vii
TABLE OF CONTENTS
CHAPTER
1
TITLE
PAGE
DECLARATION
ii
DEDICATION
iii
ACKNOWLEDGEMENTS
iv
ABSTRACT
v
ABSTRAK
vi
TABLE OF CONTENTS
vii
LIST OF TABLES
x
LIST OF FIGURES
xii
LIST OF ABBREVIATIONS
xiv
INTRODUCTION
1
1.1
Background
1
1.2
Current Method in Formalizing Knowledge
3
1.3
Challenges of Formalizing Knowledge
4
1.4
Statement of Problem
6
1.5
Objectives of the Study
7
1.6
Significance and Scope of the Study
7
1.7
Organization of the Thesis
9
viii
2
3
LITERATURE REVIEW
10
2.1
Introduction
10
2.2
Product Manufacturing Requirements
11
2.3
2D CAD Engineering Drawing and Product Structure
14
2.4
Extraction Algorithm
18
2.5
Knowledge Formalization
20
2.6
Ontology
26
2.7
Semantic Similarity Measures
29
2.8
Trend and Tendencies
31
2.9
Summary
32
RESEARCH METHODOLOGY
33
3.1
Introduction
33
3.2
The Framework of the Study
34
3.3
Data Sources
39
3.4
Instrumentation and Result Analysis
41
3.4.1
Hardware and Software Requirements
41
3.4.2
Case Study and Implementation
41
3.4.3
Performance Measurement
42
3.5
4
Summary
THE DEVELOPMENT AND DESIGN OF PRODUCT
43
44
STRUCTURE ONTOLOGY
4.1
Introduction
44
4.2
Product Structure Ontology (PSO)
48
4.3
Features of PSO
54
4.4
The Proposed PSO
56
4.5
PSO Anatomy and Schema
59
4.6
PSO Annotation
61
4.7
PSO Evidence
64
ix
4.8
4.9
5
The Shared PSO Resource
65
4.8.1
The PSO Website
65
4.8.2
The PSO Browsers
66
4.8.3
The PSO Database
67
4.8.4
The PSO Documentation
67
Summary
SEMANTIC ONTOLOGY-BASED SEARCHING
68
69
ALGORITHM
5.1
Introduction
69
5.2
Semantic Similarity Searching Algorithm (SSSA)
71
5.2.1
Information Content Approach
71
5.2.2
Semantic Ontology-Based Searching Algorithm
73
(SOBSA)
6
5.3
The PSO-EDEx Framework
76
5.4
Statistical Measurement
78
5.5
Results and Discussion
79
5.6
Summary
104
CONCLUSION
105
6.1
Concluding Remarks
105
6.2
Contributions
108
6.3
Future Works
109
6.4
Summary
110
REFERENCES
111
x
LIST OF TABLES
TABLE NO.
2.1
TITLE
A table of crosses represents a very simple and often
PAGE
21
used data type
3.1
The features comparison between PSO, MASON and
40
PRONTO
4.1
Attributes of PSO terms
62
4.2
Size of PSO data
68
5.1
The comparison of the query results between semantic
81
ontology-based and keyword-based searching using the
standard information retrieval measures there are recall
(R), precision (P) and F1
5.2
The evaluation of precision (P), recall (R) and F1 of the
84
query results that have been retrieved using 50% PSO
5.3
The evaluation of precision (P), recall (R) and F1 of the
85
query results that have been retrieved using 75% PSO
5.4
The evaluation of recall, precision and F1 of the query
86
results that have been retrieved using 75% PSO
5.5
The precision values of the query results that have been
91
retrieved using 50%, 75% and 100% of PSO at standard
recall
5.6
The evaluation of recall, precision and F1 of the query
results that have been retrieved using subsumption
relationship
93
xi
5.7
The evaluation of recall, precision and F1 of the query
93
results that have been retrieved using meronymy
relationship
5.8
The evaluation of recall, precision and F1 of the query
94
results that have been retrieved using association
relationship
5.9
The evaluation of recall, precision and F1 of the query
94
results that have been retrieved using combination of
subsumption and meronymy relationships
5.10
The evaluation of recall, precision and F1 of the query
95
results that have been retrieved using combination of
subsumption and association relationships
5.11
The evaluation of recall, precision and F1 for the query
95
results that have been retrieved using combination of
meronymy and association relationships
5.12
The evaluation of recall, precision and F1 for the query
96
results that have been retrieved using combination of
subsumption, meronymy and association relationships
5.13
Variance of recall for each types of relationship(s) used
98
5.14
Variance of precision for each types of relationship(s)
99
used
5.15
Variance of F1 for each types of relationship(s) used
100
5.16
Statistical significance of differences between
101
relationship(s) for recall
5.17
Statistical significance of differences between
102
relationship(s) for precision
5.18
Statistical significance of differences between
relationship(s) for F1
103
xii
LIST OF FIGURES
FIGURE NO.
