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Crawling Deep Web Content Through Query Forms

Crawling Deep Web Content Through Query Forms. Jun Liu, Zhaohui Wu, Lu Jiang, Qinghua Zheng and Xiao Liu Speaker: Lu Jiang Xi’an Jiaotong University P.R.China. Outline. Background Related work Minimum Executable Pattern Adaptive Crawling Algorithm Experimental results Conclusions.

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Crawling Deep Web Content Through Query Forms

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  1. Crawling Deep Web Content Through Query Forms Jun Liu, Zhaohui Wu, Lu Jiang, Qinghua Zheng and Xiao Liu Speaker: Lu Jiang Xi’an Jiaotong University P.R.China

  2. Outline • Background • Related work • Minimum Executable Pattern • Adaptive Crawling Algorithm • Experimental results • Conclusions

  3. Outline • Background • Related work • Minimum Executable Pattern • Adaptive Crawling Algorithm • Experimental results • Conclusions

  4. What is the Deep Web • Deep Web (or Hidden Web) refers to World Wide Web content that is not part of the surface Web which is directly indexed by search engines.

  5. Data retrieval in Deep Web [Michael K. Bergman,2001] Why the Deep Web • Organizes high-quality content • Significant piece of the Web

  6. What is the problem? • Ordinary crawlers retrieve content only in Surface Web. • Challenge: make the Deep Web accessible to web search. • A Practical solution: Deep Web crawling

  7. Outline • Background • Related work • Minimum Executable Pattern • Adaptive Crawling Algorithm • Experimental results • Conclusions

  8. Related Work • The prior knowledge-based query methods: • generate queries under the guidance of prior knowledge • E.g. HIdden Web Exposer [Raghavan, 2001] • The non-prior knowledge methods • generate new query by analyzing the data records returned from the previous queries • E.g. Deep Web crawler [Ntoulas, 2005]

  9. Outline • Background • Related work • Minimum Executable Pattern • Adaptive Crawling Algorithm • Experimental results • Conclusions

  10. The idea of the MEP • Previouswork is based on either the genetic textbox or the entire query form. • For genetic textbox: the harvest rate (capability of obtaining new records) of queries are relatively low and simplex. • For entire form. incorrectness of filling out the entire form is excessive. • A proper granularity of pattern is required.

  11. What is the MEP • Query Form. A query form F is a query interface of Deep Web, which can be defined as a set of all elements in it. where ei is an element of F such as a checkbox, text box or radio button. • Executable Pattern (EP). is an executable pattern if the deep web database returns the corresponding results after the query with value assignments of elements in it is issued. • Minimum Executable Pattern (MEP). Given is an executable pattern ,then it is a MEP iff any proper subset of it is not an executable pattern.

  12. MEP Classification • Two types of the MEP. • If there is an infinite domain element (text box) in MEP set, then the MEP is called infinite domain MEP (IMEP). • If all its element are finite domain (radio button, check boxes), then the MEP is called finite domain MEP (FMEP).

  13. What is the MEP 6 FMEPs 5 IMEPs 1 IMEP

  14. Outline • Background • Related work • Minimum Executable Pattern • Adaptive Crawling Algorithm • Experimental results • Conclusions

  15. What is a Query • The ith query to database is implemented using MEP mep and its corresponding keyword vector kv. • E.g. qi(mep(keywords),”art”). • The harvest rate of a query is the ability of obtaining new records.

  16. Overall Algorithm Data Accumulation Phase Prediction Phase

  17. How does a Crawler Work Obtained x new records while accessing y records. Harvest rate = x/y. q (mep(keywords),”art”). The harvest rate and extracted records are used to evaluate query candidate. Iteration goes on until stop condition is satisfied art

  18. Overall Algorithm Prediction Phase

  19. Pattern Harvest Rate • Pattern harvest rate of the mep, depends on the pattern mep itself, rather than choice of keyword vectors. • E.g. MEP(Keywords) and MEP(Abstract) • Two approaches to predict the value. • Continuous prediction • Weighted prediction

