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title page
ABSTRACT
Contents
Chapter 1. Introduction 13
1.1. Research Background 13
1.2. Scope and Objectives of the Research 16
Chapter 2. Literature Review 20
2.1. Rule Extraction by XRML Approach 20
2.2. Ontology, Rule Markup Language, and Knowledge Acquisition 22
2.3. Knowledge Extraction based on Ontology 24
2.4. Ontology Languages for the Semantic Web 25
2.5. Graphical Knowledge Acquisition 27
Chapter 3. Overview of Ontology based Rule Identification 29
3.1. Role of ontology in Rule Identification 29
3.2. Using Ontology in Rule Identification 31
3.3. Issues of using Ontology in Rule Identification 33
3.3.1. Specific Ontology for Rule Acquisition vs. External Ontology 33
3.3.2. The Ontology Learning Effect on the Repeated Rule Acquisition 34
3.3.3. A Top-down Approach vs. Bottom-up Approach in Rule Identification 35
3.3.4. Management of Ontology Scope in the Rule Identification Procedure 36
3.3.5. Detection Order of Variable and Value 37
3.3.6. Detection of Omitted Variable 38
3.3.7. Cutting Point Effect in the Recommendation of Rule Components 38
Chapter 4. Design of ontology for rule acquisition 40
4.1. Requirements of OntoRule 40
4.1.1. Information on the Domain of Rules for Managing Ontology Range 40
4.1.2. Vocabulary Information on Variables and Values of Rules 41
4.1.3. The Relationships between Variables and Values for Association 41
4.1.4. Synonyms for Domain Specific Terms 42
4.1.5. Information on Rule Structure 42
4.2. The Design of OntoRule 42
4.2.1. Information on Domain and Application for Managing the Ontology Range 45
4.2.2. Representation of Variables and Values 45
4.2.3. Representation of Rule Structure 46
4.2.4. Ontology for the Characteristics of Variables and Values 47
4.2.5. Ontology for Synonym 48
4.3. Ontology Generation from Rules 49
4.3.1. Generation of Domain and Application Information 51
4.3.2. Rule Group and Rule Ontology Generation 51
4.3.3. Term Ontology Generation 52
Chapter 5. Ontology Based Rule Identification Procedure 55
5.1. Select Tr(이미지참조) 56
5.2. Recall Or(이미지참조) 57
5.3. Search for Variables from the Text 57
5.3.1. Search for variables from Tr by using the ontology, Or(이미지참조) 58
5.3.2. Select variables from V 58
5.4. Search for Linked Values to Identified Variables from the Text 59
5.4.1. Search for linked values to Vi from Tr by using ontology(이미지참조) 59
5.4.2. Select values from U 60
5.5. Search for Omitted Variables 61
5.5.1. Search for omitted variables from Or(이미지참조) 61
5.5.2. Search for values linked to OV from Tr 61
5.5.3. Select values and omitted variables from OV and OU 62
5.6. Composing Rules through the Best-First Search Using Ontology 62
5.6.1. Issues on Composing Rules through the Best-First Search 63
5.6.2. Preparations for Rule Composition 64
5.6.3. Rule Ordering 66
5.6.4. Variable Ordering 67
5.6.5. Evaluation of Composed Rules 67
5.6.6. The Best-First Search Algorithm for Rule Composition 69
5.6.7. Handling Incomplete Nodes 74
5.7. Assign IF or THEN to Identified Variables and Values 75
5.8. Identify Remaining Rule Components Manually 75
Chapter 6. Illustrative Example of Rule Identification 76
6.1. Select Tr(이미지참조) 76
6.2. Recall Or(이미지참조) 76
6.3. Search for Variables from the Text 77
6.4. Search for Linked Values to Identified Variables from the Text 79
6.5. Search for Omitted Variables 80
6.6. Identify Rules from Identified Variables using OntoRule 82
6.7. Assign IF or THEN to Identified Variables and Values 83
6.8. Identify Remaining Rule Components Manually 84
Chapter 7. Performance of Rule Identification Using Ontology 85
7.1. Experiment Design for Evaluation 85
7.2. Summary of Rule Component Status in BN and Powells 89
7.3. Contribution of Using Ontology in Variable and Value Identification 91
7.4. The Ontology Learning Effect 93
7.5. Contribution of Using Ontology in Omitted Variable Identification 94
7.6. Table vs. Text in Term Identification Using Ontology 95
7.7. Contribution of Using Ontology in Identifying Rules 95
7.8. Discussions on the Experiment 97
Chapter 8. Conclusions and Further Research 99
8.1. Summary of Contributions 99
8.2. Further Research 100
요약문 102
References 104
Appendix A. Generalized Rule Ontology from Amazon.com 112
Appendix B. Acquired Rules from BN.com 134
Appendix C. Acquired Rules from Powells.com 141
감사의 글(Acknowledgement) 145
Curriculum Vitae 147
WORK EXPERIENCE 148
Table 4-1. Ontology Generation Sources 50
Table 7-1 The Summary of Ontology acquired from Amazon.com 86
Table 7-2. Summary of Rule Component Status 91
Table 7-3. Experiment Results of Variable and Value Identification 92
Table 7-4. Experiment Results of Value Identification by Variable Type 92
Table 7-5. Comparison Results of Ontology Learning from Text of Powells 94
Table 7-6. Experiment Result of Omitted Variable Identification 94
Table 7-7. Identification Result on Tables vs. Texts 95
Table 7-8. Experiment Results of Rule Composition 96
Table 7-9. Experiment Results of IF and THEN Identification 96
Figure 1-1. Rule Identification Process by using Ontology 15
Figure 2-1. Rule Acquisition Process by the XRML Approach [Kang and Lee 2005] 21
Figure 3-1. A Web Page explaining the Return Policy of BarnesAndNoble.com 30
Figure 3-2. Example of Ontology Acquired from Amazon.com 32
Figure 3-3. Cutting Point Effect 39
Figure 4-1. OntoRule Schema 43
Figure 4-2. OntoRule Example 43
Figure 4-3. RuleGroup and Rule Ontology Generation Algorithm 52
Figure 4-4. Term Ontology Generation Algorithm 54
Figure 5-1. Example of a Rule Ontology 62
Figure 5-2. The Best-first Search Algorithm for Rule Composition 70
Figure 5-3. Tree for BFS of the Example 73
Figure 5-4. Graph for BFS of the Example 74
Figure 6-1. Selecting a Range for Text 77
Figure 6-2. An Example of Variable Identification 78
Figure 6-3. Related Rules in OntoRule for the Selected Text 79
Figure 6-4. An Example of Value Identification 80
Figure 6-5. An Example of Omitted Variable Identification 81
Figure 6-6. An Example of Rule Identification 82
Figure 6-7. An Example of IF and THEN Assignment 83
Figure 6-8. An Example for the Final Result of Rule Identification 84
Figure 7-1. Relations among Metrics 89
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