1. Software and Knowledge Engineering Lecturer: Deirdre Lawless
DT228/3
Software and Knowledge Engineering
Lecturer: Deirdre Lawless

2. Data, Information, Knowledge, Wisdom
is raw.
simply exists and has no significance beyond its existence (in and of itself).
Information
data that has been given meaning by way of relational connection.
"meaning" can be useful, but does not have to be.
Knowledge
the appropriate collection of information, such that it's intent is to be useful.
Understanding...
cognitive and analytical.
It is the process by which you can take knowledge and synthesize new knowledge from the previously held knowledge.
The difference between understanding and knowledge is the difference between "learning" and "memorizing".
People who have understanding can undertake useful actions
Wisdom...
an extrapolative and non-deterministic, non-probabilistic process.
It calls upon all the previous levels of consciousness, and specifically upon special types of human programming (moral, ethical codes, etc.).
For example, elementary school children memorize, or amass knowledge of, the "times table". They can tell you that "2 x 2 = 4" because they have amassed that knowledge (it being included in the times table). But when asked what is "1267 x 300", they can not respond correctly because that entry is not in their times table.
To correctly answer such a question requires a true cognitive and analytical ability that is only encompassed in the next level... understanding.
Wisdom is therefore, the process by which we also discern, or judge, between right and wrong, good and bad. I personally believe that computers do not have, and will never have the ability to posses wisdom. Wisdom is a uniquely human state, or as I see it, wisdom requires one to have a soul, for it resides as much in the heart as in the mind. And a soul is something machines will never possess (or perhaps I should reword that to say, a soul is something that, in general, will never possess a machine).
because they can synthesize new knowledge, or in some cases, at least new information, from what is previously known (and understood). That is, understanding can build upon currently held information, knowledge and understanding itself. In computer parlance, AI systems possess understanding in the sense that they are able to synthesize new knowledge from previously stored information and knowledge.
It beckons to give us understanding about which there has previously been no understanding, and in doing so, goes far beyond understanding itself. It is the essence of philosophical probing.
it asks questions to which there is no (easily-achievable) answer, and in some cases, to which there can be no humanly-known answer period

3. Data, Information, Knowledge, Wisdom Examples
Data represents a fact or statement of event without relation to other things.
Ex: It is raining.
Information embodies the understanding of a relationship of some sort, possibly cause and effect.
Ex: The temperature dropped 15 degrees and then it started raining.
Knowledge represents a pattern that connects and generally provides a high level of predictability as to what is described or what will happen next.
Ex: 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.
Wisdom embodies more of an understanding of fundamental principles embodied within the knowledge that are essentially the basis for the knowledge being what it is. Wisdom is essentially systemic.
Ex: It rains because it rains. And this encompasses an understanding of all the interactions that happen between raining, evaporation, air currents, temperature gradients, changes, and raining.

4. Transition

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ibxtedstes bet3 ibtes otesb tapbesct ehracts
Does this mean anything to you ?

6. Knowledge 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.

7. What is Knowledge Management ?
An approach based on the central role of knowledge in organisations
Objective to manage and support knowledge work and to maximise the added value of knowledge for the organisation
Aims:
identifying and analysing knowledge and knowledge work
developing procedures and systems for generating, storing, distributing and using knowledge in the organisation.

8. What is Knowledge Management About?
improving the ability to acquire knowledge,
improving the quality of knowledge,
and using knowledge to its greatest advantage

9. Objective of KM
To create added value for the organisation at three distinct levels:
Improvement of existing business processes
what can we do better
Development of new products and services
what can we do more
Improving the strategic position, aimed at:
Developing unique knowledge
Applying knowledge to innovative products and services
Strengthening the competitive position
Safeguarding the organisation’s continuity
Improving flexibility
Creating an attractive work environment
Making the organisation independent of the individual employee’s knowledge

