跳到主要內容

臺灣博碩士論文加值系統

(44.210.83.132) 您好!臺灣時間:2024/05/25 20:17
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果 :::

詳目顯示

: 
twitterline
研究生:江姿樺
研究生(外文):JIANG, ZIH-HUA
論文名稱:文件檢索之模糊查詢操作與概念關係程度之新方法運算
論文名稱(外文):Novel Methods of Fuzzy Query Operation and Concept Relational Degree of Document Retrieval
指導教授:陳士杰陳士杰引用關係
指導教授(外文):CHEN, SHI-JAY
口試委員:帥嘉珍馬麗菁
口試委員(外文):SHUAI, JIA-JANEMA, LI-CHING
口試日期:2017-06-26
學位類別:碩士
校院名稱:國立聯合大學
系所名稱:資訊管理學系碩士班
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2017
畢業學年度:105
語文別:英文
論文頁數:68
中文關鍵詞:模糊資訊檢索模糊查詢AND/OR 查詢操作元多關係模糊概念網路
外文關鍵詞:Fuzzy information retrievalFuzzy queryAND/OR query operatorsmulti-relationship fuzzy concept networks
相關次數:
  • 被引用被引用:0
  • 點閱點閱:163
  • 評分評分:
  • 下載下載:5
  • 收藏至我的研究室書目清單書目收藏:0
本研究首先探討目前處理模糊文件檢索所遇到之相關問題,及提出以 P-norm 為
基礎之擴展查詢操作元加以改善相關問題,同時列舉一些例子與先前各種平均查
詢操作元作比較。接著,有文章指出在文件檢索中,描繪概念與概念何概念與文
件間的相關程度是建構多關係模糊概念網路中的一個重要步驟。因此我們提出新
方法來計算個概念間的相關程度,並使用一些例子與現存之方法比較。最後將總
結本研究提出之各項新方法。
This thesis firstly discusses the problems of existing AND/OR operators in fuzzy document retrieval, and proposes novel AND/OR query operators based on P-norm operator to handle these problems. We also use some examples to compare the proposed AND/OR operators with existing methods. Then, some articles pointed out that it is an important process to obtain the degree of correlation between concepts for constructing the multi-relational fuzzy concept networks in document retrieval. Therefore, we propose a new method to calculate the degree of correlation between concepts, and use some examples to compare the proposed method with existing methods. Finally, we summarize the new methods proposed in this thesis.
中文摘要 i
ABSTRACT ii
CONTENTS iii
LIST OF FIGURES iv
LIST OF TABLES v
Chapter 1 Introduction 1
1.1Research Background 1
1.2 Research Motivation and Purpose 1
1.3 Research Process 2
Chapter 2 Literature Review 4
2.1 Document Retrieval based on the Common Fuzzy Set Model 4
2.2 Review of T-Operators 4
2.3 Review of Some Existing Averaging-Based AND/OR Query Operators 5
2.4 Analysis Results of the T-Operators and the Averaging Operators 9
2.5 Fuzzy Concept Networks 10
2.6 Multi-Relationship Fuzzy Concept Networks 11
Chapter 3 New Fuzzy Query Operators based on P-norm Operators 13
3.1 Some Analyses of T-Operators and the AND/OR Operators based on Averages 13
3.2 New Fuzzy Query Operators based on P-norm Operators 20
Chapter 4 Methods for Associating Degrees of Fuzzy Concept Networks 37
4.1 Analysis of the Existing Methods for Calculating the Relationships and the Degrees of Association between Concepts 37
4.2 A Novel Method for Calculating Degrees of Association between Two Concepts 47
Chapter 5 Conclusions 53
5.1 The Advantages and Limit of Our Methods 53
5.2 Future Research Direction 53
REFERENCES 55
[1] Agosti, Crestani, M. and Melucci, F. M. (1996). Design and implementation of a tool for the automatic construction of hypertexts for information retrieval, Information Processing & Management, 32 (4), 459-476.
[2] Alsina, C., Trillas, E. and Valverde, L. (1983). On some logical connectives for fuzzy set theory, Journal of Mathematical Analysis and Application, 93 (2), 15-26.
[3] Bezdek, J. C., Biswas, G. and Huang, L.Y. (1986). Transitive closures of fuzzy thesauri for information-retrieval system, International Journal of Man-Machine Studies, 25, 343-356.
[4] Bhatia, S. K. and Deogun, J. S. (1998). Conceptual clustering on information retrieval, IEEE Transactions on systems, Man, and Cybernetics – Part B: Cybernetics, 28 (3), 427-435.
