全球資訊網是網際網路服務中使用最為簡便而且普及的服務，隨著網頁數目的快速增加，使用者在全球資訊網上的資訊檢索變得越來越困難，而搜尋引擎的使用是解決此問題的方法之一，然而，搜尋引擎所回饋的搜尋結果，常常多達數十筆甚至數百筆資料，使得使用者必須再花時間從其中找到其真正想要的網頁，另外，每個使用者因為年齡、學歷、興趣、專長等個人背景因素的不同，對於相同查詢字串檢索出來的查詢結果，可能會有不同的滿意度，因此，針對搜尋結果進行個人化的排序變成了一個重要的課題。
本研究提出利用乏晰概念網路（Fuzzy concept network）來協助使用者進行個人化網頁排序的方法，利用乏晰概念網路中的概念，紀錄使用者的興趣與偏好等個人化資料，然後根據乏晰概念網路的規則，計算搜尋結果中每個網頁與所有概念的關聯程度，最後依據關聯程度值的高低來重新排列搜尋結果的網頁。本研究另設計了一自動學習機制，藉由使用者對排序結果的回饋，能夠自動地修正使用者的個人化資料，使得排序結果越來越符合使用者的需求。

World Wide Web is the most common service in internet. As the number of homepage grows rapidly, it’s more and more difficult to retrieve information on WWW. The use of search engine is one way to solve this problem. However, the search result still contains numerous pages, and user often should pay much time to find the pages they really need from the search result. Besides, because of the different background, everyone may have different satisfaction at the same rank. For this reason, personalization is required for search result.
This paper present a method that uses the fuzzy concept network topersonalize the search result. The fuzzy concept network based on user profile reorders the search result and the system provides personalized high-quality result. We also propose a learning method. By analysing the feedback from users, the system can update user profile automatically.

第一章 緒論1
1.1研究動機1
1.2研究目的2
1.3研究範圍與限制2
1.4論文架構2
第二章 相關文獻探討4
2.1網頁搜尋4
2.1.1搜尋引擎(Search Engine)4
2.1.2 利用連結架構發展出來的演算法5
2.1.3網頁標籤(HTML Tag)權重7
2.2 資訊檢索(Information Retrieval)相關技術9
2.2.1布林模型(Boolean Model)9
2.2.2向量空間模型(Vector Space Model)10
2.3乏晰概念網路（Fuzzy Concept Network）探討11
2.3.1乏晰概念網路（Fuzzy Concept Network）11
2.3.2 概念矩陣(Concept Matrix)14
第三章 系統設計19
3.1系統架構19
3.2個人化的網頁排序21
3.2.1使用者資訊檔22
3.2.2乏晰概念網路模組23
3.2.3文件描述24
3.2.4延伸文件描述矩陣（Expanded Document Descriptor Matrix）27
3.2.5搜尋結果的重新排序27
3.3自動學習機制28
3.3.1知識檔29
3.3.2歷史檔29
3.3.3概念檢索模組29
3.3.4調整模組30
第四章 系統實作與實驗結果分析35
4.1系統實作35
4.1.1系統開發環境35
4.1.2系統流程35
4.2評估方法37
4.3實驗進行與結果38
第五章 結論及未來研究方向48
5.1結論48
5.2研究貢獻48
5.3未來研究方向49
參考文獻51
英文文獻51
中文文獻53
網站部分53

