臺灣博碩士論文加值系統

一般的網際網路搜尋引擎大多是藉由輸入關鍵詞的方式來進行查詢，搜尋引擎不會自動找出與關鍵詞同義或意義相近的關鍵詞網頁，也無法區分每個關鍵詞不同的重要性，而且一字多義的問題常會造成搜尋結果出現大量不符合的網頁。為改善這些問題，Fuzzy-Go模糊搜尋引擎應用模糊理論與語意搜尋技術，建構Fuzzy Ontology以提供較適當的語意距離來進行關鍵詞的語意搜尋，並針對所有收錄網頁加以領域分類和找出關鍵詞及特徵值，最後整合考量每個網頁的關鍵詞相關程度、關鍵詞重要性、領域相關程度、和多個特徵值後，透過模糊聚合運算得出一個整體考量的網頁排序。本論文進一步使用基因演算法提供自適應調整機制，記錄Fuzzy-Go模糊搜尋引擎每次使用者搜尋後所點選的網頁與排序結果之差異，以不斷學習的方式來調整Fuzzy Ontology的組織架構、網頁的所屬領域分類、以及各個特徵值重要性的權重，透過自適應的調整機制來持續改善模糊搜尋引擎的內部設定及搜尋排序結果。

The Fuzzy-Go search engine develops a fuzzy ontology to capture the similarities of terms in the ontology for accomplishing the semantic search of keywords, a web crawler to gather and classify web pages, and a fuzzy search mechanism to aggregate all fuzzy factors based on their degrees of importance and degrees of satisfaction. In this paper, we apply the genetic algorithm to propose a self-adaptation approach to Fuzzy-Go search engine. For each search, the fuzzy search engine records the difference between the ordering of search results and user’s real behavior on clicking web pages. The feedbacks are gathered and analyzed to adjust the fuzzy similarities between terms in the fuzzy ontology, the domain classification of web pages, and the importance degrees of fuzzy factors. The ordering of search results can thus be improved gradually by continuous learning and adaptation.

目錄
中文摘要 I
Abstract II
致謝 III
目錄 IV
圖目錄 VI
表目錄 VIII
第一章 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 3
1.3 論文架構 4
第二章 相關研究與文獻 5
2.1 使用者回饋 5
2.2 語意相關度 6
2.3 自適應機制 8
第三章 模糊搜尋引擎 12
3.1 Fuzzy Ontology 12
3.2 Web Crawler 14
3.3 模糊搜尋機制 15
第四章 自適應調整機制 18
4.1 自適應調整特徵值重要性之權重 18
4.1.1 基因演算法 19
4.1.2 自適應調整特徵值重要性權重的流程 23
4.1.3 基因演算法於自適應調整特徵值重要性權重 24
4.2 自適應調整Fuzzy Ontology之組織架構 33
4.3 自適應調整網頁所屬領域 41
第五章 實驗結果與討論 50
第六章 結論與未來工作 60
6.1 結論 60
6.2 未來工作 61
參考文獻 62

圖目錄

圖3-1 模糊搜尋引擎的系統架構 12
圖3-2 兩階層式的Fuzzy Ontology 13
圖3-3 第二階層Term Lattice範例 14
圖3-4 重要程度的Linguistic Degree 16
圖3-5 關鍵詞密度有效性的歸屬函數 17
圖3-6 Term Extension Mechanism 17
圖4-1 基因演算法流程圖 21
圖4-2 多個特徵值因子重要性的自適應調整 23
圖4-3 針對單次搜尋，個體x與個體i的各自排序結果 26
圖4-4 輪盤選擇法示意圖 28
圖4-5 本研究之GA流程示意圖 29
圖4-6 Whole Arithmetic Crossover範例圖 31
圖4-7 高斯分佈 32
圖4-8 高斯突變範例圖 33
圖4-9 找出與輸入關鍵字相關的term 35
圖4-10 針對Fuzzy Ontology組織架構的自適應功能 36
圖4-11 S型函數 39
圖4-12 Z型函數 40
圖4-13 Domain Ontology示意範例圖 43
圖4-14 自適應調整網頁所屬領域範例 48
圖4-15 自適應調整網頁所屬領域範例(2) 49
圖5-1 使用者操作介面 50
圖5-2 使用者操作介面(2) 51
圖5-3 網頁搜尋結果 52
圖5-4 自適應調整特徵值權重之變化(一) 53
圖5-5 於第25世代的搜尋結果中，排序前十名網頁之點擊次數比例 55
圖5-6 於第125世代的搜尋結果中，排序前十名網頁之點擊次數比例 56
圖5-7 自適應調整特徵值權重之變化(二) 57

