網際網路的出現使得全球資訊間的溝通變的更加容易。網際網路讓世界間的資訊能夠相連互通，使用者可以透過網際網路之搜尋引擎來查詢所需要的資訊。雖然搜尋引擎可以幫助使用者收集資訊，但是使用者無法從這些大量結果中整理出其所包含的涵意。另外，網際網路的資訊也會隨著時間增加而變化，使得使用者更不容易去追蹤主題涵意的變化及其意義。因此本研究提出一個二階段漸進式的方法針對使用者有興趣的主題，搜尋相關的資訊，找出可代表此主題的概念結構圖；並利用擴散催化理論近一步隨時間偵測概念的變化並找出其變化的意義。
本研究接著進行實驗以驗證所提方法之適用性。實驗一是評估所提方法第一階段的輸出結果，經專業專家驗證所得結果有很高的精確度與回覆率。實驗二是評估所提方法追蹤的涵意變化結果，經專業專家驗證所得結果亦有很高的同意率。這些實驗都說明所提方法在實際案例的實用性。因此，藉由本研究方法的幫助，使用者可以容易地瞭解他們有興趣的主題內容涵意，並知悉這些涵意隨時間的變化。

The emergence of the Internet has made the global information communications much easier than before. Users can navigate the desired information over the Internet by means of search engines. Even though search engine can help users search specified topic in a primary way, users usually cannot gain the overall idea of what the entire navigated results mean. In addition, information over the Internet keeps changing. Users cannot even keep track of the changes, let alone to comprehend the meanings of such changes. Consequently, this research proposes a two-stage incremental approach to figuring out the concept structure that represents the main concepts of the search results in the first stage, and keeping track of the concept changes with time based on spreading activation theory to assist users in the second stage.
Experiments are conducted to examine the feasibility of our proposed approach. The first experiment is to evaluate the results from the first stage. It shows that the performance on recall and precision is quite satisfactory based on human experts’ results. The second experiment is to examine the changing results from the entire proposed approach. It shows that high degree of agreement with our results is achieved from domain experts. Both experiments justify the feasibility of our proposed approach in real applications. That is, applying our proposed approach, users can easily focus on the topic they are interested in and learn its trend with great support.
Keywords: Internet, Concepts Extraction, Concept Change Detection, Spreading Activation Theory.

CHAPTER 1 INTRODUCTION 1
1.1 OVERVIEW 1
1.2 OBJECTIVE OF THE RESEARCH 2
1.3 ORGANIZATION OF THE THESIS 3
CHAPTER 2 LITERATURE REVIEW 4
2.1 INFORMATION RETRIEVAL 4
(1) Boolean retrieval model 4
(2) Vector space model 5
(3) Probabilistic model 5
2.2 TEXT MINING 6
(1) Text Categorization 7
(2) Document Clustering 7
(3) CONCEPT EXTRACTION 8
2.3 CLUSTERING TECHNIQUES 9
2.4 SPREADING ACTIVATION THEORY 13
CHAPTER 3 PROPOSED APPROACH 16
3.1 CONCEPT EXTRACTION STAGE 16
Step 1: Preprocessing documents 16
Step 2: Establishing the co-occurrence graph 20
Step 3: Calculating co-occurrence frequency 20
Step 4: Clustering features 21
Step 5: Extracting concepts 22
3.2 CONCEPT CHANGE DETECTION STAGE 23
Step 1: Collecting features over time 24
Step 2: Changing the activation strengths of links 25
Step 3: Analyzing the change of clusters 27
Step 4: Detecting concept changes 29
CHAPTER 4 EXPERIMENTS AND RESULTS 31
4.1 EXPERIMENT I ON CONCEPTS EXTRACTION PERFORMANCE 31
4.2 EXPERIMENT II ON THE OVERALL PERFORMANCE 38
4.2.1 Experiment II.1 38
4.2.2 Experiment II.2 42
CHAPTER 5 CONCLUSIONS 46
5.1 CONCLUDING REMARKS 46
5.2 FUTURE WORK 47
REFERENCE 48
APPENDIX A THE EXPERIMENT RESULTS OF CONCEPT EXTRACTION 48
APPENDIX B PREDEFINED KEYWORDS BY EXPERTS 48
APPENDIX C DETAIL INFORMATION OF INFORMATION CHANGE 48
APPENDIX D SUMMARY OF RESULT CONCEPTS 48
APPENDIX E DETAIL INFORMATION OF CONCEPT CHANGE DETECTION 48
APPENDIX F SUMMARY OF RESULT CONCEPTS 48

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