[5] Buell, D. A. and Kraft, D. H. (1981). Threshold values and Boolean retrieval systems, Information Processing and Management, 17 (4), 127-136.
[6] Buell, D. A. (1985). A problem in information retrieval with fuzzy sets, Journal of the American Society for Information Science, 36 (7), 398-401.
[7] Chang, C. S. and Chen, A. L. P. (1998). Supporting conceptual and neighborhood quiries on the World Wide Web, IEEE Transactions on Systems, Man, and Cybernetics – Part B: Cybernetics, 28 (2), 300-308.
[8] Chen, C. L. P. and Lu, Y. (1997). FUZZY: A fuzzy-based concept information system that integrates human categorization and numerical clustering, IEEE Transactions on Systems, Man, and Cybernetics – Part B: Cybernetics, 27 (1), 79-94.
[9] Chen, S. J. and Chen, S. M. (2002). A new method for fuzzy information retrieval based on geometric-mean averaging operators, Proceeding of the 2002 International Computer Symposium: Workshop on Artificial Intelligence, Hualien, Taiwan, Republic of China.
[10] Chen, S. J. and Chen, S. M. (2002). A prioritized information fusion algorithm for handling multi-criteria fuzzy decision making problems, In Proceedings of the 2002 International Conference on Fuzzy Systems and Knowledge Discovery, Singapore.
[11] Chen, S. J. and Chen, S. M. (2007). Fuzzy query processing for document retrieval based on GFNGMA operators, Intelligent Automation & Soft Computing, 13 (2), 171-196.
[12] Chen, S. J. and Chu, H. C. (2010). A new method for fuzzy information retrieval based on quadratic-mean averaging operators, Proceedings of the e-CASE & e-Tech International Conference, 2487-2513.
[13] Chen, S. M. and Horng, Y. J. (1999). Fuzzy query processing for document retrieval based on extended fuzzy concept networks, IEEE Transactions on Systems, Man, and Cybernetics – Part B: Cybernetics, 29 (1), 126-135.
[14] Chen, S. M., Horng, Y. J. and Lee, C. H. (2000). Fuzzy information retrieval method based on multi-relationship fuzzy concept networks, In Proceedings of the 2000 International Computer Symposium: Workshop on Artificial Intelligence, Chiayi, Taiwan, Republic of China, 79–86.
[15] Chen, S. M., Hsiao, W. H. and Horng, Y. J. (1997). A knowledge-based method for fuzzy query processing for document retrieval, Cybernetics and Systems: An International Journal, 28 (2), 99-119.
[16] Chen, S. J. and Liu, C. H. (2013). A new method for fuzzy information retrieval based on quadratic-mean averaging operators, Proceedings of the 2013 International Conference on Electronics and Information Engineering (ICEIE 2010), Japan.
[17] Chen, S. J. and Liu, C. H. (2013). A New Method for Calculating Associating Degrees between Concepts for Multi-Relationships Fuzzy Concept Networks, International Conference on Electrical Engineering and Computer Sciences, 2013.
[18] Chen, S. J. and Shi, Y. Y. (2015). Novel Fuzzy Query Operators for Document Retrieval, The 2015 International Conference on e-Case & e-Technology (e-Tech2015), Kyoto, Japan, Sep. 2015
[19] Chen, S. J. and Shi, Y. Y. (2016). A New Method For Associating Degrees Of Fuzzy Concept Networks, International Conference of Information Technology and Multimedia (ICITM), Kuala Lumpur, Malaysia, 2016.
[20] Chen, S. M. and Wang, J. Y. (1992). Document retrieval using knowledge-based fuzzy information retrieval techniques, IEEE Transactions on Systems, Man, and Cybernetics, 25 (6), 793-803.
[21] Chiang, D. A., Chow, L. R. and Hsien, (1997). Fuzzy information in extended fuzzy relational databases, Fuzzy Sets and Systems, 92 (1), 1-20.
[22] Chiclana, F., Herrera, F. and Herrera-Viedma, E. (2000). The ordered weighted geometric operator: Properties and application, In Proceedings of the Eighth International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, 2, 985-991, Madrid, Spain.
[23] Devedzic, G. B. and Pap, E. (1999). Multi-criteria-multistages linguistic evaluation and ranking of machine tools, Fuzzy Sets and Systems, 102 (4), 451-461.
[24] Fagin, R. (1999). Combining fuzzy information from multiple systems, Journal of Computer and System Sciences, 58 (2), 83-99.
