1. KNOWLEDGE ACQUISITION METHODS
(TSI 2323 – inc. Introduction to Machine Learning)
Unit 1 – Introduction

2. Group 2 – second years (Cadets)
Comp Science
Sem Total
Year
Sem
Unov
ALK
Short 2 19 21
Wireless Networks Security
Digital Forensics 6
Knowledge Based Systems
(Fuzzy Logic)
Digital Image Processing
Elective 2
Elective 3
Elective 4
Project 2 (PSM) 3
Short Plus 3 3 3 3
Ethics
Industrial Training 12 12 3 3 3 5
Short
ALK 2 4
Project 1 (PSM) 17 21
Information Security Management
Ethical Hackers
Cryptography
Elective 1 4
Knowledge Acquisition Methods
(Machine Learning)
Computational Intelligence
(Intelligent Agents)
Data Mining
Group Project 6 18 24 3
Software Engineering
Data Structures
Artificial Intelligence
Operating Systems
Computer Networks
Computer and Network Security
ALK
Short 1 8 12 20 2
Discrete Mathematics
Object Oriented Programming
Systems Analysis and Design
Web Programming 8 12 20 1
Statistics
Database Systems
Fundamentals of Programming
Computer Organisation and Architecture 28 93
JUMLAH KREDIT
121

3. Knowledge Based Systems (Fuzzy Logic) Digital Image ProcessingAI
Security
Project 2 (PSM)
Knowledge Based Systems
(Fuzzy Logic)
Digital Image Processing
Industrial Training
Project 1 (PSM)
Knowledge Acquisition Methods
(Machine Learning)
Computational Intelligence
(Intelligent Agents)
Data Mining
Group Project
Artificial Intelligence
Programming
Systems
Computer and Network Security
Software Engineering
Web Programming
Object Oriented Programming
Database Systems
Operating Systems
Computer Networks
Data Structures
Systems Analysis and Design
Statistics
Discrete Mathematics
Fundamentals of Programming
Computer Organisation and Architecture

4. Test Represent Acquire Identify Knowledge Acquisition – Essence
DATA
INFORMATION
KNOWLEDGE
WISDOM
IMPLICIT/
TACIT
EXPLICIT
Understanding relations
Understanding patterns
Understanding principles
Knowledge
Engineering
Identify
Represent
Acquire
Test
Domain
Ontology
General
Ontologies
Semantic Networks
Cases
Data
Structured Data (Databases)
Data Input
(usually manually)
Requirements
Analysis
Data Mining /
Machine Learning
RAW
Information literacy also is increasingly important in the contemporary environment of rapid technological change and proliferating information resources. Because of the escalating complexity of this environment, individuals are faced with diverse, abundant information choices – in their academic studies, in the workplace, and in their personal lives. Information is available through libraries, community resources, special interest organizations, media, and the….
Unstructured Data (e.g. text)
Structured Data
(e.g. numbers, vectors)
IMPLICIT/
TACIT

5. Knowledge Acquisition – Essence
DATA
INFORMATION
KNOWLEDGE
WISDOM
IMPLICIT/
TACIT
EXPLICIT
Understanding relations
Understanding patterns
Understanding principles
Have an appreciation of how knowledge is acquired.
Knowledge
Engineering
Identify
Represent
Acquire
Test
Domain
Ontology
General
Ontologies
Semantic Networks
Cases
Know how to (manually) represent knowledge – Ontology-based
This course may appear very complex, but the main points to be learnt are:
RAW
Information literacy also is increasingly important in the contemporary environment of rapid technological change and proliferating information resources. Because of the escalating complexity of this environment, individuals are faced with diverse, abundant information choices – in their academic studies, in the workplace, and in their personal lives. Information is available through libraries, community resources, special interest organizations, media, and the….
Unstructured Data (e.g. text)
Structured Data
(e.g. numbers, vectors)
Attain comfort level with different types of input data.

