臺灣博碩士論文加值系統

經驗學習檔案常被營建業用來保留過去所累積之經驗與知識，以做為未來專案之用。傳統經驗學習檔案之建立方式倚賴具經驗之工程師或專案經理以人工撰寫之方式，此種人工填寫方式不但耗費心力，且許多營建工程知識常隱含在施工日報、計畫書、竣工報告、規範等工程文件中。欲以人工方式由上述文件中建立經驗學習檔案，確有其困難。文字探勘在自動化資料處理領域成功應用於其他領域之文件摘要處理，若能將此一技術引入營建業經驗學習檔案之建立，對於營建業知識管理之發展與技術經驗之累積將有突破性之助益。本研究參酌美國CII 之經驗學習檔案格式，並以文字探勘技術中之「摘要技術」為基礎，建立經驗學習檔案格式樣版。雖然本研究之成果尚屬實驗性質，文字探勘技術應用在自動建立經驗學習檔案上具有大幅提升經驗學習效率之潛在效益，值得學術與產業界進一步發展。

Lessons-learned file (LLF) is commonly adopted to retain previous knowledge and experiences for future use in many construction organizations. Current practice in capturing LLF is mainly through the costly and time-consuming manual processes conducted by the construction engineers or managers. Moreover, many construction knowledge accumulated from previous projects is berried in the construction documents such as construction journals, proposals, as-built drawings, SPECs, plans, etc. It is impossible to develop LLFs from these documents manually. Text mining (TM) techniques have been successfully applied to document summarization in many areas. It is promising to adopt TM techniques for automatic lesson-learned file generation in construction. This research is a preliminary attempt to develop an Automatic Lessons-Learned File Generator (ALLFG) based on text mining techniques and the lesson-learned file template of CII. A prototype system is programmed. Case study is conducted to extract meaningful LLF from sample Chinese construction document automatically. Although the results are still experimental, promising potentials can be envisioned for practical applications.

第一章 緒論
1.1 研究動機
1.2 研究課題
1.3 研究目的
1.4 研究範圍
1.5 研究方法
1.6 研究流程
第二章 文獻回顧
2.1 知識管理
2.1.1 知識之定義
2.1.2知識管理系統定義
2.1.3 知識管理系統之類型與功能
2.1.4 知識活動分類
2.2 經驗學習
2.2.1 經驗學習之定義
2.2.2 經驗學習相關系統
2.2.3 經驗學習系統概念
2.2.4 CII於經驗學習之研究
2.2.5 LL系統相關研究
2.3 小結
第三章 資料探勘技術
3.1 摘要之類型
3.2文字探勘
3.3 資訊檢索
3.3.1 資訊檢索簡介
3.3.2 資訊檢索相關技術簡介
3.4 以語料庫為基礎的文件摘要方法
3.5 以文件關聯為基礎的文件摘要方法
3.6 以潛在語意為基礎的文件摘要技術
3.7 近年相關研究
3.8 小結
第四章 自動化經驗學習演算法之規劃
4.1 演算法規劃
4.2 案例說明
4.2.1 案例之背景
4.2.2 手動計算範例
4.3 小結
第五章 系統開發
5.1 研究工具
5.2 功能元件
5.3 系統展示
第六章 結果驗證與比較
6.1 實驗設計
6.2 測試資料
6.3 向量空間模型參數測試分析
6.4 系統專家信度驗證
6.4.1驗證結果說明
6.4.2 不符案例之原因探討
第七章 結論與建議
7.1 結論
7.2 建議
參考文獻
附錄一 實驗設計結果
附錄二 案例處理結果

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