Knowledge Acquisition and
modelling
Introduction to Knowledge Acquisition and Elicitation
DIKW (Data, Information, Knowledge,
Wisdom)
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Pyramid
Hierarchy
Framework
Continuum
Data, Information, Knowledge, Wisdom
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Data...
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is raw.
simply exists and has no significance beyond its existence (in
and of itself).
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It is raining
Information
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data that has been given meaning by way of relational
connection.
"meaning" can be useful, but does not have to be.
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The temperature dropped 15 degrees and then it started raining.
Data, Information, Knowledge, Wisdom
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Knowledge
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the appropriate collection of information, such that it's intent is
to be useful.
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If the humidity is very high and the temperature drops substantially
the atmospheres is often unlikely to be able to hold the moisture so it
rains.
“Knowledge is a fluid mix of framed experience, values, contextual
information, expert insight and grounded intuition that provides an
environment and framework for evaluating and incorporating new
experiences and information. It originates and is applied in the
minds of knowers. In organizations it often becomes embedded not
only in documents and repositories but also in organizational
routines, processes, practices and norm”
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Wallace, Danny P. (2007).Knowledge Management: Historical and CrossDisciplinary Themes.
Data, Information, Knowledge, Wisdom
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Understanding...
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Cognitive and analytical.
Way you can take knowledge and synthesize new knowledge
from the previously held knowledge.
Wisdom...
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calls upon all the previous experience
previous levels of consciousness
upon special types of human programming (moral, ethical
codes, etc.).
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It rains because it rains.
Transition
Example
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I have a box.
The box is 3' wide, 3' deep, and 6' high.
The box is very heavy.
The box has a door on the front of it.
When I open the box it has food in it.
It is colder inside the box than it is outside.
You usually find the box in the kitchen.
There is a smaller compartment inside the box with ice in it.
When you open the door the light comes on.
When you move this box you usually find lots of dirt underneath it.
Junk has a real habit of collecting on top of this box.
What is it?
Types of Knowledge
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Procedural
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How to
E.g. I Know How To Drive A Car
Processes, Tasks, Activities
And conditions under which tasks are performed
And sequence of tasks
Conceptual
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I know that …
About ways in which things (concepts) are related to each other
and their properties
Types of Knowledge
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Explicit
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Knowledge at the forefront of a person’s brain
Thought about in a deliberate, conscious way
Concerned with basic tasks, basic relationships between concepts,
basic properties of concepts
Not difficult to explain
Tacit
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Deep, embedded knowledge
At the back of a person’s brain
Built from experience rather than being taught
Gain when practice
Leads to activities which seem to require no conscious thought at
all
Types of Knowledge
Procedural
Knowledge
How to boil an
egg
How to
interview an
expert
How to tie a
shoelace
E=mc2
The properties
of knowledge
The position of
keys on a
keyboard
Conceptual
Knowledge
Basic, Explicit
Knowledge
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How to Boil An Egg
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How to tie a shoelace
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Requires demonstration with commentary
E=mc2
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Simple task easily explained
Simply relates concepts
The position of keys on a keyboard
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Most people know this sub-conciously but few conciously
Taken from Knowledge Acquisition in Practice A Step By Step Guide, Millton, Springer-Verlag
Deep, Tacit
Knowledge
Exercise
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Working in groups for 10 mins
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Create a version of the previous slide with examples of your
own
Knowledge Acquisition
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First need to determine what that knowledge is
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the process of Knowledge Acquisition and Elicitation
non-trivial process
The information is often locked away in the heads of
people - domain experts
The experts themselves may not be aware of the implicit
conceptual models that they use
Have to draw out and make explicit all the known
knowns, unknown knowns, etc….
Example
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“There are known knowns.
These are things we know that
we know. There are known
unknowns. That is to say, there
are things that we know we
don't know. But there are also
unknown unknowns. There are
things we don't know we don't
know.”