6. Ontology-Based Knowledge Representation
Representation Target
Ontologies within the Knowledge Representation Fundamentals
(to be given in Chapter 2)

7. Within the Curriculum Structure

8. Curriculum (Cadets) Industrial Training ALK 120 90 12 3 21 22 20 19 1715 30 2 5 7 8
Year 1
Sem
Short 6 4
Unov
Discrete Mathematics
Fundamentals of Programming
Computer Organisation and Architecture
Statistics
Data Structures
Object Oriented Programming
Systems Analysis and Design
Operating Systems
ALK
Software Engineering
Web Programming
Database Systems
Computer Networks
Computer and Network Security
Artificial Intelligence
Computational Intelligence
(Intelligent Agents)
Data Mining
Ethical Hackers
Cryptography
Elective 1
Knowledge Based Systems
(Fuzzy Logic)
Digital Image Processing
Wireless Networks Security
Digital Forensics
Elective 3
Elective 2
Elective 4
Ethics
Industrial Training
Project 1 (PSM)
Group Project
Project 2 (PSM)
Comp Science
Sem Total
JUMLAH KREDIT
Curriculum (Cadets)

9. List of Electives (choose 4 of 6)
Data Mining
Digital Image Processing (DIP)
Knowledge Based System (KBS)
Fuzzy Logic, uncertainty, belief nets, Case based reasoning
Expert Systems
Computational Intelligence (CI)
Intelligent Agents, ant colony algorithm, swarm optimization, constraints handling, genetic algorithm, Neural Networks
Knowledge Acquisition Methods
inc. machine learning, ….
Visualisation & Simulation

10. Knowledge Acquisition Methods
These will be emphasised on
Objectives
Unit 1
Introduction to Knowledge Acquisition
Knowledge Acquisition (KA)
Methods of KA
To describe the methods used for knowledge acquisition and knowledge discovery
To understand data and knowledge representation in knowledge acquisition
To assess the applicability of machine learning algorithms for a specific data mining task
To understand various approaches of knowledge acquisition from experts
Unit 2
Data Input and Knowledge Representation
Types of data input
Knowledge representation
Reasoning
Unit 3
Manual Knowledge Acquisition
Requirements analysis
Knowledge Engineering
Tacit Knowledge
Unit 4
Semi-automated Knowledge Acquisition
From workflow systems
Prototyping
Data Mining
Unit 5
Automated Knowledge Acquisition
Machine Learning: Supervised and unsupervised learning
Natural Language Processing
Image Processing

11. Data Mining and Knowledge Acquisition Methods
Unit 1
Introduction to Data Mining
Lectures
Unit 1
Introduction to Knowledge Acquisition
Lectures
Coursework
Data mining includes concept of knowledge discovery process, data pre-processing and data mining techniques to find hidden pattern of information from large volume of data using selected data mining tools.
Unit 2
Data, Preprocessing and Exploration
Unit 2
Data Input and Knowledge Representation
Unit 3
Data Mining Tools
Unit 3
Manual Knowledge Acquisition
Unit 4
Cluster Detection
Unit 4
Semi automated Knowledge Acquisition
Unit 5
Classification
Unit 5
Automated Knowledge Acquisition
Unit 6
Association Rules
Knowledge acquisition is the process of extracting, structuring and organizing knowledge from human experts and also databases, so it can be used in intelligent systems.
Artificial Intelligence
Introduction to Artificial Intelligence
Heuristic and State Search
Knowledge Representations
Semantic Query
Reasoning
Declarative Programming
AI Applications

12. AI Specialisation - Links
Knowledge Acquisition Methods
Data Mining
Unit 1
Introduction to Data Mining
Unit 1
Introduction to Knowledge Acquisition
Data mining tools - WEKA
Unit 2
Data, Preprocessing and Exploration
Unit 2
Data Input and Knowledge Representation
Unit 3
Data Mining Tools
Unit 3
Manual Knowledge Acquisition
Unit 4
Cluster Detection
Unit 4
Semi automated Knowledge Acquisition
Knowledge Extraction
Unit 5
Classification
Knowledge Interpretation
Unit 5
Automated Knowledge Acquisition
Unit 6
Association Rules

13. Coverage

14. Knowledge Management Framework
Leadership
Apply
Adapt
Create
Organisational Knowledge
Measurement
Culture
Identify
Share
Organise
Acquire
Technology
KNOWLEDGE MANAGEMENT ENABLERS
KNOWLEDGE MANAGEMENT PROCESSES
C. O’Dell & C.J. Grayson If We Only Knew What We Know: Identification and Transfer of Internal Best Practices. Best Practices White Paper, American Productivity & Quality Center, 1997.

