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Deep-Web Crawling and Related Work Matt Honeycutt CSC 6400.

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1 Deep-Web Crawling and Related Work Matt Honeycutt CSC 6400

2 Outline Basic background information Google’s Deep-Web Crawl Web Data Extraction Based on Partial Tree Alignment Bootstrapping Information Extraction from Semi- structured Web Pages Crawling Web Pages with Support for Client-Side Dynamism DeepBot: A Focused Crawler for Accessing Hidden Web Content

3 Background Publicly-Indexable Web (PIW) –Web pages exposed by standard search engines –Pages link to one another Deep-web –Content behind HTML forms –Database records –Estimated to be much larger than PIW –Estimated to be of higher quality than PIW

4 Google’s Deep-Web Crawl J. Madhavan D. Ko L. Kot V. Ganapathy A. Rasmussen A. Halevy

5 Summary Describes process implemented by Google Goal is to ‘surface’ content for indexing Contributions: –Informativeness test –Query selection techniques and algorithm for generating appropriate text inputs

6 About the Google Crawler Estimates that there are ~10 million high-quality HTML forms Index representative deep-web content across many forms, driving search traffic to the deep- web Two problems: –Which inputs to fill in? –What values to use?

7 Example Form

8 Query Templates Correspond to SQL-like queries: select * from D where P First problem is to select the best templates Second problem is to select the best values for those templates Want to ignore presentation-related fields

9 Incremental Search for Informative Query Templates Classify templates as either informative or uninformitive Template is informative if it generates sufficiently distinct pages from other templates Build more complex templates from simpler informative ones Signatures computed for each page

10 Informativeness Test T is informative if: Heuristically limit to templates with 10,000 or fewer possible submissions and no more than 3 dimensions Can estimate informativeness using a sample of possible queries (ie: 200)

11 Results

12 Observations URLs generated for larger templates are not as useful ISIT Generates far fewer URLs than CP but still has high coverage Most common reason for inability to find informative template: JavaScript –Ignoring JavaScript errors, informative templates found for 80% of forms tested

13 Generating Input Values Text boxes may be typed or untyped Special rules for small number of typed inputs that are common Can’t use generic lists, best keywords are site specific Select seed keywords from form, then iterate and select candidate keywords from results using TF-IDF Results are clustered and representative keywords are chosen for each cluster, ranked by page length Once candidate keywords have been selected, treat text inputs as select inputs

14 Identifying Typed Inputs

15 Conclusions Describes the innovations of “the first large-scale deep-web surfacing system” Results are already integrated into Google Informativness test is a useful building block No need to cover individual sites completely Heuristics for common input types are useful Future work: support for JavaScript and handling dependencies between inputs Limitation: only supports GET requests

16 Web Data Extraction Based on Partial Tree Alignment Yanhong Zhai Bing Liu

17 Summary Novel technique for extracting data from record lists: DEPTA (Data Extraction based on Partial Tree Alignment) Automatically identifies records and aligns their fields Overcomes limitations of existing techniques

18 Example

19 Approach Step 1: Build tag tree Step 2: Segment page to identify data regions Step 3: Identify data records within the regions Step 4: Align records to identify fields Step 5: Extract fields into common table

20 Building the Tag Tree and Finding Data Regions Computes bounding regions for each element Associate items to parents based on containment to build tag tree Next, compare tag strings with edit distance to find data regions Finally, identify records within regions

21 Identifying Regions

22 Partial Tree Alignment Tree matching is expensive Simple Tree Matching – faster, but not as accurate Longest record tree becomes seed Fields that don’t match are added to seed Finally, field values extracted and inserted into table

23 Seed Expansion

24 Conclusions Surpasses previous work (MDR) Capable of extracting data very accurately –Recall: 98.18% –Precision: 99.68%

25 Bootstrapping Information Extraction from Semi-structured Web Pages A. Carlson C. Schafer

26 Summary Method for extracting structured records from web pages Method requires very little training and achieves good results in two domains

27 Introduction Extracting structured fields enables advanced information retrieval scenarios Much previous work has been site-specific or required substantial manual labeling Heuristic-based approaches have not had great success Uses semi-supervised learning to extract fields from web pages User only has to label 2-5 pages for each of 4-6 sites

28 Technical Approach Human specifies domain schema Labels training records from representative sites Utilizes partial tree alignment to acquire additional records for each site New records are automatically labeled Learns regression model that predicts mappings from fields to schema columns

29 Mapping Fields to Columns Calculate score between each field and column Score based on field contexts and contexts observed in training Most probable mapping above a threshold is accepted

