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Automating the Extraction of Domain Specific Information from the Web

Automating the Extraction of Domain Specific Information from the Web. A Case Study for the Genealogical Domain Troy Walker Thesis Proposal January 2004. Research funded by NSF. Genealogical Information on the Web. Hundreds of thousands of sites

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Automating the Extraction of Domain Specific Information from the Web

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  1. Automating the Extraction of Domain Specific Information from the Web A Case Study for the Genealogical Domain Troy Walker Thesis Proposal January 2004 Research funded by NSF

  2. Genealogical Information on the Web • Hundreds of thousands of sites • Some professional (Ancestry.com, Familysearch.org) • Mostly hobbyist (203,200 indexed by Cyndislist.com) • Search engines • “Walker genealogy” on Google: 199,000 results • 1 page/minute = 5 months to go through • Why not enlist the help of a computer?

  3. Problems • No standard way of presenting data • Text formatted with HTML tags • Tables • Forms to access information • Sites have differing schemas

  4. Proposed Solution • Based on Ontos and other work done by the BYU Data Extraction Group (DEG) • Able to extract from: • Single-Record or Multiple Record Documents • Tables • Forms • Scalable and robust to changes in pages • Easily adaptable to other domains

  5. Text

  6. Tables

  7. Forms

  8. Forms

  9. URL List Single- or Multiple-Record Engine Document Retriever and Structure Recognizer User Query URL Selector Table Engine Data Constrainer Result Filter Result Presenter Form Engine Ontology System Overview

  10. User Query • Generated from ontology • Generated once per application domain

  11. User Query

  12. URL Listand URL Selector • Contains Genealogy URLs • Search each URL—too much time • Select likely URLs • Distribute document processing using DOGMA

  13. URL Listand Document Retriever

  14. Document Structure Recognizer • Requests analysis from each Data Extraction Engine • Selects appropriate method

  15. Data Extraction Engines • Text • Improved record-separation • Ability to handle single-record pages • Table • Forms

  16. Data Constrainer • Selects attribute/value pairs • Fits data to ontology

  17. Result Filter • Fits data to query • Returns to central Result Presenter

  18. Result Presenter • Creates XML Schema from Ontology • Presents results to user

  19. Result Presenter

  20. Evaluation • Scalability • Query on large URL list • Experiment on number of PCs • Precision and recall • Recall difficult to determine • Query on small URL list • Adaptability • Car ontology • Small URL list

  21. Conclusion • Integrates, builds on previous DEG work • Extracts from: • Single- or Multiple-Record Documents • Tables • Forms • Scalable • Only searches probable pages • Distributed with DOGMA • Robust to changes in pages • Ontology based—easily adapted to other domains

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