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研究生:薛茹分
研究生(外文):Ju-Fen Hsueh
論文名稱:從網頁文件中學習知識架構以支援網頁搜尋
論文名稱(外文):Learning ontology from Web documents for supporting Web query
指導教授:林福仁林福仁引用關係
指導教授(外文):Fu-Ren Lin
學位類別:碩士
校院名稱:國立中山大學
系所名稱:資訊管理學系研究所
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
畢業學年度:91
語文別:英文
論文頁數:92
中文關鍵詞:查詢延伸本體論建構本體論
外文關鍵詞:query expansionontology leaningontology
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本研究提出一個以本體論知識架構為基礎的查詢延伸機制。自動化的查詢延伸機制發展已久,並使用在包含網頁搜尋等多種不同的查詢系統中。為了更進一步協助使用者釐清有效的搜尋關鍵字,區分使用者可能的目標領域網頁,自動化查詢延伸機制常結合外在的知識庫或字典,試圖提升搜尋的滿意度以及正確性。在資訊科技領域裡,本體論知識架構被視作一個可交換、明確的語言,它定義了特定領域下的重要概念及其關聯。根據本體論知識架構,查詢延伸機制可利用它的概念以及關聯來作為延伸字彙的依據。本研究實作了以機器建構本體論知識架構的流程,以網際網路上的文件作為資料來源,產出一特定領域的本體論知識架構來。此外,亦探討不同的延伸機制以及字彙選擇依據。自動建構的本體論知識架構將藉由延伸機制的搜尋結果來加以驗證。
This thesis proposes a query expansion mechanism based on ontology. Automatic query expansion has facilitated web pages search in several ways. An external knowledge resource can help user identify the searching domain and efficient keywords. Ontology is taken as the metadata of a knowledge domain. Query could be expanding in different approaches based on ontology. In this research, an ontology learning process is implemented. With no initial ontology as backbone, domain ontology is constructed from World Wild Web document semi-automatically. Three expanding approaches based on concepts and their relations are proposed. Ontology learning result and expanding approaches are evaluated by comparing the different search results in atypical IR system.
ABSTRACT III
中文摘要 IV
TABLE OF CONTENTS V
LIST OF FIGURES VIII
LIST OF TABLES IX

CHAPTER 1 INTRODUCTION 1
1.1 RESEARCH BACKGROUND 1
1.2 RESEARCH MOTIVATION 2
1.3 RESEARCH OBJECTIVES 3
1.4 THESIS ORGANIZATION 3
CHAPTER 2 LITERATURE REVIEW 4
2.1 QUERY EXPANSION 4
2.1.1 Query Model 4
2.1.2 Expansion approaches 6
2.2 ONTOLOGY 9
2.2.1 Ontology as Content Theory 9
2.2.2 Automatic Ontology Construction 11
2.3 WEB MINING 16
2.3.1 Text Mining Perspective 17
2.3.2 Internal Web Structure 18
CHAPTER 3 ONTOLOGY LEARNING PROCESS 19
3.1 THE COMPONENTS AND NOTIONS OF ONTOLOGY 20
3.2 DATA COLLECTION AND PREPROCESS 21
3.2.1 Parsing HTML Structure 23
3.2.2 Lexical Analysis 25
3.3 EXTRACTING CONCEPTS FROM HTML DOCUMENTS 27
3.4 FINDING RELATIONS BETWEEN CONCEPTS 29
CHAPTER 4 KNOWLEDGE-BASED QUERY EXPANSION 35
4.1 CRITERIA IN ONTOLOGY-BASED APPROACH 35
4.2 TERM SELECTION AND QUERY MODIFICATION 36
4.2.1 Searching Approaches 36
4.2.2 Query Modification Operations 44
CHAPTER 5 THE EVALUATION OF ONTOLOGY LEARNING WITH QUERY EXPANSION APPLICATION 46
5.1 EVALUATING ONTOLOGY 46
5.2 THE EXPERIMENTAL DESIGN OF QUERY EXPANSION 47
5.2.1 Experimental Environment 47
5.2.2 Testing Data Collection 47
5.3 THE EXPERIMENTAL DESIGN 49
5.3.1 Selecting Initial Query String 49
5.3.2 Query Modification Mechanisms 50
5.3.3 Retrieval Results 51
5.4 EXPERIMENTAL RESULTS AND EVALUATION 52
5.4.1 Recall and Precision Evaluation 52
5.4.2 Evaluating Different Expansion Approaches 55
5.4.3 Evaluating Different Support values 59
CHAPTER 6 CONCLUSIONS AND RESEARCH LIMITATION 61
REFERENCES 64
APPENDIX A. A PENN TREEBANK POS TAG SET 69
APPENDIX B. A PENN TREEBANK PHRASAL CATEGORIES 70
APPENDIX C. A LIST OF CONCEPT TERMS WITH WEIGHTS 71
APPENDIX D. A LIST OF CONCEPT PAIRS WITH DISTANCE 74
APPENDIX E. THE RANKING ALGORITHM USED IN LUCENE 80
APPENDIX F. TERM EXPANSION RESULTS IN THREE APPROACHES WITH VARIOUS SUPPORT VALUES 81
APPENDIX G. THE PAIR T-TEST OF EXPANSION EFFECTIVENESS IN DIFFERENT SUPPORT VALUES 90
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