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Crawling the Hidden Web

Crawling the Hidden Web. Sriram Raghavan Hector Garcia-Molina @ Stanford University. Introdution. What’s the problem? Current-day crawlers retrieve only Publicly Indexable Web (PIW) Why is it a problem? Large amounts of high quality information are ‘hidden’ behind search forms

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Crawling the Hidden Web

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  1. Crawling the Hidden Web Sriram Raghavan Hector Garcia-Molina @ Stanford University

  2. Introdution • What’s the problem? • Current-day crawlers retrieve only Publicly Indexable Web (PIW) • Why is it a problem? • Large amounts of high quality information are ‘hidden’ behind search forms • The hidden Web is 500 times as large as PIW

  3. Introduction (cont’d) • What’s the solution? • Design a crawler capable of extracting content from the hidden Web • A generic operational model of a hidden Web crawler, Hidden Web Exposer (HiWE) • Why is HiWE a solution?

  4. User Form Interaction

  5. Challenges and Simplifications • Challenges • Parse, process and interact with search forms • Fill out forms for submission • Simplifications • Application dependant • With user assistance • Only address content retrieval and resource discovery step is done

  6. Crawler Form Interaction

  7. Performance Metrics • Coverage Metric • Submission Efficiency • Lenient Submission Efficiency

  8. Design Issues • Internal Form Representation • Task-specific Database • Matching Function • Response Analysis

  9. HiWE Architecure

  10. HiWE – Form Representaion

  11. HiWE – Sample Forms

  12. HiWE – Task-Specific Database • Label Value-Set (LVS) Tables • Vaule Set is a fuzzy set of element values is a membership function to assign weights [0, 1] to the member of the set

  13. HiWE – Populating the LVS Table • Explicit Initialization • Built-in Entries • Wrapped Data Sources • Crawling Experience

  14. HiWE – Computing Weights • Values from explicit initialization and built-in categories have weight 1 • Values from external data sources assigned weights by wrappers [0, 1] • Values gathered by crawlers • Extract and Match the label – add new values • Extract and can not match the label – add new entries (L,V) • Can not extract the label – find closest entry and add new values

  15. HiWE – Matching Function • Enumerate values for finite domain elements • Label matching • step 1: string normalization • step 2: string matching • Evaluate value assignment • Fuzzy Conjunction • Average • Probabilistic

  16. Configuring HiWE

  17. HiWE – extraction from pages • Prune form page and only keep forms • Approximately lay-out the pruned page using a lay-out engine • Using lay-out engine to identify candidate labels to form elements • Rank each candidate and chose the best one

  18. HiWE – extraction from pages(cont’d)

  19. HiWE – Experiments

  20. HiWE – Experiments (cont’d)

  21. HiWE – Experiments (cont’d)

  22. HiWE – Experiments (cont’d)

  23. HiWE – Experiments (cont’d) 93% accuracy

  24. Future Work • Recognize and respond to the dependencies between form elements • Support partially filling-out forms

  25. Conclusion • Propose an application specific approach to hidden Web crawling • Implement a prototype crawler – HiWE • Set the stage for designing a variety of hidden Web crawlers

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