10. How can computers help? Share knowledge Discover Knowledge
Assist people
About both people and technology
Knowledge not just stored in a knowledge base but constructed through co-operation with a person using that knowledge base

11. Knowledge Engineering
KE is an engineering discipline that involves integrating knowledge into computer systems in order to solve complex problems normally requiring a high level of human expertise.
At present, it refers to the building, maintaining and development of knowledge-based systems

12. Knowledge Engineering
Or it refers to transferring human knowledge into some form of knowledge based system (KBS)
Five steps
Acquisition
Obtaining knowledge from various sources human experts, documents, existing computer systems etc
Validation
Check knowledge acquired using test cases
Representation
Producing a map of knowledge and encoding into some sort of knowledge base
Inferencing
Forming links in the knowledge so that a KBS can make a decision or provide advice
Explanation and justification
Allow a KBS to show how it reached a conclusion

13. Knowledge Engineer
Person who translates knowledge relating to an area of expertise into the knowledge base which supports a KBS

14. 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

15. 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

16. 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

17. Eliciting Knowledge Most knowledge is in the heads of experts
Experts have vast amounts of knowledge
Experts have a lot of tacit knowledge
They don't know all that they know and use
Tacit knowledge is hard (impossible) to describe
Experts are very busy and valuable people
Each expert doesn't know everything 17

18. Knowledge Acquisition/Knowledge Engineering
Knowledge Representation is about representing some knowledge
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 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…. 18

19. Knowledge Acquisition
Capturing knowledge about a subject domain
From experts
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

20. Difficulties of knowledge acquisition
Experts find it difficult to
Express their knowledge in a manner fully comprehensible to the knowledge engineer
Know exactly what the engineer 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
Avoid drifting into talking about irrelevant things

21. Difficulties of knowledge acquisition
Engineers find it difficult to
Understand everything the expert says
Note down everything the expert says
Keep the expert talking about relevant issues
Maintain high level of concentration needed
Check they have fully understood what has been said

22. 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

23. 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
Experts
poor appreciation of different types
ignorance
Expertise
need to organise knowledge into higher level units 23

24. What is a Knowledge Based System ?
Use knowledge to solve problems
Exercise knowledge to solve problems
Knowledge used is that possessed by people knowledgeable in the domain
Cause-and-effect
Heuristics
Etc
Definition:
A computerised system that uses domain knowledge to arrive at a solution to a problem within that domain.
The solution is essentially the same as one concluded by a person knowledgeable about the domain when confronted with the same problem. 24

25. What is a Knowledge Based System ?
Computer system that is programmed to imitate or assist with human problem-solving
By means of artificial intelligence
And reference to a database containing human knowledge on a particular subject.
Core components are the knowledge base and the inference mechanisms.
Typical Architecture
a knowledge base (where the knowledge is stored)
Data plus more,
an inferencing engine or reasoning engine,
a working memory where the initial data and intermediate results are stored 25

26. Knowledge based systems
Use highly specific domain knowledge
Heuristic nature of knowledge rather than algorithmic
Human ability
Separation of knowledge from how it is used
Knowledge of how to infer something 26

27. Knowledge Based Systems Development Team
Expert System
Development Team
Project Manager
Domain Expert
Knowledge
Engineer
Programmer
Expert System
Knowledge is provided by a human expert and we want to put that into a computer
Need the computer to act as an intelligent assistant in some specific domain of expertise or to solve a problem that would otherwise have to be solved by an expert
Most popular are rule-based
End-User

29. Intelligence ?
the ability to comprehend; to understand and profit from experience
Intelligence is a general mental capability that involves the ability to reason, plan, solve problems, think abstractly, comprehend ideas and language, and learn. In psychology, the study of intelligence is related to the study of personality but is not the same as creativity, personality, character, or wisdom. 29

