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研究生:陳盈旭
研究生(外文):Ying-Hsu Chen
論文名稱:基於插曲網絡與軟式計算技術之實體論建構方法
論文名稱(外文):An Ontology Construction Approach Based on Episode Net and Soft Computing Techniques
指導教授:郭耀煌郭耀煌引用關係郭淑美郭淑美引用關係
指導教授(外文):Yau-Hwang KuoShu-Mei Guo
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:2004
畢業學年度:92
語文別:英文
論文頁數:148
中文關鍵詞:插曲網絡軟式計算中文自然語言處理實體論建構
外文關鍵詞:Ontology ConstructionEpisode NetSoft ComputingChinese Natural Language Processing
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  實體論在許多的資訊系統及語意網中越來越重要,而實體論的建構成本卻是一筆很龐大的花費。在本論文中,我們提出自動建構實體論的方法來協助知識工程師建構某特定領域的實體論。在建構實體論的程序上,我們希望能夠提高自動化的程度,使得整個方法套用到不同的領域知識上都能夠正確且有效的建構出實體論。針對特定領域的語料庫(Corpus),我們發展一套機制能自動的擷取語料庫中的中文新詞。並利用資訊擷取(Information Retrieval)、自然語言處理(Natural Language Processing)及軟式計算(Soft Computing)等技術來找出實體論中的概念。此外,我們將Episode 的概念加以延伸利用,建構一個具有上下文關聯的網狀結構,稱為Episode Net。利用Episode Net,我們可以從中擷取概念中靜態的屬性(Attribute)、動態的行為(Operation)以及概念間的關聯(Association)關係。再者,我們利用Episode Net 來計算某一特定語料庫中之上下文關聯強度,且利用HowNet 及WordNet 中的語意知識來計算語意關聯強度,使得建構出的實體論架構能夠更精準。最後,我們利用物件導向模式來表示實體論,建構出四層式物件導向架構的實體論。經由實驗證實,本論文所提出之方法可有效地協助實體論之建構。
  The Ontology is increasingly important for many information systems and SemanticWeb, while the cost of constructing ontology is too much. In this thesis, we propose anautomatic approach for ontology construction to assist the knowledge engineers toconstruct the specific domain ontology. We hope to raise the automation level to makecorrectly and efficiently build the ontology while applying the method to different domains.For different domains, we propose an approach to extract new Chinese terms from thespecific corpus automatically. And we use information retrieval, natural languageprocessing, and soft computing techniques to find out the concepts of the ontology. Inaddition, we extend the concept of episode to construct an Episode Net. Using Episode Net,we can find out the static attributes, dynamic operations, and the associations betweenconcepts of the ontology. Finally, we use object-oriented model to represent the ontologyand then construct the ontology with four-layer object-oriented structure. The experimentalresults show that our approach can effectively assist ontology engineers to construct thedomain ontology.
Chapter 1 Introduction.................................................1
 1.1 Overview of Research on Ontology.................................1
 1.2 Motivations and Research Contribution............................2
 1.3 Thesis Organization..............................................3
Chapter 2 Related Works................................................4
 2.1 Survey of Ontology...............................................4
 2.2 Survey of Episode................................................5
 2.3 Survey of HowNet.................................................6
 2.4 Survey of WordNet................................................7
 2.5 Survey of Soft Computing.........................................8
Chapter 3 The Structure of Ontology Construction Approach.............10
 3.1 Domain Ontology Structure.......................................10
 3.2 The Architecture of Ontology Construction.......................12
  3.2.1 Part-Of-Speech Tagger.......................................14
  3.2.2 Domain Term Combination Processor...........................19
  3.2.3 Feature Term Pre-processor..................................20
  3.2.4 Concept Extractor...........................................23
  3.2.5 Episode Extractor...........................................23
  3.2.6 Episode Net Extractor.......................................24
  3.2.7 Attribute-Operation-Association Extractor...................24
Chapter 4 Episode Net.................................................29
 4.1 Event Sequences.................................................29
 4.2 Episode.........................................................30
 4.3 Graph Transforming..............................................41
Chapter 5 Soft Computing Techniques for Ontology......................43
 5.1 The SOM Concept Clustering Based on Fuzzy Inference.............43
  5.1.1 The Conceptual Similarity in POS Between Any Two Terms......43
  5.1.2 The Conceptual Similarity in Term-Vocabulary Between Any Two Terms.................................................................44
  5.1.3 The Conceptual Similarity in Term-Concept Between Any Two Terms.................................................................44
  5.1.4 Aggregate Term Resonance with Parallel Fuzzy Inference Network...............................................................45
  5.1.5 Self-Organization Map for concept clustering................51
 5.2 Semantic Similarity Computing Based on HowNet...................52
  5.2.1 Using Individual Words to Enhance the Semantic Similarity Based on HowNet.............................................................56
 5.3 Semantic Similarity Computing Based on WordNet..................57
Chapter 6 Experimental Results........................................59
 6.1 Experimental Results for Generating New Chinese POS Rules.......59
 6.2 Domain Term Combination Processing Analysis.....................64
 6.3 Feature Term Pre-processing Analysis............................68
 6.4 Concept Extraction Processing Analysis..........................68
 6.5 Ontology Building Processing Analysis...........................73
Chapter 7 Conclusions and Future Works................................81
 7.1 Conclusions.....................................................81
 7.2 Future Works....................................................82
Reference.............................................................83
Appendix..............................................................87
 Appendix A. The Source of Generating Domain Term POS................87
 Appendix B. Complete Results of Automatic Ontology..................98
 Appendix C. Complete Results of Automatic Ontology.................101
 Appendix D. The Documents in Politics Domain 3.....................106
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