Knowledge Acquisition and modelling

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Knowledge Acquisition and
modelling


Introduction to Knowledge Acquisition and Elicitation

DIKW (Data, Information, Knowledge,
Wisdom)


Pyramid


Hierarchy


Framework


Continuum


Data, Information, Knowledge, Wisdom


Data...


is raw.


simply exists and has no significance beyond its existence (in
and of itself).


It is raining


Information


data that has been given meaning by way of relational
connection.



"meaning" can be useful, but does not have to be.


The temperature dropped 15 degrees and then it started raining.

Data, Information, Knowledge, Wisdom


Knowledge


the appropriate collection of information, such that it's intent is
to be useful.


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”



Wallace, Danny P.

(2007).
Knowledge Management: Historical and Cross
-
Disciplinary Themes
.

Data, Information, Knowledge, Wisdom


Understanding...


Cognitive and analytical.


Way you can take knowledge and synthesize new knowledge
from the previously held knowledge.


Wisdom...


calls upon all the previous experience


previous levels of consciousness


upon special types of human programming (moral, ethical
codes, etc.).


It rains because it rains.

Transition

Example


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


Procedural


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


I know that …


About ways in which things (concepts) are related to each other
and their properties


Types of Knowledge


Explicit


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


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


How to Boil An Egg


Simple task easily explained


How to tie a shoelace


Requires demonstration with commentary


E=mc2


Simply relates concepts


The position of keys on a keyboard


Most people know this sub
-
conciously but few conciously

Basic, Explicit
Knowledge

Deep, Tacit
Knowledge

Conceptual
Knowledge

Procedural
Knowledge

How to boil an
egg

E=mc
2

How to
interview an
expert

The properties
of knowledge

The position of
keys on a
keyboard

How to tie a
shoelace

Taken from Knowledge Acquisition in Practice A Step By Step Guide, Millton, Springer
-
Verlag

Exercise


Working in groups for 10
mins


Create a version of the previous slide with examples of your
own


Knowledge Acquisition


First

need

to

determine

what

that

knowledge

is


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


“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


Capturing knowledge about a subject domain


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


Can be a knowledge web, ontology, knowledge document etc

Eliciting Knowledge


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


People find it difficult to


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


Person attempting to acquire knowledge from someone
find it difficult to:


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


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


Nothing happens until knowledge is acquired


Sources of knowledge are unreliable


Domain experts provide incomplete, even incorrect knowledge


Domain experts may not be able to articulate their knowledge


Knowledge bases are hard to build


Computational knowledge representations are complex


Techniques


Limited range


Ignorance


Knowledge Acquisition Bottleneck


Narrow bandwidth.


Available channels convert
organizational knowledge from
its source (either experts,
documents, or transactions) are
relatively narrow.


Acquisition latency.


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.


Knowledge inaccuracy.


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.


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


A Method of Learning

Aristole


For our purposes


Elicitation


Collection


Analysis


Modelling


Validation


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


Identifying sources, vetting for quality, combining findings …

Terminology
-

Knowledge Elicitation


Sub
-
set of Acquisition


Focuses on retrieving knowledge from humans (usually
experts)


Lots of tacit




Terminology
-

Knowledge Codification


Representing knowledge in some form


Model


Rules


Ontology


Video


Presentation etc


Terminology
-

Knowledge Capture


Can be used instead of Acquisition or Codification


Generic term covering aspects of all three previous terms



Terminology


Knowledge Engineering


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
ed.), Reading, MA: Addison
-
Wesley

© 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

Knowledge


Sources


Documented


Written, viewed, sensory, behavior


Undocumented


Memory


Acquired from


Human senses


Machines



© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban,
Aronson, and Liang

Knowledge


Levels


Shallow


Surface level


Input
-
output


Deep


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


Categories


Declarative


Descriptive representation


Procedural


How things work under different circumstances


How to use declarative knowledge


Problem solving


Metaknowledge


Knowledge about knowledge

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban,
Aronson, and Liang

Knowledge Engineers


Professionals who elicit knowledge from experts


Empathetic, patient


Broad range of understanding, capabilities


Integrate knowledge from various sources


Creates and edits code


Operates tools


Build knowledge base


Validates information


Trains users

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban,
Aronson, and Liang

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.
Typical problems addressed

Example


Algorithm

-

a strategy, consisting of a series of steps,
guaranteed to find the solution to a problem, if there is a
solution.


