1. Crawling the Hidden Web Authors: Sriram Raghavan Hector Gracia-Molina Presented by: Jorge Zamora

2. Outline Hidden Web Crawler Operation Model HiWE – Hidden Web Exposer LITE – Layout-based Information Extraction Experimental Results Relation to class lectures Pros/Cons Conclusion July 21, 2011JAZ-2Crawling the Hidden Web

3. Hidden Web PIW – Publicly Indexable Web Deep Web –500 times the PIW Hidden Crawler –Parse, process and interact with forms Task specific approach Two Steps –Resource Discovery –Content Extraction July 21, 2011JAZ-3Crawling the Hidden Web

4. Hidden Crawler – Operation Model July 21, 2011JAZ-4Crawling the Hidden Web

5. Hidden Crawler – Operation Model Internal form representation F = ({{E1, E2,…,En},S,M}) Task specific database –Formulates search queries Matching Function Match(({E1,…,En},S,M),D) = {[E1<-v1,…,En<- Vn]}. Response Analysis –Success and error pages, Storage, Tuning July 21, 2011JAZ-5Crawling the Hidden Web

6. Hidden Crawler – Performance Challenge –Wanted to get away from a metric significantly depended on D Submission Effiency –Ntotal = total number of forms crawler submits –SEstrict = Nsucess/Ntotal Penalizes the crawler which might be correct but did not yield any results –SElenient = Nvalid/NTotal Penalized only if the form submission is semantically incorrect. Difficult to evaluate - must evaluate every form submission. July 21, 2011JAZ-6Crawling the Hidden Web

7. HiWE Hidden Web Exposer Prototype Hidden Web Crawler built at Stanford Basic idea –extract some kind of descriptive information or label for each element in the form –task-specific which contains a finite set of categories with associated labels –Matching algorithms attempts to match form labels with database values to form value assignment sets July 21, 2011JAZ-7Crawling the Hidden Web

8. HiWE – Conceptual Parts July 21, 2011JAZ-8Crawling the Hidden Web

9. HiWE – Form Representation F = ({E1,E2,…,En} S, 0) –Dom(Ei) –Label(Ei) July 21, 2011JAZ-9Crawling the Hidden Web

10. HiWE – Task specific Database Organized as a finite set of concepts of categories Each concept has one or more labels and associated values Each Row in the LVS table is of the form (L, V), –L is a label –V = {v1,…, vn} is a fuzzy –vi represents a value –Fuzzy set V has associated membership function Mv –Mv(vi) is the crawlers confidence of assignment July 21, 2011JAZ-10Crawling the Hidden Web

11. HiWE – Matching Function Label Matching –All labels are normalized Common case, Stemming, Stop word removal –String Matching with min edit distances, word orderings –Threshold of Sigma < edit operations. Then set to nil Ranking Value Assignments –Min Rho. –Fuzzy Conjunction - Rho fuz –Average – Rho avg –Probabilistic – Rho prob July 21, 2011JAZ-11Crawling the Hidden Web

12. HiWE – Populating LVS Table Explicit Initialization Built-in entries –Dates, Times, names of months, days of the week Wrapped data Sources –Set of Labels, new entries –Set of Values, search similar, expand existing Crawling Experience –Finite domain elements –Can be used to fill out the second form more efficiently July 21, 2011JAZ-12Crawling the Hidden Web

13. HiWE – Computing Weights Explicit initialization –Fixed, predefined weights (usually 1) representing maximum confidence in human supplied values External data sources or crawler activity –Positive boost – Successful –Negative boost – Unsuccessful Initial weights obtained from external data sources are computed by the wrapper July 21, 2011JAZ-13Crawling the Hidden Web

14. HiWE – Computing Weights Finite domain –Case 1 – Crawler Extracts label, Label Match found Unions the values to the Boost the weights/confidence of the existing values –Case 2 – Crawler Extracts label, Label Match = nil New row is added in LVS table –Case 3 – Can not extract label Identify values that most closely resembles Dom(E) Once located, add values in Dom(E) to value set July 21, 2011JAZ-14Crawling the Hidden Web

