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題名 以學名結構為基礎之網路搜尋負載量模型設計
A Generic Construct based Workload Model for Web Search
作者 柯怡芬
Ke, I Fen
貢獻者 管郁君<br>諶家蘭
Huang, E.Y.<br>Seng, J.L.
柯怡芬
Ke, I Fen
關鍵詞 網路搜尋
績效評估
負載量模型
學名結構
web search
benchmark
workload model
generic construct
performance
evaluation
日期 2006
上傳時間 18-九月-2009 14:31:59 (UTC+8)
摘要 網際網路搜尋是很重要的工具，可用以蒐集或尋找資訊。然而搜尋結果有時無法完全符合使用者的原意，所以網際網路搜尋引擎公司致力於發展更好的搜尋演算法，是為了增進搜尋結果的準確性並提高使用者對搜尋引擎的使用率，我們從探討的文獻中發現目前並沒有一個較彈性、開放的工具來評量網路搜尋的效能。本研究的目的就是希望能發展出一個較具彈性的負載量模型以針對網路搜尋進行效能評量。本研究著重在效能評量的負載量模型及測試套組的設計，我們希望透過以學名結構為基礎的方法擴展負載量模型的彈性，我們蒐集及研討幾個具代表性的網路搜尋演算法，並找出這些主要演算法的學名結構，以這些學名結構為基礎進行負載量模型的設計，負載量模型包含網頁模型、查詢模型與控制模型。最後，我們利用雛形實作來驗證本研究所提出的研究方法。
Web search service is a vital way to find information on the web. However, not every piece of information found is relevant or useful. In order to improve search accuracy, most designers of the web search engines devote to working on search algorithms development and optimization. From literature, we realize that there are few open or flexible performance evaluation methods for web search service. The objective of this research is to develop a more flexible workload model based on generic construct for web search benchmarking and build an automated benchmarking environment of performance evaluation. Generic constructs are major components which can represent the web search algorithm. We collect and review literature related to web search algorithms and benchmarking. And we identify the generic constructs of key web search algorithms. The workload model consists of a page model, query model and control model. The page model describes the web page structure in web search. The query model defines some important criteria to query the web search engines. The control model defines the variables that used to set up the benchmark environment. Finally, we validate the research model through the prototype implementation.
參考文獻 [1]. Bharat, K., & Henzinger, M. R. (1998). Improved algorithms for topic distillation in a hyperlinked environment. SIGIR `98: Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Melbourne, Australia. 104-111. from http://doi.acm.org/10.1145/290941.290972
[2]. Brin, S., & Page, L. (1998). The anatomy of a large-scale hypertextual web search engine. [Electronic version]. Computer Networks & ISDN Systems, 30, 107-118.
[3]. Can, F., Nuray, R., & Sevdik, A. B. (2004). Automatic performance evaluation of web search engines. [Electronic version]. Information Processing and Management, 40(3, May, 2004), 495-514.
[4]. Chidlovskii, B., Roustant, B., & Brette, M. (2006). Documentum ECI self-repairing wrappers: Performance analysis. SIGMOD `06: Proceedings of the 2006 ACM SIGMOD International Conference on Management of Data, Chicago, IL, USA. 708-717. from http://doi.acm.org/10.1145/1142473.1142555
[5]. C. J., van Rijsbergen. Information retrieval (online book)., 2006 from http://www.dcs.gla.ac.uk/Keith/Preface.html
[6]. Clarke, S., & Willett, P. (1997). Estimating the recall performance of search engines. ASLIB Proceedings, 49 (7), 184-189.
[7]. David, H., Nick, C., Peter, B., & Kathleen, G. (2001). Measuring search engine quality. [Electronic version]. Information Retrieval, 4(1), 33-33.
[8]. Hastie, T., Tibshirani, R., & Friedman, J. H. (2001). The elements of statistical learning : Data mining, inference, and prediction
[9]. Jansen, B. J., & Spink, A. (2006). How are we searching the world wide web? A comparison of nine search engine transaction logs. [Electronic version]. Information Processing and Management, 1, January, 2006(42), 248-263.
[10]. Ji-Rong , W., Ruihua, S., Deng, C., Kaihua, Z., Sphipeng, Y., & Shaozhi, Y., et al. (2003). MICROSOFT RESERACH ASIA AT THE WEB TRACK OF TREC 2003. Paper presented at the Text Retrieval Conference 2003, 408-408.