15. National R&D Technology Components Development Framework (Mainly AI)
Knowledge Management
Organization
Identification
Creation
Acquisition
Dissemination
Adaptation
Application
Knowledge/ Ontology
Multimedia
Raw Data
Multimedia Knowledge Representation
Knowledge Base Frameworks
Meaning (e.g. Discourse, Lexicontology analysis)
Features (e.g. Phrasal & Dependency analysis)
Generic AI
Processors
Database/XML
Multimedia Knowledge Extraction/
Generation
Text
Speech
Image
Music
Audio
Biosignal
Graphics/Animation
Data/Text Mining
Clustering/ Classification
Multimedia Data Indexing
Focused Information Retrieval
Search
Pattern Identification
Biometrics
Distributed computing
Parallel computing
Grid computing
High Performance Computing
Ubiquitous computing
Universal Multimedia Filter
Data Compression
Multimedia Communication
Data Security
Multimedia Network
protocol
Signal processing
Robotic Navigation
Video/Stereo Vision Informatics
Surveillance
Prediction
Scheduling
Optimization
Creative Multimedia
Medium
Content
User
Environment
Domain specific Applications
Healthcare
Language
Finance
Law
Robotics
Smart office
Smart Home
Smart Meeting Room
Computer Aided Translation
3D Games
Culture & Heritage
Education
3D Film/Movie
Multilingual Translation
Question Answering
Multimodal Visualizer
National R&D Technology Components Development Framework (Mainly AI)
KBS
Simulation & Visualisation
Computational Intelligence
Data Mining AI
Image Proc
Knowledge Acquisition Methods

16. UNIT 1 – Introduction to Knowledge Acquisition
Knowledge Acquisition (KA)
Methods of KA

17. UNIT 1 – Introduction to Knowledge Acquisition
Knowledge Acquisition (KA) includes the elicitation, collection, analysis, modelling and validation of knowledge for the representation of knowledge to be used within intelligent systems
WISDOM
Understanding relations
Understanding patterns
Understanding principles
Knowledge is a combination of information, experience and insight that may benefit the individual or the organisation – e.g. "When crude oil prices go up by $10 per barrel, it's likely that petrol prices will rise by 10 sen per litre" is knowledge.
KNOWLEDGE
Information is data that has been interpreted so that it has meaning for the user – e.g. "The price of crude oil has risen from $70 to $80 per barrel" gives meaning to the data and so is said to be information to someone who tracks oil prices
INFORMATION
Data is unprocessed facts and figures without any added interpretation or analysis – e.g "The price of crude oil is $80 per barrel."
DATA
Data – symbols <history>
Information – answers to "who", "what", "where", and "when" questions <context>
Knowledge (& Understanding) – answers to “how’ and “why” questions <present>
Wisdom – evaluated understanding <future>
IMPLICIT/
TACIT

18. Knowledge Acquisition (KA) is the transfer and transformation of potential problem solving expertise from some knowledge source to a program
KNOWLEDGE
Knowledge Acquisition is a large process itself and is composed of four main stages:
Identification- Identifies the knowledge sources and problem characteristics
Representation
Conceptualisation - Finds concepts to represent the knowledge
Formalisation - Designs the structure to organise the knowledge
Implementation - Formulates rules, frames, etc., to represent the knowledge
Acquisition process – Determines the method to extract the knowledge
Testing - Validates the representation that organises the knowledge
INFORMATION
DATA
IMPLICIT/
TACIT
identify
represent
acquire
test