15. HiWE – Explicit Configuration July 21, 2011JAZ-15Crawling the Hidden Web 1Set of sites to crawl 2Explicit initialization entries for the LVS table 3Set of data sources, wrapped if necessary 4Label matching threshold (σ) 5Minimum acceptable value assignment rank (ρ min) 6Minimum form size (α) 7Value assignment aggregation function

16. LITE Layout-based information extraction Used in automatically extracting semantic information from search forms. In addition to text, uses the physical layout of the page to aid in extraction Not always reflected in HTML markup July 21, 2011JAZ-16Crawling the Hidden Web

17. LITE – Usage in HiWE Used in Label Extraction Implemented by page pruning. Isolate elements that directly influence the layout of the form elements and labels July 21, 2011JAZ-17Crawling the Hidden Web

18. LITE – Steps Approximate layout of pruned page discarding images, font styles and style sheets Identifies pieces of text closest to form element as candidates Ranks Each candidate taking into account position, font size, font style, number of words Chooses the highest ranked candidate as label associated with element July 21, 2011JAZ-18Crawling the Hidden Web

19. Experiment - Parameters Task 1 Shown which is for “News articles, reports, press releases, and white papers relating to the semiconductor industry, dated sometime in the last ten years” July 21, 2011JAZ-19Crawling the Hidden Web PARAMETERVALUE Number of sites visited 50 Number of forms encountered 218 Number of forms chosen for submission 94 Label matching threshold (σ) 0.75 Minimum form size (α) 3 Value assignment ranking functionρfuz Minimum acceptable value assignment rank (ρmin) 0.6

20. Results – Value Ranking Was executed three times with same parameters, initializations values and parameters but using different ranking function Pave might be a better choice for maximum content extraction Pfuz is the most efficient Pprob submits the most forms but performs poorly July 21, 2011JAZ-20Crawling the Hidden Web Ranking Function Task 1 N total N success SE strict ρ fuz 3214285388.8 ρ avg 3760312683.1 ρ prob 4316281065.1

21. Results – Form Size July 21, 2011JAZ-21Crawling the Hidden Web 3735 2950 3214 2853 2800 2491 1404 78.9% 88.77% 88.96% 90% Number of form submissions

22. Results – Crawler additions to LVS July 21, 2011JAZ-22Crawling the Hidden Web

23. Results – LITE Label Extraction Elements from 1 to 10 Manually analyzed to derive correct label Also ran other label extraction heuristics –Purely textual analysis –Common ways forms are laid out LITE was 93% vs 72% and 83% July 21, 2011JAZ-23Crawling the Hidden Web Total number of forms100 Number of sites from which forms were picked52 Total number of elements460 Total number of finite domain elements140 Average number of elements per form4.6 Minimum number of elements per form1 Maximum number of elements per form12

24. Relation to Class Notes Content driven Crawler –Different crawlers for different purposes Contains Similar crawler Metrics –Crawling speed –Scalability –Page importance –Freshness Data Transfer –Stored after crawled July 21, 2011JAZ-24Crawling the Hidden Web

25. Cons Freshness/Recrawling isn’t addressed Task specific, human configuration Login Based, Cookie JAR implementation Didn’t discuss Hidden fields or Capchas Didn’t run task 1 results without LITE. Not using the “name” element tag in form elements Required fields vs. not required Wild cards, incomplete forms Form element decencies. July 21, 2011JAZ-25Crawling the Hidden Web

26. Pros First Hidden Crawler Report Not run at runtime –VS. shopping and travel sites that do. Gets better overtime July 21, 2011JAZ-26Crawling the Hidden Web

27. Conclusion / Thoughts Hidden web is much bigger now. Hidden web reached now with google analytics and google ads Now we also have ajax based forms. How do we deal with ajax based forms? July 21, 2011JAZ-27Crawling the Hidden Web

28. Thank You Questions ? July 21, 2011JAZ-28Crawling the Hidden Web