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系統識別號 U0026-2506201212530300
論文名稱(中文) 以需求導向知識獲取方法強化領域知識本體之研究
論文名稱(英文) A Demand-Driven Knowledge Acquisition Method for Enhancing Domain Ontology Integrity
校院名稱 成功大學
系所名稱(中) 製造資訊與系統研究所
系所名稱(英) Institute of Manufacturing Information and Systems
學年度 100
學期 2
出版年 101
研究生(中文) 鍾震
研究生(英文) Zhen Zhong
學號 p96994054
學位類別 碩士
語文別 中文
論文頁數 87頁
口試委員 指導教授-陳裕民
共同指導教授-陳育仁
口試委員-李昇暾
中文關鍵字 需求  知識管理  知識本體  知識本體獲取  知識擷取 
英文關鍵字 demand  knowledge management  ontology  ontology acquisition  knowledge retrieval 
學科別分類
中文摘要 知識為現今經濟體系中最重要的資源,因此企業須有效地執行知識管理相關策略,在適當的時機提供正確的知識給對的使用者,以產生最高效益。而正確地表達知識為知識管理之基礎與成敗關鍵。知識本體結構化的知識表達模式有助於不同概念或語意間的轉換、交換與再利用,進而協助使用者更加流暢地運用知識,是目前最廣泛被接受的知識表達工具,然而快速成長的知識將導致領域知識本體完整性不足,並降低其使用價值。
本研究之目的為發展一強化領域知識本體之需求導向知識獲取方法,利用使用者之知識需求獲取領域知識本體所缺乏之知識概念,並與領域知識本體整合,以加強領域知識本體之完整性,進而提升其使用價值。為達上述研究目的,本研究主要研究項目包括(1) 強化領域知識本體之需求導向知識獲取流程設計,(2)需求前處理方法發展,(3)知識擷取與搜尋方法發展,(4)知識本體建構方法發展,(5)知識本體整合方法發展(6)強化領域知識本體之需求導向知識獲取機制實作。
英文摘要 Knowledge has been the most important resource in the contemporary economic system. Enterprises need to take effective knowledge- management strategies to provide right knowledge to appropriate knowledge workers at a suitable time in order gain highest benefit. However, accurate knowledge representation is a fundamental and critical point for knowledge management among enterprises. Ontologies are the most popular and acceptable technology to represent domain knowledge due to its structurized representing fashion which performs well in semantic transition, transaction and reuse for knowledge concepts to the end of applying knowledge more smoothly by knowledge user. But the rapidly growth of knowledge with more and more interdisciplinary knowledge workers may relatively decrease the integrity of domain ontologies which reduces its value somehow.
This study proposed a Demand-Driven Knowledge Acquisition Method for enhancing the integrity of domain ontologies. This method acquires and integrates knowledge concepts which the original domain ontology lacked according to users’ knowledge demand in order to increases the value of domain ontologies. According to above mentioned purpose, the study first design a process model of “Demand-Driven Knowledge Acquisition for Enhancing Domain Ontology” and then develops following methods according to such model: (1) Demand Preprocessing, (2) Knowledge Retrieval and Searching, (3) Ontology Construction, (4) Ontology Integration. Finally, implement such model as a mechanism.
論文目次 中文摘要 I
Abstract II
致謝 III
目錄 V
表目錄 VII
圖目錄 VIII
第一章、序論 1
1.1 研究背景 1
1.2 研究動機 1
1.3 研究目的 2
1.4 研究問題分析 2
1.5 研究項目與方法 3
1.6 研究發展程序 4
1.7 研究限制 5
第二章、文獻探討 6
2.1 知識本體 6
2.1.1 知識本體之定義 6
2.1.2 知識本體之呈現 7
2.2 知識本體開發 8
2.2.1知識本體建構 10
2.2.2 知識本體維護 12
2.3 使用者需求分析 13
2.3.1 自然語言處理 14
2.4 資料探勘 15
2.4.1 資訊檢索 15
2.4.2知識獲取 16
第三章、強化領域知識本體之需求導向知識獲取流程 18
3.1 需求導向之領域知識本體獲取模式 18
3.2 強化領域知識本體之需求導向知識獲取流程 19
第四章、強化領域知識本體之需求導向知識獲取方法發展 23
4.1 需求前處理方法 23
4.2 知識擷取方法 28
4.3 知識搜尋方法 38
4.4 知識本體建構方法 43
4.5 知識本體整合方法 62
第五章、系統實作 71
5.1 系統架構 71
5.2 實作環境介紹 72
5.3 系統實作結果 73
第六章、結論與未來研究方向 77
6.1 結果與貢獻 77
6.2 未來研究方向 78
參考文獻 79


表目錄
表2.1 知識本體之相關定義 6
表2.2 知識本體開發之相關活動 8
表2.3 知識本體建構相關研究 10
表2.4 知識本體維護之相關研究 12
表2.5 使用者需求分析之相關研究 14
表2.6 自然語言處理相關研究 14
表4.1 相似度分數表 (實例) 35
表4.2 相似度分數表之正規化結果 37
表4.3 相似度分數表之權重調整結果 38
表4.4 BM25F演算法之參數設定 43
表4.5 詞項與詞組之頻率計算 54
表4.6 轉換矩陣 58
表4.7 轉換矩陣之擴展與膨脹結果 58
表4.8 子結構 與子結構 之知識概念節點數與連結性之比較 61
表4.9 目標知識概念與原始領域知識概念之隸屬值 (實例) 67
表4.10 模糊正規概念繼承關係之關聯強度計算結果 (實例) 69
表4.11 模糊正規概念包含之目標知識概念與原始領域知識概念 69


圖目錄
圖1.1 研究發展程序圖 5
圖2.1 物件導向知識本體 7
圖2.2 知識擷取生命週期 17
圖3.1 需求導向之領域知識本體獲取模式 19
圖3.2 強化領域知識本體之需求導向知識獲取流程 20
圖4.1 語句前處理程序 24
圖4.2 查詢字串知識本體建構程序 26
圖4.3 詞項與詞義之無向圖 (實例) 27
圖4.4 詞義之中心性評估 (實例) 27
圖4.5 查詢字串知識本體 (實例) 28
圖4.6 知識擷取程序 29
圖4.7 原始領域知識本體 (以證券交易知識本體為例) 32
圖4.8 知識搜尋程序 39
圖4.9 搜尋知識文件 (實例) 42
圖4.10 知識文件之前處理程序 44
圖4.11 概念與關係萃取程序 46
圖4.12 概念萃取程序 49
圖4.13 概念後處理程序 50
圖4.14 天際圖(SkyGraph)演算法 51
圖4.15 知識概念之子結構篩選程序 53
圖4.16 知識概念之關係建構程序 53
圖4.17 知識文件(一)之前處理結果 (實例) 54
圖4.18 知識文件(一)之部分無向圖 56
圖4.19 知識文件集之部分無向圖 57
圖4.20 知識文件集部分無向圖之子結構集 60
圖4.21 已刪除最小分支節點之子結構 61
圖4.22 目標知識概念 62
圖4.23 知識本體整合程序 63
圖4.24 目標知識概念與原始領域知識概念之概念點陣 (實例) 68
圖4.25 更新後之領域知識本體 (實例) 70
圖5.1 領域知識本體獲取機制系統架構 72
圖5.2 需求前處理結果 73
圖5.3 查詢字詞與知識文件欄位之相似度計算結果 74
圖5.4 知識擷取與搜尋結果 75
圖5.5 知識文件集之轉換矩陣(部份) 75
圖5.6 知識本體建構結果 76
圖5.7 知識本體整合結果 76

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