19. Targeted Representations of Knowledge
Ontologies
Rules
Semantic networks
Frames ……
KNOWLEDGE
Sources of Knowledge (Identification)
Humans (memory)
Implicit (not yet documented)
Tacit (hard, possibly impossible to describe)
Explicit knowledge (raw)
Text
Images
Audio
Video
Multimedia
Partially processed knowledge (Information)
Data bases ….
INFORMATION
DATA
IMPLICIT/
TACIT
identify
represent
acquire
test

20. Acquisition process – How can it be achieved??? Testing Manual
Knowledge Elicitation: Series of interviews between the domain expert and the knowledge engineer who then writes a computer program representing the knowledge
Based on interview
Track reasoning process
Observation
Semi-Automated
Interaction between a domain expert and a computer program
Automated methods (see below) aided by human experts
Build base with minimal help from knowledge engineer
Allows execution of routine tasks with minimal expert input
Automated
Analysis technologies (language, speech, image processing, …)
Data-driven techniques (computer programs that create associations using a large body of case data)
KNOWLEDGE
INFORMATION
DATA
IMPLICIT/
TACIT
Testing
Surveys ……
identify
represent
ACQUIRE
test

21. One of the most basic forms of Knowledge Acquisition is Requirements Analysis
[as in Systems Analysis and Design]
Requirements analysis in systems engineering and software engineering, encompasses those tasks that go into determining the needs or conditions to meet for a new or altered product, taking account of the possibly conflicting requirements of the various stakeholders, analysing, documenting, validating and managing software or system requirements
KNOWLEDGE
INFORMATION
DATA
IMPLICIT/
TACIT

22. Knowledge Acquisition (basic)
EXPLICIT
WISDOM
KNOWLEDGE
Data
Structured Data (Databases)
Data Input
(usually manually)
Requirements
Analysis
INFORMATION
DATA
Information literacy also is increasingly important in the contemporary environment of rapid technological change and proliferating information resources. Because of the escalating complexity of this environment, individuals are faced with diverse, abundant information choices – in their academic studies, in the workplace, and in their personal lives. Information is available through libraries, community resources, special interest organizations, media, and the….
Unstructured Data (e.g. text)
IMPLICIT/
TACIT
Unstructured Data
(e.g. numbers)
RAW
IMPLICIT/
TACIT

23. Unstructured Data (e.g. text)
Data – symbols
Information – answers to "who", "what", "where", and "when" questions
Knowledge (& Understanding) – answers to “how’ and “why” questions
Wisdom – evaluated understanding
Knowledge answers to “how’ and “why” questions, which requires reasoning, and as such information has to be further processed to allow for this, hence requiring more complex representations. This processing can be manual, semi-automated or automated.
Domain
Ontology
General
Ontologies
Semantic Networks
Cases
KNOWLEDGE
Information literacy also is increasingly important in the contemporary environment of rapid technological change and proliferating information resources. Because of the escalating complexity of this environment, individuals are faced with diverse, abundant information choices – in their academic studies, in the workplace, and in their personal lives. Information is available through libraries, community resources, special interest organizations, media, and the….
Unstructured Data (e.g. text)
Unstructured Data
(e.g. numbers)
Data
Structured Data (Databases)
INFORMATION
DATA
IMPLICIT/
TACIT

24. Knowledge Acquisition Methods
Objectives
Unit 1
Introduction to Knowledge Acquisition
Knowledge Acquisition (KA)
Methods of KA
To describe the methods used for knowledge acquisition and knowledge discovery
To understand data and knowledge representation in knowledge acquisition
To assess the applicability of machine learning algorithms for a specific data mining task
To understand various approaches of knowledge acquisition from experts
Unit 2
Data Input and Knowledge Representation
Types of data input
Knowledge representation
Reasoning
Unit 3
Manual Knowledge Acquisition
Requirements analysis
Knowledge Engineering
Tacit Knowledge
Unit 4
Semi-automated Knowledge Acquisition
From workflow systems
Prototyping
Data Mining
Unit 5
Automated Knowledge Acquisition
Machine Learning: Supervised and unsupervised learning
Natural Language Processing
Image Processing

