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系統識別號 U0026-0208201015042500
論文名稱(中文) 由文獻探勘建立一數位學習與知識發現環境之研究
論文名稱(英文) Building a personalized e-learning and knowledge discovery environment via literature mining
校院名稱 成功大學
系所名稱(中) 資訊管理研究所
系所名稱(英) Institute of Information Management
學年度 98
學期 2
出版年 99
研究生(中文) 黃天祥
研究生(英文) Tian-Hsiang Huang
學號 R7894101
學位類別 博士
語文別 英文
論文頁數 60頁
口試委員 口試委員-李建興
口試委員-古政元
口試委員-謝邦昌
口試委員-盧文祥
口試委員-李昇暾
指導教授-王惠嘉
中文關鍵字 個人化數位學習  知識發現  文獻探勘  功能關聯預測 
英文關鍵字 Personalized e-Learning  Knowledge Discovery  Literature Mining  Functional Relationships Prediction 
學科別分類
中文摘要 各類線上資訊來源急遽擴增以及在網際網路可取得之資料多樣化,因而明顯地影響到數位學習之有效應用。原則上若能自動為個別學習者調整其學習輪廓、造就個人化之數位學習環境,將有助於學習效率且可同時兼顧個別學習者之學習興趣。對此本研究提出一個兩階段的做法。首先,經由一個人化文獻探勘演算法,在數位學習者提出查詢後推薦其一份文獻清單。再者,以指定參數方式,藉由文獻之間的關係去挖掘學習者有興趣的知識。在本研究論文中,以一個由三大模組所組成的架構來同時達成個人化數位學習與知識發現,並且以一個生物資訊的個案研究來論證本研究所提架構是有助於改善推薦品質與讓學習者滿意。在此個案中,並針對學習者有興趣之生物功能有效率地提出表現序列標籤(ESTs)之間的可信賴關係預測,該獲取之知識特別有助於生物學家用以建構基因網路。
英文摘要 The rapid expansion in the number of disparate information sources and available online data presents challenges that significantly affect the applicability of e-learning. In principle, it would be better to automatically adjust the learning profiles for individual learners that are derived because of a personalized e-learning environment, which could promote efficient learning while still capturing the interests of the individual learner. Our system encompasses a two-step procedure. First, using a personalized literature-mining algorithm, we propose a literature recommendation list to an e-learner in response to a query. Second, we acquire professional knowledge via literature relationships based on user-specified parameters. In this dissertation, a three-module architecture was devised to build a personalized e-learning and knowledge discovery environment. The evaluation with a biological case study demonstrates that our proposed architecture is helpful for improving recommendation quality and satisfying users. In addition, we efficiently predicted reliable expressed sequence tags (ESTs) relationships for specific biological functions of interest. The professional knowledge is particularly useful for constructing gene networks presented to biologists.
論文目次 摘要 ............................................................................................................... I
Abstract ........................................................................................................... II
Acknowledgement ........................................................................................ III
List of Tables .................................................................................................. VI
List of Figures .............................................................................................. VII
List of Important Symbols ........................................................................ VIII
Chapter 1 Introduction................................................................................. 1
1.1 Background ............................................................................................................. 1
1.2 Research Motivation and Objective ........................................................................ 2
1.3 Research Scope ....................................................................................................... 6
1.4 Research Procedure ................................................................................................. 6
1.5 The Organization of the Dissertation ...................................................................... 7
Chapter 2 Literature Review ....................................................................... 8
2.1 e-Learning ............................................................................................................... 8
2.1.1 Personalized e-Learning ............................................................................... 9
2.1.2 e-Material Recommender ........................................................................... 10
2.2 KDD Method for Bio-domain ............................................................................... 12
2.2.1 Abundant Biomedical Data ........................................................................ 12
2.2.2 Reliable in silico EST Functional Relationships Prediction ...................... 13
Chapter 3 Research Methodology ............................................................. 15
3.1 Personalized e-Material Recommendation Architecture ....................................... 16
3.1.1 Periodic Raw Data Accumulation and Processing Module ....................... 17
3.1.2 Knowledge Acquisition Module ................................................................ 18
3.1.3 e-Material Recommendation Module ......................................................... 20
3.2 Proposed KDD Method for EST Functional Relationships .................................. 23
3.2.1 Literature Retrieval .................................................................................... 23
3.2.2 Mining Representative Keywords for ESTs ............................................... 24
3.2.3 Predicting EST Functional Relationships of Interest ................................. 26
3.2.3.1 Similarity Evaluated through Literature Relationships ................... 26
3.2.3.2 Similarity Assessed through Functional Keywords ........................ 28
3.2.4 Output ......................................................................................................... 30
Chapter 4 A Case Study for a Biological Research Institution .............. 32
4.1 Building a Personalized e-Material Learning Environment .................................. 32
4.1.1 Experimental Dataset ................................................................................. 36
V
4.1.2 Evaluation of Recommendation Quality .................................................... 36
4.1.3 Evaluation for User Satisfaction ................................................................ 38
4.2 Evaluation for the Proposed User-oriented KDD Method .................................... 39
4.2.1 Experimental Dataset for the Proposed User-oriented Method ................. 40
4.2.2 Experimental Design and Evaluation Criteria ............................................ 41
4.2.3 Select a Suitable Threshold  Value ...................................................... 42
4.2.4 Comparison of the Performance of Two Methods ..................................... 43
4.2.5 Ability to Find Real Relationships ............................................................. 45
4.2.6 The Influence of Different E-values ........................................................... 46
Chapter 5 Conclusion and Suggestion ...................................................... 48
5.1 Conclusion ............................................................................................................. 48
5.2 Suggestion ............................................................................................................. 50
5.3 Future work ........................................................................................................... 50
References ...................................................................................................... 52
Biographical Sketch ...................................................................................... 60
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