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Intro. To Knowledge Engineering

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Presentation on theme: "Intro. To Knowledge Engineering"— Presentation transcript:

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

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