[11]. Kleinberg, J. M. (1999). Authoritative sources in a hyperlinked environment. J.ACM, 46(5), 604-632.
[12]. Kraaij, W., Westerveld, T., & Hiemstra, D. (2002). The importance of prior probabilities for entry page search. SIGIR `02: Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Tampere, Finland. 27-34. from http://doi.acm.org/10.1145/564376.564383
[13]. Lawrence, P., Sergey, B., Rajeev, M., & Terry, W. (1998). The PageRank citation ranking: Bringing order to the web, Stanford Digital Libraries Working Paper.
[14]. Li, L., Shang, Y., & Zhang, W. (2002). Improvement of HITS-based algorithms on web documents. WWW `02: Proceedings of the 11th International Conference on World Wide Web, Honolulu, Hawaii, USA. 527-535. from http://doi.acm.org/10.1145/511446.511514
[15]. Nick, C., & David , H. (2004). Overview of the TREC-2004 web track. Paper presented at the Text Retrieval Conference 2004.
[16]. Pant, G. (2003). Deriving link-context from HTML tag tree. DMKD `03: Proceedings of the 8th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, San Diego, California. 49-55. from http://doi.acm.org/10.1145/882082.882094
[17]. Qin, T., Liu, T., Zhang, X., Feng, G., Wang, D., & Ma, W. (2007). Topic distillation via sub-site retrieval. [Electronic version]. Information Processing and Management, 43(2, March, 2007), 445-460.
[18]. Richard, J. Measuring search effectiveness., 2006, from http://www.hsl.creighton.edu/hsl/Searching/Recall-Precision.html
[19]. S E , R., & S, W. (1999). Okapi/Keenbow at TREC-8. Paper presented at the The Eighth Text Retrieval Conference (TREC 8), 151-162.
[20]. S E, R., & K , S. J. (1976). Relevance weighting of search terms. [Electronic version]. Journal of the American Society for Information Science, 27(May-June), 129-146.
[21]. Scarpa, M., Puliafito, A., Villari, M., & Zaia, A. (2004). A modeling technique for the performance analysis of web searching applications. IEEE Transactions on Knowledge and Data Engineering, 16(11), 1339-1356.
[22]. Shafi, S. M., & Rather, R. A. (2005). "Precision and Recall of Five Search Engines for Retrieval of Scholarly Information in the Field of Biotechnology." Webology, 2 (2), Article 12. Available at: http://www.webology.ir/2005/v2n2/a12.html
[23]. Stephen, R. (2002). Threshold setting and performance optimization
in adaptive filtering. [Electronic version]. Information Retrieval, 5(2-3), 239-239.
[24]. Vapnik, V. N. (1998). Statistical learning theory Willey.
[25]. Vaughan, L. (2004). New measurements for search engine evaluation proposed and tested. [Electronic version]. Information Processing and Management, 40(4, July, 2004), 677-691.
描述 碩士
國立政治大學
資訊管理研究所
94356017
95
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0094356017
資料類型 thesis
DOI http://dx.doi.org/10.1016/j.ipm.2009.04.004
dc.contributor.advisor 管郁君<br>諶家蘭zh_TW
dc.contributor.advisor Huang, E.Y.<br>Seng, J.L.en_US
dc.contributor.author (作者) 柯怡芬zh_TW
dc.contributor.author (作者) Ke, I Fenen_US
dc.creator (作者) 柯怡芬zh_TW
dc.creator (作者) Ke, I Fenen_US
dc.date (日期) 2006en_US
dc.date.accessioned 18-九月-2009 14:31:59 (UTC+8)-
