shown below. This
particular jacket was lifted into place using a crane.
11th September 2006
Bogdan L. Vrusias © 2006
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AI – CS289
Knowledge Acquisition
A Case Study – Focused Interview
GRID SYSTEM
Topside
Rows
1
2
3
Cellar Deck
B
Sea level
Jacket
Fac es
A
Sea bed
Piles
Diagram 1. Major components of a Typical rig
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AI – CS289
Knowledge Acquisition
A Case Study – Focused Interview
• 015 A Barge Launched Jacket (Figure 2)
– The isometric view of the same jacket shows in more detail aspects of the
frame structure the type of loading experienced and typical trouble spots.
The increasing diameter of the leg is so that it is strong enough to be able
to take the increasing axial load at the lower levels. When a wave hits the
platform it causes an overturning moment which in turn causes an axial
load in the leg. This is resisted by the piles, but in this example the
eccentricity of the load due to the leg shape causes flexure in the short
stubby diagonal braces and causes fatigue problems in their corresponding
node joints. Other crossed-diagonal members also experience fatigue due
to this sort of flexure but not to the same degree.
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AI – CS289
Knowledge Acquisition
A Case Study – Focused Interview
conduc tor
frame
WAVE
increas ing
leg
di ameter
Short s tubby members
Diagram 2. A Barge Launched Jacket
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AI – CS289
Knowledge Acquisition
A Case Study – Analysing the Data
OBJECT
ATTRIBUTES
rig or platform topsides
part of platform
cellar deck
part of platform
jacket
part of platform
sea-bed piles
part of jacket
attached to (leg 1, 2, 3, 4) ...
number of guides
sleeve type
member
part of jacket
part of level frame
type of (leg, brace, diagonal, horizontal ...)
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AI – CS289
Knowledge Acquisition
A Case Study – Analysing the Data
• rule 1
if:
then:
jacket is barge launched
jacket will have extra structural members included
purely for transportation and launching which become
redundant once it is placed on the sea bed.
• rule 2
if:
then:
a wave strikes the jacket
the diagonal members will take the load/shear force.
• rule 3
if:
then:
a jacket has sloping legs
any crossed diagonal members at the lowest level will
flex and cause fatigue in their corresponding node joints.
• rule …
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AI – CS289
Knowledge Acquisition
A Case Study – Transcript Corrections
• The interview transcript was sent to the expert for comments and
criticism and was duly returned with corrections. It is not easy to
classify the comments, except that the expert imposed constraints on
his statements or expanded on others. Some examples below are
presented to highlight the point we have just made. The amendments
are shown in italics:
• 000 Major components of a Typical rig
– The diagram shows the topsides, consisting of the cellar deck to support
the drilling rig, accommodation module, helideck etc. Also shown is the
flare boom and other crane booms. The jacket supports the topsides fixing
it securely to the sea-bed above the level of the highest waves likely to be
encountered in the North Sea at the site. Piles are driven through guides
attached to the legs of the jacket into the sea be to ensure the rig position
is …
11th September 2006
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AI – CS289
Knowledge Acquisition
A Case Study – Structured Interview
• Based on the data collected from the previous two interviews a third
interview was prepared and then planned with the Senior Structural
Engineer UK Offshore Operator.
• The main areas of the third interview was focussed on the following:
– Current inspection, repair and maintenance.
– Assessment of AME proposed approach to IRM.
– Opinion of where expert systems would be useful generally and
specifically to the operator practice.
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AI – CS289
Knowledge Acquisition
Specialist Languages
• Characteristics of Specialist languages:
– Considered variants of natural language are restricted lexically,
syntactically and semantically.
– Have a preponderance of open class words.
– Have single and compound noun phrases (NP). These phrases are used to
name objects, events, actions and states.
– Have few adjectives and adverbs.
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AI – CS289
Knowledge Acquisition
Terminology
• Systematically organised collection of terms and their elaborations,
including definitions, grammatical categories, and related term.
• The system used is usually a conceptual one. The conceptual basis is
that of the discipline and its potential application. For example:
– physicists organise their subject discipline in terms of forces, energy and
mass;
– chemists focus on atoms and molecules;
– biologists organise their subject in terms of kingdoms, families and
species.
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AI – CS289
Knowledge Acquisition
Terminology
• Usually nouns, few verbs, adjectives or adverbs.
• Terminology can be documented in:
– paper form: terminological dictionaries; glossaries; thesauri; hierarchy
diagrams or ontology.
– electronic form: term-bases; hierarchies; ontology.
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AI – CS289
Knowledge Acquisition
Terminology and Knowledge
• The terminology of a specialist domain, and to some extent the details
of the problem-solving heuristics and that of the meta rules, reflect the
underlying structure of the domain.
• This structure allows the members of the domain community to
develop new ideas, to challenge existing wisdom, to disseminate and to
learn from each other. In effect, the underlying structure provides a
cohesive framework for the domain community to function as a whole.
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AI – CS289
Knowledge Acquisition
Closing
•
•
•
•
Questions???
Remarks???
Comments!!!
Evaluation!
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