Nowadays, personalization plays a crucial role in many software applications and in commercial advertisements. Different people have different views towards the world they perceive. By personalization, we can achieve a higher chance that people obtain the results they expect, which may further lead to improved user experience. Moreover, personalization also serves as an important basis for advertisements. Advertisement tailored to meet personal preference increases the likelihood that the target product or service attracts the attention of the target audiences, and therefore, may result in a higher product profit.
The goal of this thesis is to create a system that generates users’ Personal Ontologies based on their personal data, for example the history of their log queries and results selected after submitting these queries. These ontologies are used for refining users’ queries by keywords the users are interested in and that are semantically related to those queries. A graphical editor is also designed for viewing, editing and visualizing these ontologies. The visualization transforms the ontology into a graph. Multiple layout algorithms provided by Gephi [1] are supported. The editor can also be used for testing the query refinement.
This research is focused on three important aspects: ontology modeling, construction and evolution. Ontology modeling describes the model used in this approach and its notation. Ontology construction focuses on the way how the information is retrieved and saved in the ontology. Ontology evolution allows for changes in users’ preferences and reflects these changes in their ontologies.
The innovative approach to query refinement process is based on unique formulas, which use not only TF/IDF, but also employ time decay and word similarity metric so that the real meaning of the words contained in the ontology is taken into account. The thesis also shows strengths and abilities of the solution in several scenarios.

ACKNOWLEDGMENTS I
ABSTRACT II
LIST OF FIGURES VII
LIST OF TABLES X
LIST OF EQUATIONS XII
1 INTRODUCTION 1
1.1 PERSONAL ONTOLOGY 1
2 RELATED WORK 3
3 ONTOLOGY MODELING 5
3.1 OWL BASICS 6
3.1.1 Class 6
3.1.2 Individuals 7
3.1.3 Properties 8
3.2 PERSONAL ONTOLOGY MODEL 8
3.2.1 Document 9
3.2.2 Keyword 10
3.2.3 Keyword Instance 10
3.2.4 Ontology Info 11
3.2.5 Similarity 12
3.2.6 Stem 14
3.2.7 Overall Ontology model 16
4 ONTOLOGY CONSTRUCTION 18
4.1 INFORMATION RETRIEVAL 18
4.1.1 WSDL Parser 18
4.1.2 Tokenization 19
4.1.3 Stop words removal 21
4.1.4 Stemming 21
4.2 SIMILARITY CALCULATION 22
4.2.1 Values used in word similarity calculation 22
4.2.2 Hirst and St-Onge 23
4.2.3 Leacock-Chodorow 24
4.2.4 Resnick 24
4.2.5 Jiang-Conrath 25
4.2.6 Lin 25
4.2.7 Comparison of the similarity metrics 25
4.2.8 Similarity metric used in Personal Ontology 26
4.2.9 Normalized Leacock &; Chodorow 27
4.3 ONTOLOGY UPDATE 27
4.3.1 Local Transitive Frequency 29
5 ONTOLOGY EVOLUTION 30
5.1 TERM FREQUENCY / INVERSE DOCUMENT FREQUENCY (TF/IDF) 30
5.2 TIME DECAY 31
6 QUERY REFINEMENT 33
6.1 INFORMATION RETRIEVAL 33
6.2 KEYWORD RANKS CALCULATION 34
6.3 DOCUMENT SCORES CALCULATIONS 35
6.4 KEYWORD EXTRACTION 36
7 PERSONAL ONTOLOGY EDITOR 38
7.1 EDITOR TAB 38
7.2 VISUALIZATION TAB 39
7.3 QUERY REFINEMENT TAB 40
8 WHAT MAKES THE ONTOLOGY “PERSONAL”: SCENARIOS 42
8.1 HOW THE “PERSONALIZATION” IS ACHIEVED 42
8.2 PERSONALIZING SEARCH RESULTS 43
8.3 ONTOLOGY EVOLUTION 45
8.4 ADVANTAGES OF LOCAL TRANSITIVE FREQUENCY 49
9 DESIGN AND IMPLEMENTATION 53
9.1 REQUIREMENTS 53
9.1.1 Functional Requirements 53
9.1.2 Interface Requirements 63
9.2 SYSTEM ARCHITECTURE 67
9.3 DESIGN 68
9.3.1 Personal Ontology 68
9.3.2 Personal Ontology Editor 71
9.4 DESIGN ISSUES 71
9.5 REDESIGN 73
9.6 UNIT TESTING AND TEST COVERAGE 76
9.7 ANDROID VERSION 76
9.7.1 Porting to Android: Issues and Solutions 76
9.7.2 Optimizations 77
10 COMPARISON 80
11 CONCLUSION AND FUTURE WORKS 82
12 REFERENCES 84
13 APPENDIX A: PERSONAL ONTOLOGY API MANUAL 86

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