Donald Rumsfeld 2002
(US Secretary of Defense 2001
to 2006)
Knowledge Acquisition
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Capturing knowledge about a subject domain
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From people
And other sources
Using this to create a store of knowledge
Usable by many different applications, users and benefits
Does not have to be a database
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Can be a knowledge web, ontology, knowledge document etc
Eliciting Knowledge
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Most knowledge is in the heads of people
People have vast amounts of knowledge
People have a lot of tacit knowledge
They don't know all that they know and use
Tacit knowledge is hard (impossible) to describe
People with knowledge in organisations are usually very
busy and valuable people
Each person doesn't know everything
Difficulties of knowledge acquisition
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People find it difficult to
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Express their knowledge in a manner fully comprehensible to
the person who wishes to acquire it
Know exactly what the person wants
Give the right level of detail
Present ideas in a clear and logical order
Explain all the jargon and terminology of the subject domain
Recall everything relevant to the project/topic at hand
Avoid drifting into talking about irrelevant things
Difficulties of knowledge acquisition
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Person attempting to acquire knowledge from someone
find it difficult to:
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Understand everything the person says
Note down everything the person says
Keep the person talking about relevant issues
Maintain high level of concentration needed
Check they have fully understood what has been said
Difficulties of Knowledge Acquisition
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Arise due to human cognition and communication
Humans are good at communication and performing
complex activities
Not good at communicating complex activities to those
not from the same subject areas
Knowledge Acquisition Bottleneck
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Nothing happens until knowledge is acquired
Sources of knowledge are unreliable
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Knowledge bases are hard to build
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Domain experts provide incomplete, even incorrect knowledge
Domain experts may not be able to articulate their knowledge
Computational knowledge representations are complex
Techniques
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Limited range
Ignorance
Knowledge Acquisition Bottleneck
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Narrow bandwidth.
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Available channels convert
organizational knowledge from
its source (either experts,
documents, or transactions) are
relatively narrow.
Knowledge inaccuracy.
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Acquisition latency.
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Slow speed of acquisition is
frequently accompanied by a
delay between the time when
knowledge (or the underlying
data) is created and when the
acquired knowledge becomes
available to be shared.
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Experts make mistakes and so do
tools used to mine data and
information.
Maintenance can introduce
inaccuracies or inconsistencies into
previously correct knowledge
bases.
Maintenance trap.
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As knowledge base grows, so does
the requirement for maintenance.
Previous updates that were made
with insufficient care and foresight
accumulate and render future
maintenance more difficult .
As summarised by Christian Wagner in his paper titled Breaking the Knowledge Acquisition Bottleneck
Through Conversational Knowledge Management., 2006
Terminology - Knowledge Acquisition
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A Method of Learning
Aristole
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For our purposes
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Elicitation
Collection
Analysis
Modelling
Validation
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Of Knowledge for use in a project
Process of obtaining all data, information and knowledge
to get a consistent view of a person solving a problem
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Identifying sources, vetting for quality, combining findings …
Terminology - Knowledge Elicitation
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Sub-set of Acquisition
Focuses on retrieving knowledge from humans (usually
experts)
Lots of tacit
Terminology - Knowledge Codification
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Representing knowledge in some form
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Model
Rules
Ontology
Video
Presentation etc
Terminology - Knowledge Capture
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Can be used instead of Acquisition or Codification
Generic term covering aspects of all three previous terms
Terminology – Knowledge Engineering
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Feignbaum and McCorduck 1983
Integrating knowledge into a computer system
To solve problems that require extensive human
expertise
Typically building a knowledge based system
Shares a lot with software engineering
Feigenbaum, Edward A.; McCorduck,
Pamela (1983), The fifth generation (1st
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban,
Aronson, and Liang
Knowledge
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Sources
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Documented
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Undocumented
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Written, viewed, sensory, behavior
Memory
Acquired from
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Human senses
Machines
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban,
Aronson, and Liang
Knowledge
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Levels
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Shallow
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Surface level
Input-output
Deep
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Problem solving
Difficult to collect, validate
Interactions betwixt system components
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban,
Aronson, and Liang
Knowledge
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Categories
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Declarative
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Descriptive representation
Procedural
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How things work under different circumstances
How to use declarative knowledge
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Problem solving
Metaknowledge
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Knowledge about knowledge
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban,
Aronson, and Liang
Knowledge Engineers
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Professionals who elicit knowledge from experts
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Integrate knowledge from various sources
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Empathetic, patient
Broad range of understanding, capabilities
Creates and edits code
Operates tools
Build knowledge base
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Validates information
Trains users
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban,
Aronson, and Liang
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban,
Aronson, and Liang
Typical problems addressed
Problem type
Description
Diagnosis
Inferring malfunctions of an object from its behaviour and
recommending solutions.