[25] Frakes, W. B. (1992). Stemming algorithms, In Information Retrieval: Data Structure & Algorithms, 131-160.
[26] Frigui, H. (2001). Interactive image retrieval using fuzzy sets, Pattern Recognition Letters, 22 (9), 1021-1031.
[27] Gloria, B. and Gabriella, P. (2001). Modeling Vagueness in Information Retrieval, Lecture Notes in Computer Science: Vol. 1980. Berlin, Italy: Springer Heidelberg. 207-241.
[28] Gorzałczany, M. B. (1987). A method of inference in approximate reasoning based on interval-valued fuzzy sets, Fuzzy Sets and Systems, 21 (1), 1-17.
[29] Gupta, M. M. and Qi, J. (1991). Theory of T-norms and fuzzy inference methods, Fuzzy Sets and Systems, 40 (4), 431-450.
[30] Herrera-Viedma, E. (2001). An information retrieval model with ordinal linguistic weighted queries based on two weighting elements, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 9 (supplement), 77-88.
[31] Hong, W. S., Chen, S. J. and Chen, S. M. (2005). Fuzzy information retrieval based on weighted power-mean average operators, Proceedings of the Tenth International Symposium on Artificial Life and Robotics, Beppu, Oita, Japan.
[32] Horng, Y. J., Chen, S. M. and Lee, C. H. (2001). Automatically constructing multi-relationship fuzzy concept networks for document retrieval, In proceedings of the 10th IEEE International Conference on Fuzzy Systems, Melbourne, Australia.
[33] Kacprzyk, J. and Ziółkowski, A. (2001). Computing with words in intelligent database querying: standalone and Internet-based application, Information Sciences, 134 (1), 71-109.
[34] Kandel, A. (1986). Fuzzy Mathematical Techniques with Applications. MA: Addison-Wesley.
[35] Kenney, J. F. and Keeping, E. S. (1962). Mathematics of Statistics, Part I, 3rd ed. Princeton, NJ: Van Nostrand.
[36] Kim, K. J. and Cho, S. B. (2001). A personalized web search engine using fuzzy concept network with link structure, Proceedings of the Joint 9th IFSA Congress and 20th NAFIPS International Conference, Vancouver, Canada, 81-86.
[37] Kim, C. M. and Kim, Y. G. (1999). An improvement of Bandler-Kohout fuzzy information retrieval model using reduced set, In Proceedings of the 1999 IEEE International Fuzzy Systems Conference, 22-25, Seoul, Korea.
[38] Kim, M. H., Lee, J. H. and Lee, Y. J. (1993). Analysis of fuzzy operators for high quality information retrieval, Information Processing Letters, 46 (6), 251-256.
[39] Klir, G. J. and Yuan, B. (1995). Fuzzy Sets and Fuzzy Logic: Theory and Applications, Prentice-Hall, Upper Saddle River, N J.
[40] Kohli, S. and Gupta, A. (2014) A Survey on Web Information Retrieval Inside Fuzzy Framework. Proceedings of the Third International Conference on Soft Computing for Problem Solving, 259, 433-445. doi: 10.1007
[41] Kosko, B. (1992). Neural Network and fuzzy System. NJ: Prentice-Hall.
[42] Kracker, M. (1992). A fuzzy concept network model and its applications, “Proceedings of the First IEEE International Conference on Fuzzy Systems, U. S. A., 761-768.
[43] Kraft, D. H. and Buell, D. A. (1994). An extended fuzzy linguistic approach to generalize Boolean information retrieval, Information Sciences, 2 (4), 119-134.
[44] Kraft, D. H. and Buell, D. A. (1983). Fuzzy sets and generalized Boolean retrieval systems, International Journal of Man-Machine Studies, 19 (2), 45-56.
[45] Lee, J. H., Kim, W. Y., Kim, M. H. and Lee, Y. J. (1993). On the evaluation of Boolean operators in the extended Boolean retrieval framework, In Proceedings of the Sixteenth Annual ACM Conference on Research and Development in Information Retrieval, 291-297, Pittsburgh, PA.
[46] Lee, J. H., Kim, M. H. and Lee, Y. J. (1992). Enhancing the fuzzy set model for high quality document rankings, In Proceedings of the 19 th Euromiero Conference, 337-344, Paris, France.
[47] Lee, J. H., Kim, M. H. and Lee, Y. J. (1994). Ranking documents in thesaurus-based Boolean retrieval systems, Information Processing and Management, 30 (2), 79-91.