  20. Keyword Vector Harvest Rate • Keyword vector harvest rate represents the conditional harvest rate of kv among all candidate keyword vectors of the given mep. • E.g. given the MEP(keywords), find out which kv will bring the most new records. • The estimation of kv harvest rate consists of two parts • Calculate how many records containing kv has been downloaded (SampleDF) Sampling • Estimate how many records containing kv reside in Deep Web (Keyword Capability) Zipf Law • Keyword Vector Harvest rate = Keyword Capability – SampleDF

  21. Convergence Analysis • When to terminate crawling the Deep web database, especially when the size of target database is unknown? • If we assume mk is constant, We have: • S is the record numeber of Deep Web Database • akis the cumulated fraction of new records • mkis the fraction of records returned by the kth query Crawler Bottleneck!

  22. Outline • Background • Related work • Minimum Executable Pattern • Adaptive Crawling Algorithm • Experimental results • Conclusions

  23. Effectiveness

  24. Comparison with state of art method We believe MEP method with multi-MEP outperforms than that with a single one of the multi-MEP

  25. Outline • Background • Related work • Minimum Executable Pattern • Adaptive Crawling Algorithm • Experimental results • Conclusions

  26. Conclusion • The novel concept of MEP provides a foundation to study Deep Web crawling through query forms. • The adaptive crawling method and its related prediction algorithm offer a efficient way to crawling Deep Web content through query forms.

  27. Thanks You!

  28. Appendix Here comes the Appendix

  29. MEP Generation Algorithm

  30. Examples of Prediction

  31. Comparison with LVS method

  32. Continues Prediction • The current harvest rate of a MEP totally depends on the harvest rate of the latest issued query by the MEP. Issue a query via mep1 and get 200 record assessing 250 records Accessing new record rate = 200/250 = 0.8 mep1 = 0.8/(0.33+0.33+0.8) = 0.55 mep2 = 0.33/(0.33+0.33+0.8) = 0.22 mep3 = 0.33/(0.33+0.33+0.8) = 0.22 Issue a query via mep1 and get 30 record assessing 100 records Accessing new record rate = 30/100 = 0.3 mep1 = 0.3/(0.22+0.22+0.3) = 0.40 mep2 = 0.22/(0.22+0.22+0.3) = 0.29 mep3 = 0.22/(0.22+0.22+0.3) = 0.29 mep1 mep2 mep3 0.33 0.33 0.33 0.55 0.22 0.22 0.40 0.29 0.29

  33. Weighted Prediction • The current harvest rate of a MEP depends on all its previous harvest rates of issued query by the MEP.

  34. SampleDF Calculation • document frequency of observed keyword vectorkv in sample croups {d1,...,ds}. • where kvxk is the corresponding Boolean vector of kv in dk, and similarly mepx is the Boolean vector of mep. • ith dimension of vector kv contains in document corresponding dimension of vector kvx is assigned to 1. 0 otherwise; • ith dimension of mep is infinite domain mep then the corresponding position is assigned to 1. 0 otherwise.

  35. SampleDF Calculation Example • kx = (a,b) mep = (student id, exam id, subject) • Four documents D1,D2,D3 and D4 • D1 has both Student ID a and Exam ID b • D2 has only Student ID a • D3 has only Exam ID b • D4 has neither Student ID a and Exam ID b • mepx = (1,1,0) • D1 kvx1 (1,1,0) cos<(1,1,0),(1,1,0)> = 1 • D2 kvx2 (1,0,0) cos<(1,0,0),(1,1,0)> = 0.707 • D3 kvx3 (0,1,0) cos<(0,1,0),(1,1,0)> = 0.707 • D4 kvx4 (0,0,0) cos<(0,0,0),(1.1.0)> = 0 • SampleDF((a,b)| mep) = 1+0.707+0.707+0 = 2.414

  36. Keyword Capability Estimation • Keyword capability denote capability of obtaining records. (differ from harvest rate) • |Dt| is Cartesian product of values of finite element in MEP • For FMEP: f = 1 • For IMEP: Zipf-Mandelbrot Law to estimate f Keyword capability =

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