Dickson Lukose Rob Kremer


Knowledge Representation

Dr Dickson Lukose
Department of Mathematics, Statistics and Computer Science
The University of New England
Armidale, N.S.W., 2350
Email: ke@neumann.une.edu.au
Tel: (067) 73 2302
Fax: (067) 73 3312
Dr Rob Kremer
Department of Computer Science
The University of Calgary
Calgary, Alberta, T2N 1N4
Email: kremer@cpsc.ucalgary.ca
Tel: (403) 220-5112
Fax: (403) 284-4707


Knowledge Engineering is the technique applied by knowledge engineers to build intelligent systems: Expert Systems, Knowledge Based Systems, Knowledge based Decision Support Systems, Expert Database Systems, etc. There are two main view to knowledge engineering. The traditional view is known as "Transfer View". In this view, the assumption is to apply conventional knowledge engineering techniques to transfer human knowledge into artificial intelligent systems. The alternative view is known as the "Modelling View". In this view, the knowledge engineer attempts to model the knowledge and problem solving techniques of the domain expert into the artificial intelligent system.

The view studies in this Knowledge Engineering topic is the "Modelling View". To effectively practice knowledge engineering, a knowledge engineer require knowledge in two main areas. They are: Knowledge Representation; and Knowledge Modelling. The knowledge representation scheme studied is Conceptual Structure (Sowa, 1984), and the Knowledge Modelling techniques studied is the KADS (Schreiber, Wielinga, and Breuker, 1993). Thus, the study in Knowledge Engineering is divided into 2 major parts. They are:

Part A: Conceptual Structure
Part B: Knowledge Modelling

Dr Dickson Lukose
Department of Mathematics,
Statistics, and Computer Science
UNE, Armidale. 


Lecture 1. Philosophical Basis 1.1. Introduction
1.2. Knowledge and Models
1.3. Psychological Issues
1.4. Linguistic Issues
1.5. Intensions and Extensions
1.6. Primitives and Prototypes
1.7. Symbolic Logic and Common Sense
1.8. Artificial Intelligence
Lecture 2. Psychological Evidence 2.1. Introduction
2.2. Percepts
2.3. Mechanisms of Perception
2.4. Conceptual Encoding
2.5. Schemata
2.6. Working Registers
2.7. Recognition and Recall
Lecture 3. Conceptual Graphs 3.1. Conceptual Graphs
3.2. Percepts and Concepts
3.3. Semantic Networks
Lecture 4. Conceptual Graphs  4.1. Individuals and Names
4.2. Canonical Graphs
Lecture 5. Conceptual Graphs 

Lecture 6. Conceptual Graphs 

6.1. Abstraction and Definition
6.2. Aggregation and Individuation
Lecture 7. Reasoning and Computation 7.1. Schemata and Prototype Lecture 8. Reasoning and Computation 8.1. Symbolic Logic
8.2. Propositional Calculus
8.3. Predicate Calculus
8.4. Existential Graphs
8.5. Peirce's Alpha Rules for Propositional Calculus
8.6. Peirce's Beta Rules for Predicate Calculus

Dickson Lukose &.  Rob Kremer.  KNOWLEDGE ENGINEERING, PART A: Knowledge Representation.  July 1996.