Ontology-driven Semi-supervised Model for Conceptual Analysis

89 Pages·2014·2.14 MB·English

ONTOLOGY-DRIVEN SEMI-SUPERVISED MODEL
FOR CONCEPTUAL ANALYSIS OF DESIGN
SPECIFICATIONS
BY
ARUNPRASATH SHANKAR
Submitted in partial fulfillment of the requirements
For the degree of Master of Science
Thesis Advisor: Dr. Christos Papachristou
Department of Electrical Engineering and Computer Science
CASE WESTERN RESERVE UNIVERSITY
2014
August
CASE WESTERN RESERVE UNIVERSITY
SCHOOL OF GRADUATE STUDIES
we hereby approve the thesis of
ARUNPRASATH SHANKAR
candidate for the MASTER OF SCIENCE degree.
chair of the committee
DR. CHRISTOS PAPACHRISTOU
DR. FRANCIS MERAT
DR. FRANCIS WOLFF
date
15 2014
MAY ,
∗Wealsocertifythatwrittenapprovalhasbeenobtainedforanyproprietaryma-
terial contained therein.
“And, when you want something, all the universe conspires in helping you to
achieve it.”
— Paulo Coelho, The Alchemist
Dedicated to my family and friends.
CONTENTS
1 introduction 1
2 background and related work 7
21 7
. Information Extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
22 9
. Ontology Generation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
23 12
. Component Retrieval . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3 specification mining 14
31 14
. The Proposed Model: NEFCIS . . . . . . . . . . . . . . . . . . . . . . . . .
32 15
. Preprocessing and Frequency Analysis . . . . . . . . . . . . . . . . . . . .
33 16
. Attribute Clustering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
331 17
. . Forward Chaining . . . . . . . . . . . . . . . . . . . . . . . . . . .
332 19
. . Seed Template . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
333 20
. . Spec Rules . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
34 24
. Fuzzy Sets generation . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
35 26
. Estimating Priority indices . . . . . . . . . . . . . . . . . . . . . . . . . . .
36 28
. Estimation of Fuzzy Parameter Scores . . . . . . . . . . . . . . . . . . . .
37 32
. Estimation of Feature Proximity Scores . . . . . . . . . . . . . . . . . . .
38 35
. Fuzzy Concept Based Information Retrieval . . . . . . . . . . . . . . . . .
4 conceptual analysis 39
41 39
. Candidate Entity Extraction . . . . . . . . . . . . . . . . . . . . . . . . . .
42 41
. Relation Mining . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
43 42
. Conceptualization Process . . . . . . . . . . . . . . . . . . . . . . . . . . .
44 45
. Ontology Building Process . . . . . . . . . . . . . . . . . . . . . . . . . . .
5 component retrieval 47
51 47
. Estimation of Spec Coverage . . . . . . . . . . . . . . . . . . . . . . . . . .
iv
contents
v
52 56
. Bridging SPARQL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6 experimental results 59
7 conclusion and future work 65
appendix 66
i
a fuzzy matching implementation 67
b uag to dag algorithm implementation 71
bibliography 74
LIST OF FIGURES
1 15
Figure NEFCIS: Neuro-Fuzzy Concept based Inference System . . . . .
2 18
Figure Forward Chaining . . . . . . . . . . . . . . . . . . . . . . . . . . .
3 18
Figure Pattern Matching . . . . . . . . . . . . . . . . . . . . . . . . . . .
4 20
Figure Entity Segregation . . . . . . . . . . . . . . . . . . . . . . . . . . .
5 21
Figure N-grams Generation . . . . . . . . . . . . . . . . . . . . . . . . . .
6 23
Figure Attributes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
7 23
Figure Attribute Discovery . . . . . . . . . . . . . . . . . . . . . . . . . .
8 25
Figure Word Clusters before Fuzzification . . . . . . . . . . . . . . . . .
9 26
Figure Word Clusters after Fuzzification . . . . . . . . . . . . . . . . . .
10 27
Figure NEFCIS: Fuzzy Sets Generation . . . . . . . . . . . . . . . . . . .
11 28
Figure NEFCIS: Prioritization of keywords . . . . . . . . . . . . . . . . .
12 40
Figure Specification Analysis System . . . . . . . . . . . . . . . . . . . .
13 42
Figure Concept UAG Representation . . . . . . . . . . . . . . . . . . . .
14 43
Figure Example Concept UAG . . . . . . . . . . . . . . . . . . . . . . . .
15 43
Figure Example Concept DAG . . . . . . . . . . . . . . . . . . . . . . . .
16 50
Figure Spec to Spec mapping using multiple domain ontologies . . . .
17 52
Figure Component Retrieval . . . . . . . . . . . . . . . . . . . . . . . . .
18 56
Figure Source Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
19 57
Figure Target Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
20 57
Figure Generated RDF Triples . . . . . . . . . . . . . . . . . . . . . . . .
21 58
Figure Ontology FPU . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
22 59
Figure NEFCIS Precision across Design Domains. . . . . . . . . . . . . .
23 60
Figure Comparative Analysis based on retrieval performance metrics. .
24 61
Figure Precision Vs Recall . . . . . . . . . . . . . . . . . . . . . . . . . .
vi
List of Figures vii
25 61
Figure Iteration: Saturation Point . . . . . . . . . . . . . . . . . . . . . .
26 62
Figure Ontology Construction: Statistics of Generated Suggestions . . .
27 63
Figure OWL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
LIST OF TABLES
1 45
Table UAG to DAG transformation . . . . . . . . . . . . . . . . . . . .
2 63
Table Spec Analysis using FPU Domain Ontology . . . . . . . . . . . .
viii
ACRONYMS
SoC System on Chip
NEFCIS Neuro-fuzzy Concept based Inference System for Specification Mining
URI Universal Resource Identifier
RDF Resource Data Finder
UAG Undirected Acyclic Graph
DAG Directed Acyclic Graph
OWL Web Ontology Language
RDF Resource Description Framework
ix
ACKNOWLEDGEMENTS
Firstandforemost,IwouldliketothankmyadvisorDr.ChristosPapachristouforhis
great patience and careful guidance through the past three years. He is a knowledge-
able person with great passion on everything he works on. It has been a pleasure
to meet with him weekly, having discussions on either research or life. He always
granted me enough flexibility on scheduling my time, as well as gave me insightful
advices on difficulties I encountered. I appreciate these a lot and have learned how
to treat other people the same way. It has been great fun to listen to and discuss
artificial intelligence and other interesting topics with him.
I am thankful to the two people with whom I worked on this project, Dr. Francis
Wolff and Bhanu Singh. This work would not have been possible without invaluable
help and suggestions from them.
A special gratitude and love goes to my family for their unfailing support. I thank
my parents for their abiding love. Lastly, I would like to thank all my friends, with-
out them I would not have gotten a chance to enjoy these wonderful three years at
Case Western!
x