25. Knowledge Acquisition thus includes:
EXPLICIT
Knowledge Acquisition thus includes:
Knowledge Engineering (more advanced)
Requirements Analysis (basic)
WISDOM
Domain
Ontology
General
Ontologies
Semantic Networks
Cases
Knowledge
Engineering
KNOWLEDGE
Data
Structured Data (Databases)
Data Input
(usually manually)
INFORMATION
DATA
Information literacy also is increasingly important in the contemporary environment of rapid technological change and proliferating information resources. Because of the escalating complexity of this environment, individuals are faced with diverse, abundant information choices – in their academic studies, in the workplace, and in their personal lives. Information is available through libraries, community resources, special interest organizations, media, and the….
Unstructured Data (e.g. text)
IMPLICIT/
TACIT
Unstructured Data
(e.g. numbers)
RAW
IMPLICIT/
TACIT

26. Unstructured Data (e.g. text)
Complex
Simpler
Intelligent Programmable Integrated Controller(PIC)
Reinforced Learning
Hidden Markov Models
Multilayer Perceptrom
Decision Trees
Clustering
Multivariate Methods
Parametric Methods
Bayesian Decision Theory
Machine Learning
EXPLICIT
Knowledge Acquisition also includes:
Machine Learning (more advanced)
WISDOM
Semantic Networks
General
Ontology
Ontologies
Domain
Ontology
KNOWLEDGE
Cases
Structured Data (Databases)
Data
INFORMATION
Data
Data Input
(usually manually)
Data Mining /
Machine Learning
DATA
Information literacy also is increasingly important in the contemporary environment of rapid technological change and proliferating information resources. Because of the escalating complexity of this environment, individuals are faced with diverse, abundant information choices – in their academic studies, in the workplace, and in their personal lives. Information is available through libraries, community resources, special interest organizations, media, and the….
Unstructured Data (e.g. text)
Unstructured Data
(e.g. numbers, vectors)
RAW
IMPLICIT/
TACIT

27. Knowledge Acquisition also includes:
STATISTICAL METHODS
DATA MINING
Processes
QUERY
INFORMATION RETRIEVAL
DECLARATION
COMPLEX
SIMPLE
Balance of Complexity of Processes vs Data
COMPLEX
Knowledge Acquisition also includes:
Language Processing (more advanced)
…. Some balance is also required …
WISDOM
RAW
Annotated
Morphology
Structural
Logical
Semantics
Discourse
Dependencies
Semantic Networks
KNOWLEDGE
Ontologies
Cases
Structured Data (Databases)
INFORMATION
DATA
Information literacy also is increasingly important in the contemporary environment of rapid technological change and proliferating information resources. Because of the escalating complexity of this environment, individuals are faced with diverse, abundant information choices – in their academic studies, in the workplace, and in their personal lives. Information is available through libraries, community resources, special interest organizations, media, and the….
Unstructured Data (e.g. text)
Unstructured Data
(e.g. numbers)
RAW

28. Summary & Generalities

29. Knowledge Acquisition is a large process: Identification
UNIT 1 – Summary
Knowledge Acquisition is extracting and representing knowledge within the given spectrum
WISDOM
Understanding relations
Understanding patterns
Understanding principles
Data – symbols <history>
Information – answers to "who", "what", "where", and "when" questions <context>
Knowledge (& Understanding) – answers to “how’ and “why” questions <present>
Wisdom – evaluated understanding <future>
KNOWLEDGE
Knowledge Acquisition is a large process:
Identification
Representation
Acquisition process
Testing
INFORMATION
Sources (Identification)
Humans (memory)
Explicit knowledge (raw)
Partially processed knowledge (Information)
Representations of Knowledge
Ontologies
Rules
Semantic networks
Frames ……
DATA
Knowledge Acquisition Process
Manual
Semi-Automated
Automated
Techniques/ Methodologies
Knowledge Engineering
Machine Learning
Natural Language Processing ……
IMPLICIT/
TACIT