dc.date.available 18-九月-2009 14:31:59 (UTC+8)-
dc.date.issued (上傳時間) 18-九月-2009 14:31:59 (UTC+8)-
dc.identifier (其他 識別碼) G0094356017en_US
dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/35243-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 資訊管理研究所zh_TW
dc.description (描述) 94356017zh_TW
dc.description (描述) 95zh_TW
dc.description.abstract (摘要) 網際網路搜尋是很重要的工具，可用以蒐集或尋找資訊。然而搜尋結果有時無法完全符合使用者的原意，所以網際網路搜尋引擎公司致力於發展更好的搜尋演算法，是為了增進搜尋結果的準確性並提高使用者對搜尋引擎的使用率，我們從探討的文獻中發現目前並沒有一個較彈性、開放的工具來評量網路搜尋的效能。本研究的目的就是希望能發展出一個較具彈性的負載量模型以針對網路搜尋進行效能評量。本研究著重在效能評量的負載量模型及測試套組的設計，我們希望透過以學名結構為基礎的方法擴展負載量模型的彈性，我們蒐集及研討幾個具代表性的網路搜尋演算法，並找出這些主要演算法的學名結構，以這些學名結構為基礎進行負載量模型的設計，負載量模型包含網頁模型、查詢模型與控制模型。最後，我們利用雛形實作來驗證本研究所提出的研究方法。zh_TW
dc.description.abstract (摘要) Web search service is a vital way to find information on the web. However, not every piece of information found is relevant or useful. In order to improve search accuracy, most designers of the web search engines devote to working on search algorithms development and optimization. From literature, we realize that there are few open or flexible performance evaluation methods for web search service. The objective of this research is to develop a more flexible workload model based on generic construct for web search benchmarking and build an automated benchmarking environment of performance evaluation. Generic constructs are major components which can represent the web search algorithm. We collect and review literature related to web search algorithms and benchmarking. And we identify the generic constructs of key web search algorithms. The workload model consists of a page model, query model and control model. The page model describes the web page structure in web search. The query model defines some important criteria to query the web search engines. The control model defines the variables that used to set up the benchmark environment. Finally, we validate the research model through the prototype implementation.en_US
dc.description.tableofcontents ABSTRACT I
中文摘要 II
誌謝 III
TABLE OF CONTENTS IV
LIST OF FIGURES VI
LIST OF TABLES VIII
CHAPTER 1 INTRODUCTION 1
1.1 Research Motivation 1
1.2 Research Problem 1
1.3 Research Objective 2
1.4 Research Limitation 2
1.5 Research Flow 2
1.5 Organization of Thesis 3
CHAPTER 2 LITERATURE REVIEW 5
2.1 Link Structure 5
2.2 PageRank 5
2.2 HITS ("hypertext induced topic selection") 8
2.3 Improvement of HITS 12
2.4 TREC Web Track 20
2.5 Summary 22
CHAPTER 3 RESEARCH MODEL 26
3.1 Research Structure 26
3.2 Components of Research Model 28
3.3 Page Model 28
3.4 Query Model 30
3.5 Control Model 33
3.6 Performance Metrics 33
CHAPTER 4 PROTOTYPE SYSTEM DEVELOPMENT 35
4.1 Prototype Development Tool 35
4.2 Prototype System Implementation 39
CHAPTER 5 RESEARCH EXPERIMENT 44
5.1 Experimental Design 44
5.2 Experimental Execution and Results 50
5.3 Summary 74
CHAPTER 6 RESEARCH IMPLICATIONS AND CONCLUSIONS 75
6.1 Research Implications 75
6.2 Conclusions 77
6.3 Future Research Work 77
REFERENCES 79




List of Figures
FIGURE 1.1: RESEARCH FLOW 3
FIGURE 2.1: LINK STRUCTURE 5