30. Intelligent System ? 30

31. Artificial Intelligence
Definition ?
Science that provides computers with the ability to represent and manipulate symbols so that they can be used to solve problems not easily solved through algorithmic methods
Most methods founded on realization that intelligence is tightly coupled with knowledge
Knowledge is associated with symbols that are manipulated
Human intelligence ? Definition ?
Human intelligence ? – innate ability to learn and manipulate knowledge in order to communicate or solve a problem 31

32. What is Artificial Intelligence ?
Agreement that it is concerned with two things
Studying human thought processes
Representing these processes via machines
Computers
Robots
Artificial Intelligence is behaviour by a machine which if performed by a human would be considered intelligent
“Artificial Intelligence is the study of how to make computers do things at which, at the moment, people are better” (Rich & Knight 1991)
So what is the main thing you take out of these definitions ?
How do you get machines to behave intelligently if you don’t understand intelligence ?
What it intelligence ?
Also that the range of applications will expand over time – at the moment people are better 32

33. Typical problems addressed by KBS
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 33

34. Knowledge Representation
Programming language is a means of representing knowledge
Procedural knowledge
“how to”
Knowledge about how to perform some task
Declarative knowledge
“what is “
Procedural knowledge doesn’t have to be encoded as cryptically as it is in a programming language
Can represent it as a set of rules 34

35. Rule-based reasoning
One can often represent the expertise that someone uses to do an expert task as rules.
A rule means a structure which has an if component and a then component.

36. Other Examples of Rules
if - the leaves are dry, brittle and discoloured
then - the plant has been attacked by red spider mite
if - the customer closes the account
then - delete the customer from the database

37. Rules
The statement, or set of statements, after the word if represents some pattern which you may observe.
The statement, or set of statements, after the word then represents some conclusion that you can draw, or some action that you should take.
IF some condition(s) exists THEN perform some action(s)
IF-THEN
Test-Action
Production rules or just rules
Set of such rules = production system
Test-action if left side holds true produce the right side 37

38. Rule-Based Systems A rule-based system, therefore
identifies a pattern and draws conclusions about what it means OR
identifies a pattern and advises what should be done about it OR
identifies a pattern and takes appropriate action.

39. Rule-based system model
Long Term Memory
Production rule
Short Term Memory
Fact
Interpreter
(Inference engine)
Conclusion
Production System Model
Based on idea that humans solve problems by applying their knowledge(expressed as rules) to a given problem represented by problem-specific information. These facts exist in short term memory
If you put enough information in rule base that it can perform interesting, complex task at same performance level as a human have a rule-based expert system

40. Knowledge Representation
Rules represent
Relations
Recommendations
Directives
Strategies
Rules can represent relations, recommendations, directives, strategies and heuristics
Relation:
IF fuel tank is empty then car is dead
Recommendation:
If the season is autumn
And the sky is cloudy
And the forecast is drizzle
Then the adivce is take an umberella
Directive
If the car is dead
And the fuel tank is empty
Then the action is refuel the car
Strategy
Then the action is check the fuel tank
step 1 is complete
If step1 is complete
Then check the battery
step 2 is complete
Heuristic (rule of thumb)
A heuristic is a short cut decision strategy that leads to a reasonable decision in most cases but may lead to an incredibly bad one
If the spill is liquid
And the spill ph is < 0
And the spill smell is vinegar
Then the spill material is acetic acid 40

41. Knowledge Representation…Relations
IF fuel tank is empty then car is dead.

42. Recommendation If the season is autumn And the sky is cloudy
And the forecast is drizzle
Then the adivce is take an umberella

43. Directive If the car is dead And the fuel tank is empty
Then the action is refuel the car

44. Strategy If the car is dead Then the action is check the fuel tank
step 1 is complete
If step1 is complete
And the fuel tank is empty
Then check the battery
step 2 is complete

45. Class Exercise : Rule-Based System for Tic-Tac-Toe
What rules do we need ?
Rules may have tests that are satisfied at the same time – need some mechanism for selecting right rule 45