1. Intro. To Knowledge Engineering
“Knowledge Engineering is the process of developing knowledge based systems in any field, whether it be in the public or private sector, in commerce or industry”

2. Intro. To Knowledge Engineering
Data
“The fundamental, indivisible objects within an application”
Information
“The implicit functional associations between data in the application”
Knowledge
“The explicit functional associations between items of information and/or data”
( J.K. Debenham(1988) Knowledge Systems design, Prentice Hall )

3. Intro. To Knowledge Engineering
Concepts
Example
If temperature is 5 C it feels cold
Value
Knowledge
The temperature outside is 5 C
Information
Data 5
Facts and Figures

4. Intro. To Knowledge Engineering
Tasks of a Knowledge Engineer
Extracting knowledge from people
Representing it in some form
Including it in a computer program which makes use of that knowledge
Validating the software system produced

5. Intro. To Knowledge Engineering
A Knowledge Engineer must -
Be bound by a professional code of conduct
Update their knowledge and skills
Adhere to rules, regulations and legal requirements

6. Intro. To Knowledge Engineering
Knowledge Acquisition
Knowledge Representation
Software Design
Implementation

7. Intro. To Knowledge Engineering
Results of a survey undertaken in the U.K. in 1994
The 7 Most Important Skills for a Knowledge Engineer

8. Intro. To Knowledge Engineering
Human Behaviour
Can adapt in time and evolve
Navigation
Visual Recognition
Avoid Danger
Speech
Use Basic Tools
Simple Problem Solving
Mimic Humans
Build Mental Models
Learn from being told
Learn from the past
Teach
Solve complex problems
Design, plan and schedule
Create complex abstract models

9. Intro. To Knowledge Engineering
Expert Systems
Model higher order cognitive functions of the human mind
Can be used to mimic decision making processes
Applications include -
Planning
Scheduling and
Diagnostics systems

10. Intro. To Knowledge Engineering
Neural Networks
Model the brain at a biological level
Are adept at pattern recognition
Can learn to read
Can recognise patterns from experience
Can be used to predict future trends

11. Intro. To Knowledge Engineering
Case Based Reasoning Systems
Model the human ability to learn from past experience
Examples include -
Legal cases