FIGURE 2.2: ILLUSTRATION OF PAGERANK ALGORITHM 6
FIGURE 2.3: EXAMPLE OF PAGERANK 7
FIGURE 2.4: THE RELATIONSHIP BETWEEN HUBS AND AUTHORITIES 9
FIGURE 2.5: AUTHORITY WEIGHT P = HUB WEIGHT Q1 + HUB WEIGHT Q2+HUB WEIGHT Q3 10
FIGURE 2.6: HUB WEIGHT P=AUTHORITY WEIGHT Q1+AUTHORITY WEIGHT Q2+AUTHORITY WEIGHT Q3 11
FIGURE 2.7: AUTHORITY WEIGHT OF P = HUB WEIGHT OF Q1 *1/3+HUB WEIGHT OF Q2*1/3+HUB WEIGHT OF Q3*1/3 13
FIGURE 2.8: HUB WEIGHT OF P =AUTHORITY WEIGHT OF Q1*1/3+AUTHORITY WEIGHT OF Q2*1/3+AUTHORITY WEIGHT OF Q3*1/3 13
FIGURE 3.1: RESEARCH STRUCTURE 27
FIGURE 3.2: THE PAGE MODEL HIERARCHY 29
FIGURE 3.3: THE QUERY MODEL HIERARCHY 31
FIGURE 4.2: THE MAIN MENU OF WORKLOAD MODEL 39
FIGURE 4.3: PAGE MODEL SELECTION AND QUERY MODEL SELECTION 41
FIGURE 4.4: QUERY SCRIPT OUTPUT OF THE WORKLOAD MODEL 42
FIGURE 4.5: CONTROL MODEL INPUT OF SCHEDULER 43
FIGURE 4.6: THE OUTPUT OF RESULT COLLECTOR 43
FIGURE 5.1: SELECTED INPUT FORTEST1 51
FIGURE 5.2: SCRIPT OUTPUT OF TEST1 51
FIGURE 5.3: THE SEARCH RESULTS OF TEST1 52
FIGURE 5.4: THE SEARCH RESULTS OF TEST1 BY GOOGLE 52
FIGURE 5.5: SELECTED INPUT FOR TEST2 54
FIGURE 5.6: SCRIPT OUTPUT OF TEST2 54
FIGURE 5.7: THE SEARCH RESULTS OF TEST2 55
FIGURE 5.8: THE SEARCH RESULTS OF TEST2 BY GOOGLE 55
FIGURE 5.9: SELECTED INPUT FOR TEST3 57
FIGURE 5.10: SCRIPT OUTPUT OF TEST3 57
FIGURE 5.11: THE SEARCH RESULTS OF TEST3 58
FIGURE 5.12: THE SEARCH RESULTS OF TEST3 BY GOOGLE 58
FIGURE 5.13: SELECTED INPUT FOR TEST4 60
FIGURE 5.14 SCRIPT OUTPUT OF TEST4 60
FIGURE 5.15: THE SEARCH RESULTS OF TEST4 61
FIGURE 5.16: SELECTED INPUT FOR TEST5 62
FIGURE 5.17: SCRIPT OUTPUT OF TEST5 62
FIGURE 5.18: THE SEARCH RESULTS OF TEST5 63
FIGURE 5.19: SELECTED INPUT FOR TEST6 64
FIGURE 5.20: SCRIPT OUTPUT OF TEST6 64
FIGURE 5.21: THE SEARCH RESULTS OF TEST6 65
FIGURE 5.22: SELECTED INPUT FOR TEST7 66
FIGURE 5.23: SCRIPT OUTPUT OF TEST7 66
FIGURE 5.24: THE SEARCH RESULTS OF TEST7 67
FIGURE 5.25: SELECTED INPUT FOR TEST8 68
FIGURE 5.26: SCRIPT OUTPUT OF TEST8 68
FIGURE 5.27: THE SEARCH RESULTS OF TEST8 69
FIGURE 5.28: SELECTED INPUT FOR TEST9 70
FIGURE 5.29: SCRIPT OUTPUT OF TEST9 70
FIGURE 5.30: THE SEARCH RESULTS OF TEST9 71
FIGURE 5.31: SELECTED INPUT FOR TEST10 72
FIGURE 5.32: SCRIPT OUTPUT OF TEST10 72
FIGURE 5.33: THE SEARCH RESULTS OF TEST10 73


List of Tables
TABLE 2.1: PAGERANK OF PAGE A, B, C IN EACH ITERATION 7
TABLE 2.2: GENERIC CONSTRUCTS OF PAGERANK 8
TABLE 2.3: GENERIC CONSTRUCTS OF HITS 12
TABLE 2.4: GENERIC CONSTRUCTS OF BHITS AND WBHITS 14
TABLE 2.5: GENERIC CONSTRUCTS OF HITS BASED-VSM 16
TABLE 2.6: GENERIC CONSTRUCTS OF HITS BASED-OKAPI 18
TABLE 2.7: GENERIC CONSTRUCTS OF HITS BASED-CDR 19
TABLE 2.8: GENERIC CONSTRUCTS OF HITS BASED-TLS 19
TABLE 2.9: SUMMARY OF ALGORITHMS 22
TABLE 2.10: GENERIC CONSTRUCTS OF ALGORITHMS 24
TABLE 2.11: OPERATIONS OF GENERIC CONSTRUCTS OF ALGORITHMS 24
TABLE 2.12: GENERIC CONSTRUCTS OF MSRA AT WEB TRACK OF TREC 2003 AND 2004 24
TABLE 2.13: OPERATIONS OF GENERIC CONSTRUCTS OF MSRA AT WEB TRACK OF TREC 2003 AND 2004 25
TABLE 3.1: THE MAPPING OF GENERIC CONSTRUCTS AND WORKLOAD MODEL 28
