網際網路的的蓬勃發展使得它成為一個重要的資訊來源。目前當使用者想從網際網路上得到某一特定主題的相關資訊時，最常使用的工具是搜尋引擎。然而使用者透過搜尋引擎所得的結果往往是龐大且雜亂的，使用者無法很容易的了解這些資訊中所包含的涵意。另外由於網際網路上的資料更新速度也相當迅速，這些資訊也會持續的變化，使用者不容易去追蹤這些變化及找出變化隱含的意義。本文提出了一個方法用以協助使用者分析搜尋資訊結果並進一步分析其涵意，同時追蹤這些資訊變化的意義。本文所提方法可分為二個部分，第一個部分是分析針對某一特定主題所搜尋出的資訊涵意，以關鍵字擷取方法RCBKE，找出可以代表資訊中主題的關鍵字及其關係。第二個部份則蒐集在一段時間內特定主題的資訊並分析其資訊變化的涵意及趨勢。二個部分皆以實驗評估以驗證所提方法之適用性。

Information acquisition over the Internet has become popular recently. Users, however, have difficulty in understanding the overall concept resulting from the searched information about a specific topic of their interests in the Internet. Moreover, such pieces of information keep changing over time. Therefore, in this thesis, an approach is proposed to help users further realize the searched results of their interested topic, and detect implications of the information changes over time. The first part of this approach is to gather information of a user-specified topic and analyze the overall meaning and the relations represented by those pieces of information. In this manner, users can gain the general concept of what the search results indicate. Here the keyword extraction approach, called RCBKE, is proposed to identify keywords with their relationships. Evaluations are performed and the results show that RCBKE can discover representative keywords. The second part is to track and investigate the information change of the topic in a certain time period. As a result, users can easily recognize the change patterns of the specified topic. An example to illustrate our approach is shown accordingly. The feasibility of our proposed approach is then justified.

CHAPTER 1. INTRODUCTION.................1
1.1 Overview
1.2 Objective of this research.........................2
1.3 Organization of the thesis........................3
CHAPTER 2. LITERATURE REVIEW...............4
2.1 Information retrieval..........................4
2.1.1 Boolean retrieval........................4
2.1.2 Vector-space model.......................5
2.1.3 Latent semantic indexing.....................5
2.2 Automatic keyword extraction.......................6
2.2.1 Frequency-based methods.....................6
2.2.2 Natural language analysis....................7
2.2.3 Learning-based methods.....................7
2.2.4 Lexical cohesion approach....................8
2.3 Clustering analysis...........................10
2.3.1 Clustering techniques......................10
2.3.2 Clustering on relational data...................12
2.4 Current information change tracking systems................13
CHAPTER 3. CLUSTER-BASED KEYWORD EXTRACTION....16
3.1 The RCBKE approach.........................16
3.2 Experiments and Results........................23
3.2.1 Example I..........................23
3.2.2 Experiment II.........................26
3.3 Summary..............................29
CHAPTER 4. PROPOSED APPROACH AND ITS APPLICATIONS..30
4.1 The proposed approach.........................30
4.2 An illustrated application........................32
4.3 Summary..............................40
CHAPTER 5. CONCLUSIONS..................42
5.1 Concluding Remarks..........................42
5.2 Future work.............................43
REFERENCES.........................44
Appendix A. Detailed results of experiment II.................48
Appendix B. Summary of extracted keywords in change detecting experiment....54
Appendix C. Summary of new concepts....................67

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