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研究生:廖嘉新
研究生(外文):Chia-Hsin Liao
論文名稱:實體論自動建構技術與其在資訊分類上之應用
論文名稱(外文):Automatic Ontology Construction Approach and Its Application for Information Classification
指導教授:郭耀煌郭耀煌引用關係
指導教授(外文):Yau-Hwang Kuo
學位類別:碩士
校院名稱:國立成功大學
系所名稱:資訊工程學系碩博士班
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2002
畢業學年度:90
語文別:英文
論文頁數:101
中文關鍵詞:資訊分類概念聚類糢糊推論特徵詞選取實體論糢糊相容關係
外文關鍵詞:SOMinformation classificationconcept clusteringfeature selectionfuzzy compatibility relationfuzzy inferenceontology
相關次數:
  • 被引用被引用:16
  • 點閱點閱:683
  • 評分評分:
  • 下載下載:133
  • 收藏至我的研究室書目清單書目收藏:8
為了達到有效的知識管理及運用,實體論的技術漸漸的被廣泛地應用在不同的知識領域裡。本論文旨在發展一個實體論的自動建構技術,以幫助知識管理者自動地建構出理想的知識系統。首先,我們提出一套物件導向的模式來表現出實體論的架構,稱之為具物件導向式之實體論,並依據此模式的建構程序來發展實體論自動建構技術。其次,利用中研院所提供之CKIP系統來進行中文文件之斷詞及詞性標注,再利用本論文所提出之特徵詞選取機制從已加注詞性之中文文件中選出重要的特徵詞。再來,本文提出二種自動建構Domain Ontology中之Concept的方法,包括SOM機制及Fuzzy Compatibility Relation技術。在建構的過程中,採用資料探勘、模糊推論機制及模糊聚類等技術來達成建構的自動化。最後,我們提出一個基於實體論架構之資訊分類方法。經由實驗証實,本方法能對網路文件有效地進行自動分類。
In order to efficiently manage and use knowledge, the technologies of ontology are widely applied to various kinds of domain knowledge. This thesis proposes an automatic ontology construction approach that can help knowledge manager efficiently construct a domain knowledge. The first feature of this thesis is to utilize an object-oriented approach to represent the structure of ontology, called object-oriented ontology. Second, the automatic ontology construction approach for Chinese News domain is presented. We embed CKIP system to carry out the Chinese natural language processing including part-of-speech tagging, Chinese-Term analysis and Chinese-Term feature selection. Third, the concept clustering mechanisms for domain ontology construction based on SOM clustering technology and fuzzy compatibility relation approach are proposed in this thesis, respectively. Furthermore, the parallel fuzzy inference mechanism is also adopted to infer the conceptual resonance strength of any two Chinese terms. Finally, we propose a new information classification model based on the constructed ontology. The experimental results exhibit that the proposed approach can classify the Internet documents effectively.
LIST OF FIGURES III
LIST OF TABLES V
CHAPTER 1 INTRODUCTION 1
1.1 OVERVIEW OF RESEARCH ON ONTOLOGY 1
1.2 MOTIVATION AND RESEARCH CONTRIBUTION 2
1.3 THESIS ORGANIZATION 3
CHAPTER 2 RELATED WORKS AND BACKGROUND 5
2.1 CONCEPT OF ONTOLOGY 5
2.1.1 What is Ontology? 5
2.1.2 Why Ontology? 7
2.2 ONTOLOGY LANGUAGE 8
2.2.1 XML-based Ontology Exchange Language (XOL) 8
2.2.2 Simple HTML Ontology Extension (SHOE) 9
2.2.3 Ontology Markup Language (OML) 9
2.2.4 Resource Description Framework (RDF) and RDF Schema (RDFS) 10
2.2.5 Ontology Interchange Language (OIL) 10
2.2.6 DARPA Agent Markup Language (DAML) + OIL 11
2.3 INFORMATION CLASSIFICATION SYSTEMS 12
2.3.1 Classification by Decision Tree Induction 13
2.3.2 Bayesian Classification 13
2.3.3 Classification by Back propagation 14
CHAPTER 3 OBJECT-ORIENTED ONTOLOGY ARCHITECTURE AND ITS CONCEPT CONSTRUCTION MECHANISM BASED ON SOM 15
3.1 OBJECT-ORIENTED STRUCTURE FOR ONTOLOGY REPRESENTATION 15
3.2 THE ONTOLOGY AND OBJECT-ORIENTED DESIGN 17
3.3 SOM-BASED CONCEPT CONSTRUCTION 19
3.3.1 Using Chinese Term’s Knowledge for SOM Input 20
3.3.2 The Ordinary Method of Feature Input for SOM 21
3.3.3 The Mapping Problem of Term’s Characteristics and Input Neuron 22
3.3.4 The Method for Mapping Many Characters into Single Input Neuron 24
CHAPTER 4 AUTOMATIC CONSTRUCTION APPROACH FOR OBJECT-ORIENTED ONTOLOGY 29
4.1 THE FLOW CHART OF ONTOLOGY CONSTRUCTION 29
4.2 DOCUMENT PRE-PROCESSING 31
4.2.1 Part-of-speech Tagging 31
4.2.2 Refining Tagging 33
4.2.3 Chinese Terms Analysis and Stop Word Filter 34
4.2.4 Chinese-Term Analyzer 36
4.3 EVALUATION OF CONCEPTUAL RESONANCE OF TWO CHINESE TERMS 37
4.3.1 Resonance in Part-of-speech 37
4.3.2 Resonance in Term Vocabulary 38
4.3.3 Resonance in Term Association 39
4.3.4 Resonance in Common Term Association 40
4.4 AGGREGATE TERM RESONANCE WITH PARALLEL FUZZY INFERENCE NETWORK 41
4.5 CONCEPT CLUSTERING AND ASSOCIATION 50
4.5.1 Fuzzy Compatibility Relation 50
4.5.2 Concepts Clustering Based on Fuzzy Compatibility Relation Approach 52
4.5.3 Association Rules of Data Mining 57
4.5.4 Associate Concepts by Data Mining Based Approach 61
CHAPTER 5 OO-BASED ONTOLOGY FOR INTERNET/INTRANET INFORMATION CLASSIFICATION 64
5.1 SYSTEM ARCHITECTURE 64
5.2 OO-BASED FUZZY CLASSIFICATION AGENT 67
CHAPTER 6 EXPERIMENTAL RESULTS AND ANALYSIS 71
6.1 DOCUMENT PRE-PROCESSING ANALYSIS 71
6.2 CONCEPTUAL RESONANCE ANALYSIS 72
6.3 THE RESULTS OF CONCEPT CLUSTERING ANALYSIS 73
6.4 THE RESULTS OF CONCEPT ASSOCIATION ANALYSIS 76
6.5 THE ANALYSIS OF INFORMATION CLASSIFICATION 78
CHAPTER 7 CONCLUSIONS AND FUTURE WORKS 83
7.1 CONCLUSIONS 83
7.2 FUTURE WORKS 83
REFERENCE 85
APPENDIX A THE CALCULATION FOR THE CENTER OF GRAVITY 88
APPENDIX B THE DISTRIBUTION OF CONCEPTUAL RESONANCE FOR EACH NEWS CATEGORY 91
APPENDIX C THE DOMAIN ONTOLOGY FOR EACH NEWS CATEGORY 95
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