Example:


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


Heuristic

-

a strategy to find the solution to a problem
which is not guaranteed to work.


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:


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”.



But…



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


Domain expert


Logic


Try to identify global
solutions, which are
appropriate and can be
made legitimate for all
possible contexts.


Aim at obtaining knowledge
models which are
transparent, objective, and
which consider a finite
number of factors.





Logic


Usually oriented towards the
individual case of their daily
working processes,


e.g. the individual patients.


Knowledge optimized for
solutions that are appropriate
for the given situation.


Try to consider as many
factors as possible and are
tolerant against
inconsistencies.


KEY DIFFERENCE between
knowledge
-
based systems and
other types of software


Knowledge Acquisition


Why a
Collaborative Process ?


Complex and highly specialized
domains


E.g. medicine


Characterized by a distribution of
knowledge between domain
experts.


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.


Different perspectives


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.


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?


“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


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


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


Human cognition and scientific theory construction
-

iterative
processes


Cognition



based on the construction of theoretical models


exposed to experimental data


from real or simulated worlds.


=> Human cognition is driven by feedback.


Theories must be validated or updated if new observations are
made.


Experimental acquisition of case data is essential in many
scientific disciplines


choice of experiments and the construction of simulation models has
an impact on the resulting theoretical models.

Knowledge Acquisition


Why an
Evolutionary Process?


Acquisition as a kind of theory construction


Human experts have to construct formal theories about the
domain


Backed by knowledge


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?


We do not understand how humans carry out reasoning
tasks


Makes it difficult to set out a detailed specification for
artefact

to imitate humans


Potential users are often unable to assess the benefits or
usage scenarios of the new system


especially when they are inexperienced computer users.


Artefact

modifies the work processes in which it is
installed.


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?


Process cannot be completely planned


Different and unknown cognitive and social perspectives.


Hard to predict


Often based on incorrect assumptions.


Domain experts required to transparently expose their daily
practice


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 ?


Knowledge is inherently complex and vague.


especially in non
-
deterministic domains e.g. medicine


Computers require formal data structures, which can be
evaluared

e.g. threshold values of patient observables.


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 ?


Knowledge modeling process itself produces new
knowledge.


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.


E.g. installing a neural network, addition of a database, creation of a
data warehouse


Knowledge Acquisition


Why an
Evolutionary Process ?


Knowledge can not be mined and processed like a
raw material, but rather comes into existence during
the communication


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.



Personal Construct Theory (
George Kelly
)


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


Moving systems towards increased meaning

Personal Construct Theory (PCT)


Key Idea


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


the person is capable of applying alternative constructions
(meanings) to any events in the past, present or future.


PCT


Alternative Constructivism


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.


=> reality does not reveal itself to us directly, but can be construed in a
variety of ways.


Constructs are the way in which things or people are either
similar or different.


=>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


Within man
-
the
-
scientist model,


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



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


Assumes a contrast between individual reality, social
reality and shared reality:


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


Problem space


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.


Task environment


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.


Model known as the task model

Newell and Simon’s Human Problem Solving


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


What is bias?


All views of reality are filtered.


Bias only exists in relation to some reference point.


Types of bias:


Motivational bias


expert makes accommodations to please the interviewer or some other
audience


Observational bias


Limitations on our ability to accurately observe the world


Cognitive bias



Mistakes in use of statistics, estimation, memory, etc.


Notational bias


Terms used to describe a problem may affect our understanding of it


Examples


Social pressure


response to verbal and non
-
verbal
cues from interviewer


Group think


response to reactions of other
experts


Impression management


response to imagined reactions of
managers, clients,…


Wishful thinking


response to hopes or possible gains


Appropriation


selective interpretation to support
current beliefs


Misrepresentation



expert cannot accurately fit a
response into the requested
response mode


Anchoring


contradictory data ignored once
initial solution is available


Inconsistency


assumptions made earlier are
forgotten


Availability


some data are easier to recall than
others


Underestimation of uncertainty



tendency to underestimate by a
factor of 2 or 3