2.1
TITLE
Screenshot of the FuturePlan Requirements Management
PAGE
13
Software
2.2
2D CAD engineering drawing
15
2.3
AutoCAD software screenshot
17
2.4
Example of concept lattice. The information contained in
22
the formal context is preserved. The concept lattice is the
basic for further data analysis.
2.5
Example of semantic networks visualized
23
2.6
Example of ontology visualized
25
2.7
Protégé ontology editor screenshot
28
3.1
The overview of the study framework
37
4.1
The framework adopted to develop the PSO
50
4.2
PSO design in Protégé-owl editor
52
4.3
The PSO in DAG representation
53
4.4
The fragment of PSO classes and individuals
55
4.5
Overview of the PSO’s classes, individuals and
56
relationships
4.6
Part of “Product Specific Knowledge”
57
4.7
Part of “Industry Specific Knowledge”
58
4.8
Part of “Generic-term”
59
4.9
Anatomy of the PSO terms
60
4.10
Database structure scheme
63
xiii
4.11
Screenshot of the PSO web site
66
4.12
Screenshot of the PSO browser
67
5.1
The semantic ontology-based searching algorithm
74
5.2
An example of semantic similarity score results for the
75
end trim query engineering term
5.3
The flowchart of the PSO-EDEx framework
77
5.4
The A (BC) is the shaded region
79
5.5
The recall of the query results for the ontology-based
82
semantic searching and keyword-based searching using
50 sheets digital engineering drawing
5.6
The precision of the query results for the ontology-based
82
semantic searching and keyword-based searching using
50 sheets digital engineering drawing
5.7
The F1 of the query results for the ontology-based
83
semantic searching and keyword-based searching using
50 sheets digital engineering drawing
5.8
Precision-recall values for representative the
87
effectiveness of SOBSA using 50% of PSO
5.9
Precision-recall values for representative the
87
effectiveness of SOBSA using 75% of PSO
5.10
Precision-recall values for representative the
88
effectiveness of SOBSA using 100% of PSO
5.11
The average of interpolated precision-recall for different
92
percentage of PSO
5.12
The average F1 for 5 query results that have been
92
retrieved using different percentage of PSO
5.13
The average of the query results that have been retrieved
using different types of relationship.
97
xiv
LIST OF ABBREVIATIONS
2D
-
Two-Dimensional
3D
-
Three-Dimensional
A&RM
-
Automated and Robotised Manufacturing
AI
-
Artificial Intelligence
ALPS
-
A Language for Process Specification
APMSSB
-
APM Springs Sdn Bhd
BOM
-
Bill-of-Material
CAD
-
Computer Aided Design
CAM
-
Computer-Aided Manufacturing
CCO
-
Cell-Cycle Ontology
CPU
-
Central Processing Unit
DAG
-
Directed Acyclic Graph
DAML+OIL
-
DARPA Agent Markup Language + Ontology Interchange
Language
DOLCE
-
Descriptive Ontology for Linguistic and Cognitive
Engineering
DXF
-
Drawing Exchange Format
EDEx
-
Engineering Drawing Extraction
EF
-
Experience Feedback
ERP
-
Enterprise Resource Planning
FCA
-
Formal Concept Analysis
FMA
-
Foundational Model of Anatomy
GO
-
Gene Ontology
GOC
-
Gene Ontology Consortium
xv
GOLD
-
General Ontology for Linguistic Description
I/O
-
Input/Output
IDE
-
Inferred By Domain Expert Statement
IFRM
-
Intelligent Feature Recognition Methodology
IGES
-
Initial Graphics Exchange Specification
ITD
-
Inferred From Technical Documentation
JSP
-
Java Server Pages
KIF
-
Knowledge Interchange Format
KL-ONE
-
Knowledge Language One
MASON
-
Manufacturing’s Semantic Ontology
MGI
-
Mouse Genome Informatics
MLE
-
Maximum Likelihood Estimation
MPS
-
Master Production Schedule
MRD
-
Market Requirements Document
MRMS
-
Manufacturing Requirement Management System
MRP
-
Material Requirements Planning
NAS
-
Non-Traceable Author Statement
ND
-
No Engineering Data Available
NIC
-
Network Interface Card
NIST
-
National Institute of Standards and Technology
OCML
-
Operational Conceptual Modelling Language
OFS
-
Ontological Filtering System
OWL
-
Web Ontology Language
PC
-
Personal Computer
PDF
-
Portable Document Format
PRONTO
-
Product Ontology
PSL
-
Process Specification Language
PSO
-
Product Structure Ontology
RAM
-
Random Access Memory
RDBMS
-
Relational Database Management System
RDFS
-
Resource Description Framework Schemas
SGD
-
Saccharomyces Genome Database
SOBSA
-
Semantic Ontology-based searching Algorithm
xvi
SSSA
-
Semantic Similarity Searching Algorithm
SWEET
-
Semantic Web for Earth and Environmental
Terminology
TAS
-
Traceable Author Statement
UOM
-
Unit of Measures
WAVE
-
Web Analysis and Visualization Environment (WAVE)
WIP
-
Work in Process
WWW
-
World Wide Web
XML
-
Extensible Markup Language