表目錄

表4-1 個體與其適應值 28
表5-1 十組關鍵字組合及其重要程度 56

[1]A. E. Eiben, J. E. Smith, Introduction to Evolutionary Computing, Springer, 2003.
[2]A. Maedche, Ontology Learning for the Semantic Web, Kluwer Academic, 2002.
[3]B. Mobasher, R. Cooley, J. Srivastava, Creating Adaptive Web Sites Through Usage-based Clustering of URLs, 1999 Workshop on Knowledge and Data Engineering Exchange, Nov. 1999, pp.19-25.
[4]B. Xu, W. Zhang, H. Yang, W.C. Chu, A Rough Set Based Self-Adaptive Web Search Engine, 25th Annual International Computer Software and Applications Conference, 8-12 Oct 2001, pp.377-382.
[5]C. Brewster, and K. O'Hara, Knowledge Representation with Ontologies: The Present and Future, IEEE Intelligent Systems, 2004, Vol.19, no.1, pp.72-81.
[6]C. Buckley, G. Salton and J. Allan, The Effect of Adding Relevance Information in a Relevance Feedback Environment, ACM SIGIR'94, pp. 292-300, 1994.
[7]C. Fellbaum, Wordnet: An Electronic Lexical Database, MIT Press, March 1998.
[8]D. E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning. Reading, Addison-Wesley, 1989.
[9]D.H. Widyantoro, J. Yen, A Fuzzy Ontology-Based Abstract Search Engine and Its User Studies, 10th IEEE International Conference on Fuzzy Systems, 2-5 Dec 2001, pp.1291-1294.
[10]D.H. Widyantoro, and J. Yen, Using Fuzzy Ontology for Query Refinement in a Personalized Abstract Search Engine, IFSA World Congress and 20th NAFIPS International Conference, 2001, pp.610-615.
[11]D. Larose, Data Mining Methods and Models, Wiley-IEEE Press, 2006.
[12]E. Iosif, A. Potamianos, Unsupervised Semantic Similarity Computation between Terms Using Web Documents, IEEE Transactions on Knowledge and Data Engineering, vol.22, no.11, Oct. 2010, pp.1637-1647.
[13]E.J. Glover, S. Lawrence, M.D. Gordon, W.P. Birmingham, C.L Giles, Recommending Web Documents Based on User Preferences, ACM SIGIR '99 Workshop on Recommender Systems: Algorithms and Evaluation, 1999.
[14]E. Sanchez, F. van Harmelen, D. Dubois, J. Taylor, S. Thrun, Fuzzy Logic and the Semantic Web, Elsevier, 2006.
[15]E.W.T. Ngai, and F.K.T. Wat, Fuzzy Decision Support System for Risk Analysis in E-Commerce Development, Decision Support Systems, 2005, Vol.40, no.2, pp.235-255.
[16]G. Salton and C. Buckley, Improving Retrieval Performance by Relevance Feedback, Journal of the American Society for Information Science, 41(4): 288-297, 1990.
[17]H.J. Zimmermann, Fuzzy Set Theory and Its Applications, 2nd revised edition, Kluwer Academic Publishers, 1991.
[18]I. Niles, and A. Peas, Linking Lexicons and Ontologies: Mapping WordNet to the Suggested Upper Merged Ontology, Information and Knowledge Engineering, 2003, pp.412-416.
[19]I. Niles, and A. Pease, Toward a Standard Upper Ontology, International Conference on Formal Ontology in Information Systems, 2001, pp.2-9.
[20]J.C. Bezdek, Pattern Recognition With Fuzzy Objective Function Algorithms, Plenum: New York, 1981.