[48] Lee, J. H. (1994). Properties of extended Boolean models in information retrieval, In Proceedings of the Seventeenth Annual ACM Conference on Research and Development in Information Retrieval, 182-190, Dublin, Ireland.
[49] Liang, T. and Chang, C. C. (1999). Chinese textual retrieval based on fuzzy concept networks,” Proceedings of National Computer Symposium, 1, Tamsui, Taiwan, Republic of China, 61-67.
[50] Lin, C. C., Tseng, S. Y. and Chen, P.M (1999). A fuzzy document retrieval system based on concept networks and cluster analysis, Soochow Journal of Economics and Business, 25, 39-60.
[51] Liu, S. Y. and Chen, J. G. (1995). Development of a machine troubleshooting expert system via fuzzy multi-attribute decision-making approach, Expert Systems with Applications, 8 (1), 187-201.
[52] Lucarella, D. and Morara, R. First: Fuzzy information retrieval system, Journal of Information Science, 17 (3), 81-91.
[53] Mantaras, R. L., Cortes, U., Manero, J. and Plaza, E. Knowledge engineering for a document retrieval system, Fuzzy Sets and Systems, 38 (3), 223-240.
[54] Marichal, J. L. (1998). Aggregation operators for multi-criteria decision aid, PhD in Sciences, 258.
[55] Miyamoto, S. (1990). Fuzzy Sets in Information Retrieval and Cluster Analysis, Kluwer, Dordrecht.
[56] Pasi, G. and Pereira, R. A. M. (1999). A decision making approach to relevance feedback in information retrieval: A model based on soft consensus dynamics, International Journal of Intelligent Systems, 14 (2), 105-122.
[57] Quillian, R. (1968). Semantic memory, in Semantic Information Processing, edited by Object-Oriented, Cambridge, MA: MIT Press.
[58] Robertson, S. E. (1978). On the nature of fuzz: A diatribe, Journal of the American Society for Information Science, 29 (5), 304-307.
[59] Sachs, W. M. (1976). An approach to associative retrieval through the theory of fuzzy sets, Journal of the American Society for Information Science, 27 (2), 85-87.
[60] Salton, G. (1971). The SMART Retrieval System: Experiments in Automatic Document Processing. New Jersey: Prentice Hall.
[61] Salton, G. (1988). A simple blueprint for automatic Boolean query processing, Information Processing and Management, 24 (4), 269-280.
[62] Salton, G. and Buckley, C. (1988). Term-weighting approached in automatic text retrieval, Information Proceeding and Management, 24(5), 513-523
[63] Salton, G. E., Fox, A. and Wu, H. (1983). Extended Boolean information retrieval, Communications of the ACM, 26 (13), 1022-1036.
[64] Salton, G. and Mcgill, M. J. Introduction to Modern Information Retrieval. NY: McGraw-Hill.
[65] Singh, H. (2013) Real life applications of fuzzy logic, Advances in Fuzzy Systems.
[66] Smith, M. E. (1990). Aspects of the P-Norm model of information retrieval: Syntactic query generation, efficiency, and theoretical properties, Ph.D. Dissertation, Cornell University.
[67] Waller, W. G. and Kraft, D. H. (1979). A mathematical model of a weighted Boolean retrieval system, Information Processing and Management, 15 (6), 235-245.
[68] Xu, Z. S. and Da, Q. L. (2002). The Ordered Weighted Geometric Averaging Operators, International Journal of Intelligent Systems, 17, 709-716. doi: 10.1002/int.10045
[69] Yager, R. R. (1988). On Ordered weighted averaging aggregation operators in multi-criteria decision making, IEEE Systems, Man, and Cybernetics Society, 18, 183-190. doi:10.1109/21.87068
[70] Yao, J. S. and Lin, F. T. Constructing a fuzzy flow-shop sequencing model based on statistical data, International Journal of Approximate Reasoning, 29 (3), 215-234.
[71] Young, V. R (1996). Fuzzy subsethood, Fuzzy Sets and Systems, 77(3), 371-384.
[72] Zadeh, L. A. (1965) Fuzzy Sets, Information and Control, 8 (4), 338-353.
[73] Zadeh, L. A. (1997) What is soft computing. Soft Comput. 1 (1), 1.
[74] Zhong, N., Jiming, L., Yao, Y.Y., Ohsuga, S. (2000) Web intelligence. In: Proceedings of 24th Annual International Computer Software and Application Conference, COMPSAC
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top