TABLE 3.2: THE MAPPING OF OPERATIONS OF GENERIC CONSTRUCTS AND WORKLOAD MODEL 28
TABLE 3.3: EXAMPLES OF THREE CATEGORIES 30
TABLE 4.1: THE DESCRIPTION OF PROTOTYPE DEVELOPMENT TOOL 35
TABLE 4.2: THE REQUEST METHOD AND URL OF WEB SEARCH SERVICES 35
TABLE 4.3: THE REQUEST PARAMETERS OF WEB SEARCH SERVICES 36
TABLE 5.1: THE TEST SPECIFICATIONS OF SINGLE PAGE-URL 44
TABLE 5.2: THE TEST SPECIFICATIONS OF SINGLE PAGE-FONT SIZE, FONT COLOR, FRAME, META AND TABLE 45
TABLE 5.3: THE TEST SPECIFICATIONS OF SINGLE PAGE-TITLE 45
TABLE 5.4: THE TEST SPECIFICATIONS OF MULTI PAGE-COMPANY 46
TABLE 5.5: THE TEST SPECIFICATIONS OF QUERY TYPE- HOMEPAGE FINDING 46
TABLE 5.6: THE TEST SPECIFICATIONS OF QUERY TYPE- NAMED PAGE FINDING 47
TABLE 5.7: THE TEST SPECIFICATIONS OF QUERY TYPE-TOPIC DISTILLATION 47
TABLE 5.8: THE TEST SPECIFICATIONS OF LINK STRUCTURE-AUTHORITY-HUB 47
TABLE 5.9: THE TEST SPECIFICATIONS OF SIMILARITY- TLS 48
TABLE 5.10: THE TEST SPECIFICATIONS OF SYNONYM 48
TABLE 5.11: EXPERIMENTAL METRIC 49
TABLE 5.12: EXPERIMENTAL METRICS OF TEST1 53
TABLE 5.13: EXPERIMENTAL METRICS OF TEST1 BY GOOGLE 53
TABLE 5.14: EXPERIMENTAL METRICS OF TEST2 56
TABLE 5.15: EXPERIMENTAL METRICS OF TEST2 BY GOOGLE 56
TABLE 5.16: EXPERIMENTAL METRICS OF TEST3 59
TABLE 5.17: EXPERIMENTAL METRICS OF TEST3 BY GOOGLE 59
TABLE 5.18: EXPERIMENTAL METRICS OF TEST4 61
TABLE 5.19: EXPERIMENTAL METRICS OF TEST5 63
TABLE 5.20: EXPERIMENTAL METRICS OF TEST6 65
TABLE 5.21: EXPERIMENTAL METRICS OF TEST7 67
TABLE 5.22: EXPERIMENTAL METRICS OF TEST8 69
TABLE 5.23: EXPERIMENTAL METRICS OF TEST9 71
TABLE 5.24: EXPERIMENTAL METRICS OF TEST10 73		zh_TW
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dc.language.iso en_US-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0094356017en_US
dc.subject (關鍵詞) 網路搜尋zh_TW
dc.subject (關鍵詞) 績效評估zh_TW
dc.subject (關鍵詞) 負載量模型zh_TW
dc.subject (關鍵詞) 學名結構zh_TW
dc.subject (關鍵詞) web searchen_US
dc.subject (關鍵詞) benchmarken_US
dc.subject (關鍵詞) workload modelen_US
dc.subject (關鍵詞) generic constructen_US
dc.subject (關鍵詞) performanceen_US
dc.subject (關鍵詞) evaluationen_US
dc.title (題名) 以學名結構為基礎之網路搜尋負載量模型設計zh_TW
dc.title (題名) A Generic Construct based Workload Model for Web Searchen_US
dc.type (資料類型) thesisen
dc.relation.reference (參考文獻) [1]. Bharat, K., & Henzinger, M. R. (1998). Improved algorithms for topic distillation in a hyperlinked environment. SIGIR `98: Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Melbourne, Australia. 104-111. from http://doi.acm.org/10.1145/290941.290972zh_TW
dc.relation.reference (參考文獻) [2]. Brin, S., & Page, L. (1998). The anatomy of a large-scale hypertextual web search engine. [Electronic version]. Computer Networks & ISDN Systems, 30, 107-118.zh_TW
dc.relation.reference (參考文獻) [3]. Can, F., Nuray, R., & Sevdik, A. B. (2004). Automatic performance evaluation of web search engines. [Electronic version]. Information Processing and Management, 40(3, May, 2004), 495-514.zh_TW
dc.relation.reference (參考文獻) [4]. Chidlovskii, B., Roustant, B., & Brette, M. (2006). Documentum ECI self-repairing wrappers: Performance analysis. SIGMOD `06: Proceedings of the 2006 ACM SIGMOD International Conference on Management of Data, Chicago, IL, USA. 708-717. from http://doi.acm.org/10.1145/1142473.1142555zh_TW
