臺灣博碩士論文加值系統

在本研究提出了一個協助虛擬社群進行知識管理的知識分享機制－知識圖管理系統架構，此系統架構主要包含了知識圖導覽功能、知識搜尋功能及學習顧問功能。知識圖導覽的功能用來讓使用者能夠徹底地瞭解整個實踐社群的知識架構、知識搜尋功能如同搜尋引擎一般，並且以知識圖作為導覽協助知識的搜尋，而學習顧問藉由記錄相同問題背景人們的知識存取序列，由這些序列中分析出某些特定問題的學習歷程，使用者可以藉由參考此學習歷程來縮短其學習時間。為了讓知識圖在新知識加入之後能夠有效率的更新，本研究提出一個漸近式的知識結構管理方法用以管理現有的知識圖。在本研究中建構了知識圖管理系統的雛形系統，藉由知識圖建構及知識圖管理的方式完成上述的三種知識分享的功能；此外我們利用思摩特網上的教學分享資源及資訊管理領域的碩博士論文摘要，對知識圖建構及管理功能加以驗證。

This thesis proposes a knowledge management technique, called knowledge map management system, which consists of three components: knowledge navigation, knowledge seeking, and learning history advisory. The knowledge navigation function helps knowledge seekers to understand the knowledge structure. Similarly, the knowledge-seeking component helps knowledge seekers to retrieve knowledge with its structure. The learning adviser generalizes the knowledge access pattern in problem solving history and advises knowledge in the problem solving process. In this study, we prototype the knowledge map management system to facilitate knowledge sharing activities. In order to keep the knowledge map up to date, we also propose the incremental approach to maintaining knowledge structure with the addition of knowledge artifacts. The knowledge map creation and maintenance functions are evaluated using documents collected from SCTNet and master thesis repository at National Central Library, and result in encouraging results.

Abstract II
中文摘要 III
TABLE OF CONTENTS IV
LIST OF FIGURES VII
LIST OF TABLES VIII

Chapter 1. Introduction 1
1.1 Research Motivation and Objectives 1
1.2 Knowledge Map Management System 2
1.2.1 Knowledge Navigation 3
1.2.2 Knowledge Seeker 4
1.2.3 Learning Adviser 6
1.3 SCTNet: Teachers' professional community 7
1.4 Thesis Organization 9
Chapter 2. Literature Review 10
2.1 Virtual Community 10
2.2 Knowledge Management 11
2.2.1 Learning history 11
2.2.2 Nonaka SECI Model 12
2.2.3 Community of practice 14
2.2.4 Knowledge map 15
2.3 Information Retrieval 16
2.3.1 Word segmentation 16
2.3.2 Text indexing 20
2.3.3 Feature selection 23
2.3.4 Similarity assessment 24
2.4 Document Clustering 24
2.4.1 Preliminary tasks 25
2.4.2 Clustering technique 25
2.4.3 Text categorization 29
Chapter 3. Research Framework 31
3.1 The Architecture of Knowledge Map Management System 32
3.2 Knowledge Map Manager 34
Chapter 4. The Knowledge Map Creation 37
4.1 Hierarchical clustering 38
4.2 The Determination of Number of Clusters 39
4.3 K-means 40
4.4 Disjoint Test 40
4.5 An example of the knowledge map creation with synthesis data 41
Chapter 5. The Knowledge Map Maintenance 45
5.1 Document and Cluster Representation 47
5.2 Document Classification 47
5.3 Within Cluster Disjoint Test 47
5.4 Cluster Decomposition 48
5.5 Between Cluster Distance Test and Cluster Merging 48
5.6 Silhouette Coefficient Test 49
5.7 Re-Clustering 49
5.8 Knowledge Map Maintenance with Synthetic Data 49
Chapter 6. Evaluation of the Knowledge Map Creation 54
6.1 Document Set 54
6.2 Evaluation Criteria 55
6.3 The Preprocessing of Documents 56
6.3.1 Text indexing 57
6.3.2 Keyword extraction 57
6.3.3 Term-weighting 58
6.4 Results and Evaluation 58
6.4.1 Experimental design 59
6.4.2 The evaluation of results 59
Chapter 7. Evaluation of the Knowledge Map Maintenance 63
7.1 Evaluation Criteria 63
7.2 Experimental Design 65
7.3 Evaluation Results from Synthetic Documents 66
7.4 Evaluation Results from Real World Documents 70
Chapter 8. Conclusions and Future Research 72
Reference 75
Appendix 80
Appendix A. CKIP morphological tag explanation 80
Appendix B. Term-weighting components (Salton and Buckley, 1988) 82
Appendix C. (1) An Example of NSD documents 83
Appendix C. (2) An Example of the thesis abstracts 84
Appendix D. The Statistics of the Text Indexing Stage 84
Appendix E. (1) Original TAIM Document 85
Appendix E. (2) Keyword Extracted by CKIP auto_tag Program 86
Appendix E. (3) Hybrid Word Segmentation Result 87
Appendix E. (4) Original NSD Documents 87
Appendix E. (5) Keyword Extracted by CKIP auto_tag Program 88
Appendix E. (6) Hybrid Word Segmentation Result 88
Appendix E. (7) Hybrid Word Segmentation Result 89
Appendix E. (8) Hybrid Word Segmentation Result 89
Appendix F. (1) Partial Clustering Results of NSD Documents 90
Appendix F. (2) Partial Clustering Results of TAIM Documents 91
Appendix G. (1) Expanded Knowledge Map of NSD Documents 92
Appendix G. (2) Expanded Knowledge Map of TAIM Documents 93
Appendix H. (1) Evaluation Result of 30% Noise Document Set 94
Appendix H. (2) Evaluation Result of 40% Noise Document Set 95
Appendix H. (3) Evaluation Result of 50% Noise Document Set 97

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