Selection
Recommending the best option from a list of possible
alternatives.
Prediction
Predicting the future behaviour of an object from its
behaviour in the past.
Classification
Assigning an object to one of the defined classes.
Clustering
Dividing a heterogeneous group of objects into
homogeneous subgroups.
Optimisation
Improving the quality of solutions until an optimal one is
found.
Control
Governing the behaviour of an object to meet specified
requirements in real-time.
Example
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Algorithm - a strategy, consisting of a series of steps,
guaranteed to find the solution to a problem, if there is a
solution.
Example:
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How do you find the area of a triangular board, standing up
vertically with one edge on the ground?
Measure the length of the edge on the ground, multiply it by the
vertical height, and divide by two.
The answer will be exactly right, every time.
Which makes it an algorithm
Example
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Heuristic - a strategy to find the solution to a problem
which is not guaranteed to work.
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One sort of heuristic usually gives you the right answer but
sometimes gives you the wrong answer
Another sort gives you an answer which isn’t 100% accurate.
Example:
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How old are you?
Subtract the year you were born in from 2012.
The answer will either be exactly right, or one year short.
Which makes it a heuristic.
Knowledge Systems Analysis and Design
Davis’ law:
“For every tool there is a task perfectly suited to it”.
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But…
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It would be too optimistic to assume that for every task
there is a tool perfectly suited to it.
Knowledge Acquisition – Why a
Collaborative Process ?
Knowledge engineer
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Logic
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Domain expert
Logic
KEY DIFFERENCE between
 Usually oriented towards the
Try to identify global
knowledge-based
systems
andcase of their daily
individual
solutions,
which are
other
appropriate
andtypes
can beof softwareworking processes,
 e.g. the individual patients.
made legitimate for all
 Knowledge optimized for
possible contexts.
solutions that are appropriate
Aim at obtaining knowledge
for the given situation.
models which are
 Try to consider as many
transparent, objective, and
factors as possible and are
which consider a finite
tolerant against
number of factors.
inconsistencies.
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Knowledge Acquisition – Why a
Collaborative Process ?
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Complex and highly specialized
domains
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Different perspectives
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E.g. medicine
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Characterized by a distribution of
knowledge between domain
experts.
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Different experts – even from
one and the same discipline –
will have their own personal
preferences and mental models.
 E.g. Specialists for
anesthesiology will rarely
presume to build knowledge
models for cardiac surgery.
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improve the quality of the resulting
systems,
so ensure that the systems will
meet the requirements from
different user groups, especially
from both the technical and the
application domain.
Domain experts must ensure
that the system will be accepted
and trusted by their peers.
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E.g will a conservative user group
of medical doctors reject a clinical
decision-support system which is
solely designed from an engineer’s
perspective?
Knowledge Acquisition – Why a
Collaborative Process?
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“Knowledge is commonly socially constructed, through
collaborative efforts toward shared objectives or by
dialogues and challenges brought about by differences in
persons’ perspectives.”
Gavriel Salomon, Distributed Cognitions: Psychological
and Educational Considerations. Cambridge University Press, 1993
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Knowledge modeling must be heavily based on
communication and will usually require compromises.