dc.relation.reference (參考文獻) [5]. C. J., van Rijsbergen. Information retrieval (online book)., 2006 from http://www.dcs.gla.ac.uk/Keith/Preface.htmlzh_TW
dc.relation.reference (參考文獻) [6]. Clarke, S., & Willett, P. (1997). Estimating the recall performance of search engines. ASLIB Proceedings, 49 (7), 184-189.zh_TW
dc.relation.reference (參考文獻) [7]. David, H., Nick, C., Peter, B., & Kathleen, G. (2001). Measuring search engine quality. [Electronic version]. Information Retrieval, 4(1), 33-33.zh_TW
dc.relation.reference (參考文獻) [8]. Hastie, T., Tibshirani, R., & Friedman, J. H. (2001). The elements of statistical learning : Data mining, inference, and predictionzh_TW
dc.relation.reference (參考文獻) [9]. Jansen, B. J., & Spink, A. (2006). How are we searching the world wide web? A comparison of nine search engine transaction logs. [Electronic version]. Information Processing and Management, 1, January, 2006(42), 248-263.zh_TW
dc.relation.reference (參考文獻) [10]. Ji-Rong , W., Ruihua, S., Deng, C., Kaihua, Z., Sphipeng, Y., & Shaozhi, Y., et al. (2003). MICROSOFT RESERACH ASIA AT THE WEB TRACK OF TREC 2003. Paper presented at the Text Retrieval Conference 2003, 408-408.zh_TW
dc.relation.reference (參考文獻) [11]. Kleinberg, J. M. (1999). Authoritative sources in a hyperlinked environment. J.ACM, 46(5), 604-632.zh_TW
dc.relation.reference (參考文獻) [12]. Kraaij, W., Westerveld, T., & Hiemstra, D. (2002). The importance of prior probabilities for entry page search. SIGIR `02: Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Tampere, Finland. 27-34. from http://doi.acm.org/10.1145/564376.564383zh_TW
dc.relation.reference (參考文獻) [13]. Lawrence, P., Sergey, B., Rajeev, M., & Terry, W. (1998). The PageRank citation ranking: Bringing order to the web, Stanford Digital Libraries Working Paper.zh_TW
dc.relation.reference (參考文獻) [14]. Li, L., Shang, Y., & Zhang, W. (2002). Improvement of HITS-based algorithms on web documents. WWW `02: Proceedings of the 11th International Conference on World Wide Web, Honolulu, Hawaii, USA. 527-535. from http://doi.acm.org/10.1145/511446.511514zh_TW
dc.relation.reference (參考文獻) [15]. Nick, C., & David , H. (2004). Overview of the TREC-2004 web track. Paper presented at the Text Retrieval Conference 2004.zh_TW
dc.relation.reference (參考文獻) [16]. Pant, G. (2003). Deriving link-context from HTML tag tree. DMKD `03: Proceedings of the 8th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, San Diego, California. 49-55. from http://doi.acm.org/10.1145/882082.882094zh_TW
dc.relation.reference (參考文獻) [17]. Qin, T., Liu, T., Zhang, X., Feng, G., Wang, D., & Ma, W. (2007). Topic distillation via sub-site retrieval. [Electronic version]. Information Processing and Management, 43(2, March, 2007), 445-460.zh_TW
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dc.identifier.doi (DOI) 10.1016/j.ipm.2009.04.004en_US
dc.doi.uri (DOI) http://dx.doi.org/10.1016/j.ipm.2009.04.004en_US