[21]J. Gracia, R. Trillo, M. Espinoza, and E. Mena, Querying the web: A multi-ontology disambiguation method, International Conference on Web Engineering, pp. 241–248, 2006.
[22]J. Li, X. Gao, and H. Ji, A Feature Weighted FCM Clustering Algorithm Based on Evolutionary Strategy, in Intelligent Control and Automation. 2002. pp. 1549-1553.
[23]J.C. Dunn, A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters, Journal of Cybernetics, 1973. Vol.3, pp.32-57.
[24]J.C. Bezdek, Pattern Recognition With Fuzzy Objective Function Algorithms, New York: Plenum Press, 1981.
[25]J. Pitkow, P. Pirolli, Life, Death, and Lawfulness on the Electronic Frontier, CHI'97, ACM Press, pp. 383-390, 1997.
[26]J.T. Sun et al., Web-Page Summarization Using Clickthrough Data, 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, August 2005, pp.194-201.
[27]K.W. Church and P. Hanks, Word Association Norms, Mutual Information, and Lexicography, Computational Linguistics, vol.16, no.1, 1990, pp.22-29.
[28]K.W. Church, W. Gale, P. Hanks, D. Hindle, Using Statistics in Lexical Analysis, Using statistics in lexical analysis, In U. Zernik, editor, Lexical Acquisition: Exploring On-line Resources to Build a Lexicon, Lawrence Erlbaum Association, 1991.
[29]L.A. Zadeh, Fuzzy Sets, Information and Control, 1965, Vol.8, pp.338-353.
[30]L.A. Zadeh, A Computional Approach to Fuzzy Quantifiers in National Languages, Computers and Mathematics, 1983, Vol.9, pp.149-184.
[31]L.A. Zadeh, Soft Computing and Fuzzy Logic, IEEE Software, 1994, Vol.11, no.6, pp.48-56.
[32]L. Granka, T. Joachims, and G. Gay, Eye-tracking Analysis of User Behavior in WWW Search, 27th annual international ACM SIGIR conference on Research and development in information retrieval, 2004.
[33]M. Setnes, H. Roubos, GA-Fuzzy Modeling and Classification: Complexity and Performance, IEEE Transactions on Fuzzy Systems, Vol.8, No.5, 2000, pp.509-522.
[34]M. Stinivas, L.M. Patnaik, Genetic Algorithms: A Survey, IEEE Computer Journal, vol.27, 1994, pp.17-26.
[35]P. Castells, M. Fernandez, D. Vallet, An Adaptation of the Vector-Space Model for Ontology-Based Information Retrieval, IEEE Transactions on Knowledge and Data Engineering, No.19 ,2007, pp.261-272.
[36]P. D. Turney, Minning the Web for Synonyms: PMI-IR Versus LSA on Toefl, Twelfth European Conference on Machine Learning (ECML2001), 2001, pp.491-502.
[37]P. Oreizy et al., An Architecture-based Approach to Self-Adaptive Software, IEEE Intelligent Systems, Vol.14, no.3, pp.54-62, May-June, 1999.
[38] P.T. Chang et al., A Comparison of Discrete Algorithms for Fuzzy Weighted Average, IEEE Transactions on Fuzzy Systems, 2006, Vol.14, no.5, pp.663-675.
[39]P. Wongthongtham, E. Chang, T.S. Dillon, I. Sommerville, Development of a Software Engineering Ontology for Multi-site Software Development, IEEE Transactions on Knowledge and Data Engineering, Sept. 2008.