Models are “negotiated in a social relationship”
Rammert, Relations that constitute technology And media that
make a difference: Toward a social pragmatic theory, 1999
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Of technicizatio negotiation is often difficult
KEY POINT
Experience shows that the bottleneck of building
knowledge systems lies more in the social
process than in the technology.
Human Cognition- Bernd Schmidt
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Human cognition and scientific theory construction - iterative
processes
Cognition
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=> Human cognition is driven by feedback.
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based on the construction of theoretical models
exposed to experimental data
from real or simulated worlds.
Theories must be validated or updated if new observations are
made.
Experimental acquisition of case data is essential in many
scientific disciplines
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choice of experiments and the construction of simulation models has
an impact on the resulting theoretical models.
Knowledge Acquisition – Why an
Evolutionary Process?
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Acquisition as a kind of theory construction
Human experts have to construct formal theories about the
domain
Backed by knowledge
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either resides informally in their heads
or can be acquired from some other knowledge source.
Resulting knowledge model is part of a knowledge-based
system which can operate in real or simulated worlds.
Tests in both worlds produce feedback which allows the
domain expert to revise the knowledge models.
When installed in the real application scenario, the system
even changes the real world and thus produces new
requirements, which recursively suggest changes to the
knowledge model.
Knowledge Acquisition – Why an
Evolutionary Process?
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We do not understand how humans carry out reasoning
tasks
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Potential users are often unable to assess the benefits or
usage scenarios of the new system
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Makes it difficult to set out a detailed specification for artefact
to imitate humans
especially when they are inexperienced computer users.
Artefact modifies the work processes in which it is
installed.
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Users modify their environment and their use of the system
New working culture emerges.
Changes requirements => knowledge models must be updated.
Knowledge Acquisition – Why an
Evolutionary Process?
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Process cannot be completely planned
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Different and unknown cognitive and social perspectives.
Hard to predict
Often based on incorrect assumptions.
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Domain experts required to transparently expose their daily
practice
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but this “practice necessarily operates with deception”
Every artefact resulting is only an approximation of reality
and the actors involved in the process speak different
“languages”.
Knowledge Acquisition – Why an
Evolutionary Process ?
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Knowledge is inherently complex and vague.
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especially in non-deterministic domains e.g. medicine
Computers require formal data structures, which can be
evaluared e.g. threshold values of patient observables.
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Experts tend to use trial-and-error methods to determine such
thresholds, until the system exposes the expected behavior.
Cannot predict progress which may change beliefs in KB
Knowledge Acquisition – Why an
Evolutionary Process ?
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Knowledge modeling process itself produces new
knowledge.
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Self-observation performed during analysis of the existing work
processes can lead to new insights
Knowledge is being translated and reorganized => evolves in
the process of being encoded and formatted for the system
Existing work processes are challenged when analyzed – can
lead to redesign during acquisition
Installation of knowledge-based systems may require
“digitization” of the data flow in the process.
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E.g. installing a neural network, addition of a database, creation of a
data warehouse
Knowledge Acquisition – Why an
Evolutionary Process ?
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Knowledge can not be mined and processed like a
raw material, but rather comes into existence during
the communication
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Communication will influence the resulting artefacts.
Process is characterized by reciprocities between engineers
and experts
Information provided by the expert depends on the
context.
As a domain expert gets more and more used to the formal
view of the knowledge engineer, he/she will adjust her style,
and vice-versa.
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Personal Construct Theory (George Kelly)
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Theory that gives an account of how people experience the
world and make sense of that experience.
‘Person as a scientist’
Emphasises human capacity for meaning making, agency, and
ongoing revision of personal systems of knowing across time
Individuals are seen as creatively formulating hypotheses about
the areas of their lives, in an attempt to make them
understandable or predictable.
Predictability is sought as a guide to practical action in
concrete contexts and relationships.
