網誌快速地蓬勃成長，並且使得全球資訊網朝向一個網頁時常更新的動態環境。雖然Google已成功地發展了一個搜尋引擎來解決傳統的網頁，卻無法有效地處理Blogspace這個動態的環境。
在本研究中，提出了一個以知識本體為基礎的語意註解架構，並且以概念層面為基礎來適應性地作閱讀列表的推薦。以資訊擷取領域中的語意註解技術來取代關鍵字配對的方式，進而實作一個推薦系統。推薦系統的目的是以每一個網誌的概念偏好為根據，產生一個閱讀列表推薦給每一個網誌。此研究的資料來源由java.blogs 社群取得。系統的實驗透過java程式設計師來評估。
推薦的閱讀列表的評估主要由程式設計師針對於系統推薦的閱讀列表與目標網誌之間所評的相關程度來作衡量。這些作實驗的程師設計師信度亦被測試。結果顯示系統推薦的閱讀列表是呈現於中度相關、推薦的閱讀列表相關度評估與目標網誌的概念密度是不相關的。推薦系統亦是可信賴的。除此之外，亦揭露一些有價值的方向來改善自動閱讀列表的推薦。

Weblogs have been growing quickly and transforming the World Wide Web toward a dynamic environment that Web pages are frequently updated. Although, Google has developed the search engine successfully in cope with the traditional web pages, it cannot effectively handle the dynamic blogspace.
This research proposes an ontology-based semantic annotation framework based on concepts level in order to adaptively recommend blogrolls. The keyword match is being replaced by the semantic annotation technology of IE (Information Extraction) domain to implement a recommendation system. The objective of the recommendation system is to produce a recommended blogroll to the target weblog based on the weblog’s concept affinities. Data sources of this research are from java.blogs community. The experiment of recommendation system is evaluated by Java programmers.
The recommended blogrolls are evaluated by relevance that subjects score the degree of relevance between a target blogger and the recommended blogroll. The reliability of relevance among subjects is also tested. The results show that the recommended blogrolls obtain the middle level of relevance measured by subjects. The relevance evaluation of the recommended blogroll is independent from the concept density of the target blogger. The recommendation system is also reliable. Moreover, this study shed light on directions to improve the automated blogroll recommendation.

ABSTRACT I
中文摘要 II
TABLE OF CONTENTS III
LIST OF FIGURES AND TABLES V
CHAPTER 1 INTRODUCTION 1
1.1 Research Background 1
1.2 Research Motivations and Objectives 3
CHAPTER 2 LITERATURE REVIEW 4
2.1 Blogs 4
2.2 Ontology 6
2.2.1 Ontology as Content Theory 7
2.2.2 Ontology Markup Language & Tools 7
2.2.3 Related Projects Using Ontology for Annotation 12
2.3 Blog Extended Services 19
2.4 Recommendation System 22
2.4.1 Collaborative Filtering (CF) 22
2.4.2 Content-based Filtering (CBF) 23
2.4.3 Recommend System Embedded with Ontology 24
2.5 Annotations in GATE 26
2.5.1 Annotation in GATE and ANNIE 26
CHAPTER 3 FRAMEWORK OF ONTOLOGY-BASED BLOGROLL RECOMMENDATION SYSTEM 29
3.1 System Framework 29
3.1.1 Text Transformation 30
3.1.2 Semantic Annotation 31
3.1.3 Content-based Recommendation 35
CHAPTER 4 EVALUATION 36
4.1 Evaluation Design 36
4.2 Evaluation Results 37
4.2.1 Relevance Evaluation 37
4.2.2 Reliability Analysis 40
CHAPTER 5 CONCLUSION AND LIMITATION 42
5.1 Conclusion 42
5.2 Limitation 43
REFERENCES 44
APPENDIX A. EVALUATION QUESTIONS 50
APPENDIX B. SNAPSHOT OF EVALUATION GUI 54
APPENDIX C. RELEVANCY EVALUATION 55
APPENDIX D. CORRELATION MATRIX OF RECOMMENDED BLOGROLL 58
APPENDIX E. CONCEPT TREE 61
APPENDIX F. SCATTER GRAPH OF MEANS OF INDIVIDUAL RELEVANCE EVALUATION ON THE TEN BLOGROLLS 62
APPENDIX G. SCATTER GRAPH OF SUBJECTS’ MEANS OF RELEVANCE EVALUATION ON THE SAME BLOGROLL 70

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