[40]R. Laddaga, P. Robertson, Self Adaptive Software: A Position Paper, in: SELF-STAR: International Workshop on Self-* Properties in Complex Information Systems, 2004.
[41]R.L. Cilibrasi, P.M.B. Vitanyi, The Google Similarity Distance, IEEE Transactions on Knowledge and Data Engineering, vol.19, no.3, Mar. 2007, pp.370-383
[42]R. W. White, I. Ruthven, and J. M. Jose, The Use of Implicit Evidence for Relevance Feedback in Web Retrieval, 24th BCS-IRSG European Colloquium on IR Research. Lecture notes in Computer Science, pp. 93-109, 2002.
[43]S. Fox, K. Karnawat, M. Mydland, S. Dumais, T. White, Evaluating Implicit Measures to Improve Web Search, ACM Transactions on Information Systems (TOIS), Vol. 23, No. 2, April 2005.
[44]S. Lovic, M. Lu, and D. Zhang, Enhancing Search Engine Performance Using ExpertSystems, 2006 IEEE International Conference on Information Reuse Integration, pp. 567-572,2006.
[45]T. Joachims et al., Evaluating the Accuracy of Implicit Feedback from Clicks and Query Reformulations in Web Search, ACM Transactions on Information Systems, Vol. 25, No. 2, April 2007.
[46]T. Joachims, F. Radlinski, Search Engines that Learn from Implicit Feedback, IEEE Computer, vol. 40, No. 8, 2007, pp.34-40.
[47]T.Y. Tseng, and C.M. Klein, A New Algorithm for Fuzzy Multicriteria Decision Making, International Journal of Approximate Reasoning, 1992, Vol.6, pp. 45-66.
[48]V. Lopez, M. Sabou, and E. Motta, PowerMap : Mapping the Real Semantic Web on the Fly, International Semantic Web Conference, 2006, pp.414-427.
[49]W.M. Dong and F.S. Wong, Fuzzy Weighted Average and Implementation of the Extension Principle, Fuzzy Set and System, 1987, Vol.21, pp.183-199.
[50]W. Zhang, B. Xu, H. Yang, Development of a Self-Adaptive Web Search Engine, 3rd International Workshop on Web Site Evolution, 10 Nov 2001, pp.86-93.
[51]X.H. Zhi and Y.F. Li, Building Ontology Automatically Based on Bayesian Network and PART Neural Network, WRI Global Congress on Intelligent Systems, 19-21 May 2009, pp.563-566.
[52]Y. Zhao, J. Dong, and T. Peng, Ontology Classification for Semantic-Web-Based Software Engineering, IEEE Transactions on Services Computing, 2009, Vol.2, no.4, pp.303-317.
[53]Z. Z. Nick and P. Themis, Web Search Using a Genetic Algorithm, IEEE Internet Computing, Vol.5, Mar/Apr 2001, pp.18-26.
[54]侯巧玲、賴聯福、伍朝欽、施明毅、黃亮綜。應用模糊理論與語意搜尋技術開發模糊搜尋引擎。2010 Joint Conference on Object-Oriented Technology and Applications (OOTA) and Software Engineering (TCSE), pp. 408-413, July 2010.
[55] Fuzzy-GO. http://fuzzy.pika.com.tw/.
[56] Google. http://www.google.com/.
[57] SUMO. http://www.ontologyportal.org/.
[58] The Official Google BLOG. http://googleblog.blogspot.com/2008/07/we-knew-web-was-big.html/.
[59] Wikipedia http://zh.wikipedia.org/.
[60] WordNet. http://wordnet.princeton.edu/.
[61] 國立編譯館-學術名詞資訊網。 http://terms.nict.gov.tw/.
[62] 中央研究院-中英雙語知識本體詞網。 http://bow.sinica.edu.tw/.