30 Example Context Extraction

31 Feature Types Precontext 3-grams Lowercase value tokens Lowercase value 3-grams Value token type categories

32 Example Features

33 Scoring Field mappings based on comparing feature distributions –Distribution computed from training contexts –Distribution computed from observed contexts Completely dissimilar field/column pairs are fully divergent –Exact field/column pairs have no divergence Feature similarities combined using “stacked” linear regression model Weights for the model are learned in training

34 Results

35 Crawling Web Pages with Support for Client-Side Dynamism Manuel Alvarez Alberto Pan Juan Raposo Justo Hidalgo

36 Summary Advanced crawler based on browser automation NSEQL - Language for specify browser actions Stores URLs and path back to URL

37 Limitations of Typical Crawlers Built on low-level HTTP APIs Limited or no support for client-side scripts Limited support for sessions Can only see what’s in the HTML

38 Their Crawler’s Features Built on “mini web browsers” – MSIE Browser Control Handles client-side JavaScript Routes fully support sessions Limited form-handling capabilities


40 Identifying New Routes Routes can come from links, forms, and JavaScript ‘href’ attributes extracted from normal anchor tags Tags with JavaScript click events are identified and “clicked” Captures actions and inspects them

41 Results and Conclusions Large scale websites are crawler-friendly Many medium-scale, deep-web sites aren’t Crawlers should handle client-side script Presented crawler has been applied to real- world applications

42 DeepBot: A Focused Crawler for Accessing Hidden Web Content Manuel Alvarez Juan Raposo Alberto Pan

43 Summary Presents a focused deep-web crawler Extension of previous work Crawls links and handles search forms

44 Architecture

45 Domain Definitions Attributes a1…aN Each attribute has name, aliases, specificity index Queries q1…qN Each query contains 1 or more (attribute,value) pairs Relevance threshold

46 Example Definition

47 Evaluating Forms Obtains bounding coordinates of all form fields and potential labels Distances and angles computed between fields and labels

48 Evaluating Forms If label l is within min-distance of field f, l is added to f’s list –Ties are broken using angle Lists are pruned so that labels appear in only one list and all fields have at least one possible label

49 Evaluating Forms Text similarity measures used to link domain attributes to fields Computes relevance of form If form score exceeds relevance threshold, DeepBot executes queries

50 Results and Conclusions Evaluated on three domain tasks: book, music, and movie shopping Achieves very high precision and recall Errors due to: –Missing aliases –Forms with too few fields to achieve minimum support –Sources that did not label fields

51 Summary of Deep Web Crawling Several challenges must be addressed: –Understanding forms –Handling JavaScript –Determining optimal queries –Identifying result links –Extracting metadata Most of the pieces exist

52 Questions?

53 References Madhavan, J., Ko, D., Kot, Ł., Ganapathy, V., Rasmussen, A., and Halevy, A. 2008. Google's Deep Web crawl. Proc. VLDB Endow. 1, 2 (Aug. 2008), 1241-1252. Zhai, Y. and Liu, B. 2005. Web data extraction based on partial tree alignment. In Proceedings of the 14th international Conference on World Wide Web (Chiba, Japan, May 10 - 14, 2005). WWW '05. ACM, New York, NY, 76-85 Carlson, A. and Schafer, C. 2008. Bootstrapping Information Extraction from Semi- structured Web Pages. In Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I (Antwerp, Belgium, September 15 - 19, 2008). Manuel Álvarez, Alberto Pan, Juan Raposo, Justo Hidalgo. Crawling Web Pages with Support for Client-Side Dynamism. Proceedings of the 7th International Conference, Advances in Web-Age Information Management (WAIM 2006). Lecture Notes in Computer Science. Edited by Jeffrey Xu Yu, Masaru Kitsuregawa, Hong Va Leong. Published by Springer-Verlag Berlin. ISSN: 0302-9743, ISBN-10: 3-540-35225-2, ISBN-13: 978-3-540- 35225-9. Vol. 4016, pp. 252-262. Hong Kong, China. June 17-19, 2006. Álvarez, M., Raposo, J., Pan, A., Cacheda, F., Bellas, F., and Carneiro, V. 2007. DeepBot: a focused crawler for accessing hidden web content. In Proceedings of the 3rd international Workshop on Data Enginering Issues in E-Commerce and Services: in Conjunction with ACM Conference on Electronic Commerce (EC '07) (San Diego, California, June 12 - 12, 2007). M. Hepp, M. Sayal, S. Lee, J. Lee, and J. Shim, Eds. DEECS '07, vol. 236. ACM, New York, NY, 18-25.

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