| dc.contributor.advisor | 沈錳坤 | zh_TW |
| dc.contributor.author (Authors) | 陳茂富 | zh_TW |
| dc.creator (作者) | 陳茂富 | zh_TW |
| dc.date (日期) | 2001 | en_US |
| dc.date.accessioned | 11-Sep-2009 16:02:50 (UTC+8) | - |
| dc.date.available | 11-Sep-2009 16:02:50 (UTC+8) | - |
| dc.date.issued (上傳時間) | 11-Sep-2009 16:02:50 (UTC+8) | - |
| dc.identifier (Other Identifiers) | G0090753003 | en_US |
| dc.identifier.uri (URI) | https://nccur.lib.nccu.edu.tw/handle/140.119/29677 | - |
| dc.description (描述) | 碩士 | zh_TW |
| dc.description (描述) | 國立政治大學 | zh_TW |
| dc.description (描述) | 資訊科學學系 | zh_TW |
| dc.description (描述) | 90753003 | zh_TW |
| dc.description (描述) | 90 | zh_TW |
| dc.description.abstract (摘要) | Ontology可用來組織、管理與分享知識，Ontology Engineering是一種建構Ontology的過程，建構的過程中，多數的工作需要人費時費力地去完成，因此利用機器來輔助Ontology Engineering成了一門重要的課題。使用Knowledge Discovery的方法協助Ontology Engineering建構Ontology的過程，稱為Ontology Learning，本論文中提出的Ontology Learning方法為分析使用者在搜尋引擎下關鍵字查詢時的行為，加上利用與查詢關鍵字有關的網頁資訊，以輔助建構Ontology。本論文中的Ontology由使用者所查詢的關鍵字組成，我們要learning的，則是這些關鍵字彼此之間的關係，其中有上義詞、下義詞與同義詞等等，因此，自動尋找關鍵字彼此之間的關係以輔助建構Ontology，即為我們提出本論文的目的。除此之外，本論文亦實作了完整的Ontology Learning系統，從一開始使用者查詢記錄的蒐集，關鍵字擷取與分析，關鍵字之間的關係判定，直到最後Ontology的產生，都將由系統自動完成。 | zh_TW |
| dc.description.abstract (摘要) | Ontology can be used to organize, manage and share knowledge. Ontology Engineering is the process of constructing Ontology. However, it’s usually a time-consuming and error-prone task. Thus, utilizing methods of Knowledge Discovery to help Ontology Engineering is called Ontology Learning. In this thesis, Ontology Learning process is done by using those pages related query terms and analyzing the querying behavior of users on search engines. The Ontology is organized by user query terms and relations among them. These relations we define are hyperonomy, hyponomy, synonymy and et al. Our goal of this thesis is to automatically learn the correct relations among these query terms. Besides, we implemented the complete system platform for Ontology Learning. The system can automatically collect logs, extract and analyze query keywords, and produce the final Ontology. | en_US |
| dc.description.tableofcontents | 目錄 第一章 1 1.1 簡介與動機 1 第二章 3 2.1 ONTOLOGY定義 3 2.2 ONTOLOGY ENGINEERING AND LEARNING 4 2.3 相關研究 4 2.3.1 Ontology Learning 4 2.3.2 Query Log Clustering 5 第三章 7 3.1 WEB LOG PREPROCESSING與USER SESSION IDENTIFICATION 7 3.2 QUERY SESSION IDENTIFICATION 11 3.3 PHRASE EXTRACTION 13 3.3.1 Phrase Identification 16 3.3.2 Phrase Domain Identification 17 3.4 PHRASE RELATION DISCOVERY 17 3.4.1 Candidate Discovery of Phrase Relation 18 3.4.2 Phrase Feature Extraction 24 3.4.3 Final Relation Validation 26 第四章 29 4.1 LOG檔的選擇 29 4.2 PROXY LOG介紹 29 4.3 取得不同搜尋引擎QUERY LOG 32 4.4 WEBPAGE REPOSITORY的建立與網頁資訊處理 32 第五章 35 5.1 實驗環境與資料來源 35 5.2 實驗評估 36 5.3 實驗數據 38 5.4 實驗結果與分析 40 第六章 42 REFERENCE 44 LIST OF TABLES 表3.1: User Path. 9 表3.2: Query Session範例. 14 表3.3: Query Session Identification結果. 14 表3.4: 查詢範例. 16 表3.5: User Session查詢的可能資料分佈. 19 表3.6: Query Type. 20 表3.7: Relation Validation. 28 表4.1: 搜尋引擎查詢URL. 32 表4.2: 處理Yahoo Webpage資訊. 33 表5.1: 實驗資料. 36 表5.2: Learning Accuracy Score Table. 38 表5.3: 實驗數據. 39 表5.4: Query Session. 40 表5.5: Result Relation. 40 表5.6: Ambiguous And Wrong Relation. 41 LIST OF FIGURES 圖2.1: Hierarchical Query Clustering. 6 圖3.1: Web Log範例. 7 圖3.2: 系統流程. 8 圖3.3: 查詢動作. 13 圖3.4: Query Session Identification Algorithm. 15 圖3.5: Phrase Domain Identification. 18 圖3.6: single phrase→single phrase範例. 21 圖3.7: single phrase→multi phrase範例. 22 圖3.8 : multi phrase→single phrase範例. 22 圖3.9: Candidate Relation Discovery. 26 圖3.10: Final Relation Validation. 27 圖3.11: Subsumption範例. 28 圖4.1: 系統架構. 30 圖4.2: Proxy Log範例. 31 圖4.4: Yahoo搜尋的網頁. 34 圖5.1: 實驗方法. 37 圖5.2: Learning Accuracy測量. 39 | zh_TW |
| dc.language.iso | en_US | - |
| dc.source.uri (資料來源) | http://thesis.lib.nccu.edu.tw/record/#G0090753003 | en_US |
| dc.subject (關鍵詞) | 搜尋引擎查詢紀錄 | zh_TW |
| dc.subject (關鍵詞) | 學習Ontology | zh_TW |
| dc.subject (關鍵詞) | Ontology learning | en_US |
| dc.subject (關鍵詞) | Query log | en_US |
| dc.title (題名) | 從搜尋引擎查詢紀錄中學習Ontology | zh_TW |
| dc.title (題名) | Ontology Learning from Query Logs of Search Engines | en_US |
| dc.type (資料類型) | thesis | en |
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