People engage in continuous extension, refinement, and
revision of their systems of meaning
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Moving systems towards increased meaning
Personal Construct Theory (PCT)
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Key Idea
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the world is 'perceived' by a person in terms of whatever
'meaning' that person applies to it
and the person has the freedom to choose a different
'meaning' of whatever he or she wants.
i.e. the person has the 'freedom to choose' the meaning that
one prefers or likes.
Alternative constructivism
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the person is capable of applying alternative constructions
(meanings) to any events in the past, present or future.
PCT – Alternative Constructivism
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We assume that all of our present interpretations of the
universe are subject to revision or replacement...
There are always some alternative constructions available to
choose among in dealing with the world.
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Constructs are the way in which things or people are either
similar or different.
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=> reality does not reveal itself to us directly, but can be construed in a
variety of ways.
=>simultaneously differentiates and integrates.
To construe is both to abstract from past events, and provide a
reference axis for anticipating future events based on that
abstraction.
Kelly's notion of a personal scientist assumes that all people
actively seek to predict and control events by forming relevant
hypotheses, and then testing them against their experience.
PCT
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Within man-the-scientist model,
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the individual creates his or her own ways of seeing the world in which
(s)he lives;
the world does not create them for him;
(s)he builds constructs and tries them on for size;
the constructs are sometimes organized into systems
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groups of constructs which embody subordinate and superordinate relationships;
the same events can often be viewed in the light of two or more
systems, yet the events do not belong to any system; and
the individual's practical systems have particular foci and limited ranges
of convenience.
PCT
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Assumes a contrast between individual reality, social
reality and shared reality:
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Individuality: "persons differ from each other in their construction
of events."
Communality: "to the extent one person employs a construction of
experience which is similar to that employed by another, his
psychological processes are similar to those of the other person."
Socialty: "to the extent that one person construes the
construction processes of another, he may play a role in a social
process involving the other person."
Over the last 50 years, the theory has found its home in
the areas of artificial intelligence, education, human
computer interaction, and human learning.
Newell and Simon’s Human Problem Solving
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Problem space
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A person’s internal (mental)
representation of a problem, and the
place where problem-solving activity
takes place.
Model known as performance model
Represents the problem solving
behavior of one person who is
performing a specific task, but are not
adequate for system development
since they are constrained to a single
performer on a single task.
Seen as consisting of knowledge
states, and problem solving proceeds
by a selective search within the
problem space, according to Newell
and Simon using rules of thumb
(heuristics) to guide the search.
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Task environment
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The physical and social environment
in which problem solving takes place.
Situations which do not influence
individual behavior can be studied by
only analyzing the task environment.
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Model known as the task model
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Newell and Simon’s Human Problem Solving
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Both task and performance models are required to
enable problem solving behavior to be adequately
modeled within a specific domain.
Unstructured environments are open for individual
behavior, well-structured environments encourage
common behavior.
Bias
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What is bias?
 All views of reality are filtered.
 Bias only exists in relation to some reference point.
Types of bias:
 Motivational bias
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Observational bias
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Limitations on our ability to accurately observe the world
Cognitive bias
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expert makes accommodations to please the interviewer or some other
audience
Mistakes in use of statistics, estimation, memory, etc.
Notational bias
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Terms used to describe a problem may affect our understanding of it
Examples
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Social pressure
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response to imagined reactions of
managers, clients,…
Wishful thinking
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response to reactions of other
experts
Impression management
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response to verbal and non-verbal
cues from interviewer
Group think
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response to hopes or possible gains
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selective interpretation to support
current beliefs
assumptions made earlier are
forgotten
Availability
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contradictory data ignored once
initial solution is available
Inconsistency
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expert cannot accurately fit a
response into the requested
response mode
Anchoring
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Appropriation
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Misrepresentation
some data are easier to recall than
others
Underestimation of uncertainty
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tendency to underestimate by a
factor of 2 or 3