1. Knowledge Acquisition and modelling
Data, information & knowledge
n Data
n “raw signals”
n Information
n meaning attached to data
S O S = Save Our Souls
n Knowledge
n attach purpose and competence to information
n potential to generate action (remember A. Newell)
emergency alert ® start rescue operation
A Short History of
Knowledge Systems
general-purpose
search engines
(GPS)
first-generation
rule-based systems
(MYCIN, XCON)
emergence of
structured methods
(early KADS)
mature
methodologies
(CommonKADS)
=> from art to discipline =>
First generation “Expert” Systems
n shallow knowledge
base
n single reasoning
principle
n uniform
representation
n limited explanation
capabilities
reasoning
control
knowledge
operates on
Introduction to Knowledge Acquisition and Elicitation

2. DIKW (Data, Information, Knowledge, Wisdom)
Pyramid
Hierarchy
Framework
Continuum

3. Data, Information, Knowledge, Wisdom
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.

4. Data, Information, Knowledge, Wisdom
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.

5. 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.

6. Transition

7. 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?
At some point in the sequence you connected with the pattern and understood it was a description of a refrigerator. From that point on each statement only added confirmation to your understanding.

8. Types of Knowledge Procedural Conceptual 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

9. Types of Knowledge Explicit Tacit
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

10. Types of Knowledge How to interview an expert How to boil an egg
Basic, Explicit Knowledge
Deep, Tacit Knowledge
Conceptual Knowledge
Procedural Knowledge
How to boil an egg
E=mc2
How to interview an expert
The properties of knowledge
The position of keys on a keyboard
How to tie a shoelace
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
Taken from Knowledge Acquisition in Practice A Step By Step Guide, Millton, Springer-Verlag

11. Exercise Working in groups for 10 mins
Create a version of the previous slide with examples of your own

12. 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…. 12

13. 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 to 2006)

14. 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

15. 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 15

16. 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

17. 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

18. 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

19. 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 19

20. 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

21. 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 …

22. Terminology - Knowledge Elicitation
Sub-set of Acquisition
Focuses on retrieving knowledge from humans (usually experts)
Lots of tacit

23. Terminology - Knowledge Codification
Representing knowledge in some form
Model
Rules
Ontology
Video
Presentation etc

24. Terminology - Knowledge Capture
Can be used instead of Acquisition or Codification
Generic term covering aspects of all three previous terms

25. 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

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

27. Knowledge Sources Documented Undocumented Acquired from
Written, viewed, sensory, behavior
Undocumented
Memory
Acquired from
Human senses
Machines
© Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

28. Knowledge Levels Shallow Deep Surface level Input-output
Problem solving
Difficult to collect, validate
Interactions betwixt system components
© Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

29. Knowledge Categories Declarative Procedural Metaknowledge
Descriptive representation
Procedural
How things work under different circumstances
How to use declarative knowledge
Problem solving
Metaknowledge
Knowledge about knowledge
© Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

30. 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
© Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

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

32. Typical problems addressed
Type of problem – influences out choice of the tool for building an intelligent system
Something to detect faults in an electrical circuit and guide user through diagnosis
Domain knowledge can often be represented as production rules and this a rule-based expert system could be the right candidate for solution
Choice of tool will also depend on the form and content of the solution
Systems build for diagnosis often require an explanation facility to enable them to justify their solutions
This is an essential component of an expert system but not of a neural network
Neural nets would be a good choice for classification and clustering problems where the result is often more important than understanding the reasoning process
Next step is to identify the participants
Knowledge engineer, domain expert
Then specify the objectives – gain competitive edge, improve decision making, reduce labour costs 32

33. 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

34. 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.

35. 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.
Need guidelines for selecting right tools for given problem 35

36. 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
Principal difference in the attitudes and goals of domain experts and knowledge engineers:
In normal software where the engineers are usually able to acquire a sufficiently deep understanding of the problem domain, so that they can build the system with little or no further assistance by domain experts. 36

37. 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? 37

38. 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. 38

39. 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.
Beneficial to take a closer look at the human cognition process, because knowledge first has to be built up in a domain expert’s mind before it is ready to be modeled. 39

40. 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. 40

41. 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.
Other reasons why knowledge models will almost necessarily change while the knowledge-based system is built and used. 41

42. 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”. 42

43. 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 43

44. 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 44

45. 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. 45

46. 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 Psychology is a way of looking at the world. The founder, George Kelly, presents a theory that gives an account of how people experience the world and make sense of that experience. 46

47. 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.

48. 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. 48

49. 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. 49

50. 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. 50

51. 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
Introduced the concepts of problem space and task environment.
The reason for this distinction is that individual behavior influences problem solving; this influence is greater the less structured the task is.
Where behavioral aspects of problem solving are closely related to the decision maker and not to the task environment, we have to look inside the person’s mind to explain this behavior. 51

52. 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. 52

53. Bias What is bias? Types of 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 53

54. Examples Social pressure Misrepresentation Group think Anchoring
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 54