英文文獻[1] B. Pinkerton, “Finding what people want: Experiences with the WebCrawler,” In Proceedings of the First International World Wide Web Conference, Geneva, Switzerland, 1994.[2] B. Pinkerton, “WebCrawler: Finding what people want,” Ph.D. thesis, Department of Computer Science & Engineering, University of Washington, 2000.[3] C. S. Chang and A. L. P. Chen, “Supporting conceptual and neighborhood queries on the world wide web,” IEEE Trans. Syst., Man, Cybern., vol. 28, pp. 300-308, May 1998.[4] D. Lucarella and R. Morara, “FIRST: Fuzzy Information Retrieval SysTem,” Journal of Information Science, Vol. 17, pp. 81-91, 1991.[5] G. Salton and M. E. Lesk, “Computer evaluation of indexing and text processing,” Journal of ACM, Vol 25, No. 1, pp. 8-36, January 1968.[6] G. Salton, “The Smart System -- Experiments in Automatic Document Processing,” chapter Relevance Feedback in Information Retrieval, pp. 313- 323, Prentice Hall, 1971.[7] G. T. Her and J. S. Ke, “A fuzzy information retrieval system model,” in Proc. 1983 National Computer Symp., Taiwan, R.O.C., pp.147-151, 1983.[8] G. Salton, Automatic Text Processing, Addison-Wesley, Reading, MA, 1989.[9] J. Kleinberg, “Authoritative Sources in a Hyperlinked Environment,” In Proceedings of the 9th Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 668-677, Jan 1998.[10]J. Dean and M. R. Henzinger, “Finding Related Pages in the World Wide Web,” Computer Network, vol. 31, pp. 1467-1479, 1999.[11]J. Furnkranz, “Exploiting structural information for text classification on the WWW,” In David J. Hand, Joost N. Kok, and Michael R. Berthold, editors, Proceedings of IDA-99, 3rd Symposium on Intelligent Data Analysis, number 1642 in Lecture Notes in Computer Science, pp. 487--497, Amsterdam, NL, 1999. Springer Verlag, Heidelberg, DE.[12]J. Yen, R. Langari, Fuzzy Logic Intelligence, Control, and Information, Prentice Hall, 1999.[13]K. Bharat, M. Henzinger, “Improved algorithms for topic distillation in hyperlinked environments,” In Proceedings of the 21st International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 104-111, 1998.[14]K. Bharat, A. Broder, M. Henzinger, P. Kumar, and S. Venkatasubramanian, “The connectivity server: Fast access to linkage information on the web,” In Proceedings of the Seventh International World Wide Web Conference, pp. 469-477, 1998.[15]K. J. Kim and S. B. Cho, “A personalized Web Search Engine Using Fuzzy Concept Network with Link Structure,” IFSA World Congress and 20th NAFIPS International Conference, Vol. 1, pp. 81-86, 2001.[16]L. A. Zadeh, “Fuzzy sets,” Inf. Conte., vol. 8, pp. 338-353, 1965[17]L. A. Zadeh, “Fuzzy Logic,” IEEE Computer, Vol. 21, pp. 83-93, April 1988.[18]L. Page, S. Brin, R. Motwani and T. Winograd, “The PageRank Citation Ranking: Bringing Order to the Web”, submitted for publication. http://citeseer.nj.nec.com/page98pagerank.html , Jan 1998.[19]M. F. Porter, “An algorithm for suffix stripping,” Program, 14 no. 3, pp. 130-137, July 1980.[20]M. Zemankova, “FIIS: A fuzzy intelligent information system,” Data Eng., vol. 12, no. 2, pp. 11-20, 1989.[21]M. Kamel, B. Hadfield, and M. Ismail, “Fuzzy query processing using clustering techniques,” Inf. Process. Manage., vol. 26, no. 2, pp. 279- 293, 1990.[22]M. Cutler, Y. Shih and W. Meng, “Using the Structure of HTML Documents to Improve Retrieval,” Proceeding of the USENIX Symposium on Internet Technologies and Systems Monterey, California, December 1997.[23]R. Baeza-Yates, B. Ribeiro-Neto, Modern Information Retrieval, Reading, Mass. : Addison-Wesley Longman, 1999.[24]S. Miyamoto, “Information retrieval based on fuzzy associations,” Fuzzy Sets Syst., vol. 38, pp.191-205, 1990.[25]S. M. Chen, J. Y. Wang, “Document retrieval using knowledge-based fuzzy information retrieval techniques,” IEEE Trans. Syst., Man, Cybern., vol. 25, pp. 793-803, May 1995.[26]S. Soderland, “Learning to extract text-based information from the World Wide Web,” Proceedings of Third International Conference on Knowledge Discovery and DataMining, pp. 251-254, 1997.[27]S. Chakrabarti, B.E. Dom, D. Gibson, R. Kumar, P. Raghavan, S. Rajagopalan, and A. Tomkins, “Spectral filtering for resource discovery,” ACM-SIGIR Workshop on Hypertext IR for the Web, 1998.[28]S. M. Chen, Y. J. Horng, “Fuzzy Query Processing for Document Retrieval Based on Extended Fuzzy Concept Networks,” Syst., Man, Cybern., vol. 29, pp. 96-104, February 1999.[29]T. Murai, M. Miyakoshi, and M. Shimbo, “A fuzzy document retrieval method based on two-valued indexing,” Fuzzy Sets Syst., vol. 30, pp. 103-120, 1989.[30]V. N. Gudivada, V. V. Raghavan, W. I. Grosky, and R. Kasanagottu, “Information retrieval on the World Wide Web,” IEEE Internet Computing, pp. 58-68, Sep-Oct 1997.中文文獻[31]洪一禎，『根據乏晰觀念網路作乏晰資訊擷取的新方法』，國立交通大學資訊科學研究 所碩士論文，民國84 年6 月。[32]張慶權，『以乏晰概念網路為基礎的中文文件擷取研究』，國立交通大學資訊科學研究 所碩士論文，民國86 年6 月。[33]顏逸品，『網際網路半結構化資料之蒐集與整合研究』，國立中央大學資訊管理研究所 碩士論文，民國89 年6 月。網站部分[34]The Clever Project, http://www.almaden.ibm.com/cs/k53/clever.html[35]Google, http://www.google.com/[36]HTML elements, http://www.w3.org/TR/html4/index/elements.html[37]Netcraft, http://www.netcraft.com/Survey/[38]SearchEngineWatch, http://www.searchenginewatch.com/[39]Taiwan.CNET.com, http://taiwan.cnet.com/enterprise/glossary/[40]Yahoo, http://tw.yahoo.com/[41]東森寬頻電信, http://www.ebtelco.com/chinese/index.htm[42]中時電子報網路科技辭典, http://forums.chinatimes.com.tw/dictionary/main.asp