30. Test Represent Acquire Identify Knowledge Acquisition – Essence
DATA
INFORMATION
KNOWLEDGE
WISDOM
IMPLICIT/
TACIT
EXPLICIT
Understanding relations
Understanding patterns
Understanding principles
Knowledge
Engineering
Identify
Represent
Acquire
Test
Domain
Ontology
General
Ontologies
Semantic Networks
Cases
Data
Structured Data (Databases)
Data Input
(usually manually)
Requirements
Analysis
Data Mining /
Machine Learning
RAW
Information literacy also is increasingly important in the contemporary environment of rapid technological change and proliferating information resources. Because of the escalating complexity of this environment, individuals are faced with diverse, abundant information choices – in their academic studies, in the workplace, and in their personal lives. Information is available through libraries, community resources, special interest organizations, media, and the….
Unstructured Data (e.g. text)
Structured Data
(e.g. numbers, vectors)
IMPLICIT/
TACIT

31. Knowledge Acquisition – Essence
DATA
INFORMATION
KNOWLEDGE
WISDOM
IMPLICIT/
TACIT
EXPLICIT
Understanding relations
Understanding patterns
Understanding principles
This course may appear very complex, but the main points to be learnt are:
RAW
Information literacy also is increasingly important in the contemporary environment of rapid technological change and proliferating information resources. Because of the escalating complexity of this environment, individuals are faced with diverse, abundant information choices – in their academic studies, in the workplace, and in their personal lives. Information is available through libraries, community resources, special interest organizations, media, and the….
Unstructured Data (e.g. text)
Structured Data
(e.g. numbers, vectors)
Attain comfort level with different types of input data.
Domain
Ontology
General
Ontologies
Semantic Networks
Cases
Know how to (manually) represent knowledge – Ontology-based
Have an appreciation of how knowledge is acquired.
Knowledge
Engineering
Identify
Represent
Acquire
Test

32. ULTIMATE – Upgrading Database or Information Systems Applications
The ultimate in Knowledge Acquisition is that knowledge can be automatically collected (e.g. from texts, images, audio, video, …), analysed, modelled, validated and then built into intelligent systems ….
Data
TYPICAL APPLICATIONS
Information literacy also is increasingly important in the contemporary environment of rapid technological change and proliferating information resources. Because of the escalating complexity of this environment, individuals are faced with diverse, abundant information choices – in their academic studies, in the workplace, and in their personal lives. Information is available through libraries, community resources, special interest organizations, media, and the….
Unstructured Data (e.g. text)
Structured Data (Databases)
Data Input
(usually manually)

33. ULTIMATE – Knowledge Application Builder  Knowledge Applications
Ontologies
Domain
Ontology
General
Knowledge Base
Automated
Information literacy also is increasingly important in the contemporary environment of rapid technological change and proliferating information resources. Because of the escalating complexity of this environment, individuals are faced with diverse, abundant information choices – in their academic studies, in the workplace, and in their personal lives. Information is available through libraries, community resources, special interest organizations, media, and the….
Unstructured Data (e.g. text)
Structured Data (Databases)
Knowledge Application Builder
Knowledge Management Engines
ULTIMATE – Knowledge Application Builder  Knowledge Applications

34. Knowledge Representation Fundamentals
UNIT 2 – Summary
Knowledge Modelling
Knowledge TYPES
Needed Usual
Declarative Procedural
Tacit Explicit
Generic Specific
Meta-knowledge
Knowledge OBJECTS General Specific
Concepts Instances
Processes
Attributes Values
Relationships Rules
Knowledge OBJECTS ++
Description
Situational Relations
Knowledge Representation Fundamentals
Formalisms
Ontologies
Others
Rules
Semantic Networks
Frames ….
Reasoning
Inheritance
Chaining (Rules)
…..

35. THANK YOU
MERCI
GRAZZIE
GRACIAS
SPASIBA
DANKE
MANGE TAK
NAN DHRI
CAM ON
TERIMA KASIH
ARIGATO/ OKINI
GO MA SSEUM NI DA
SHUKRIYA
XIE-XIE NI
KAMSIAH / MMKOI
JABAI INAU
NGGO BUTE KABU
KOP KUN KAH