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Personalized Web Information Extraction and Management Strategies

Explore the intersection of AI, ML, DB, and HCI for extracting and managing web information efficiently. Learn about tools like Google Scholar and techniques for structured content creation. Discover the importance of personalization in information management.

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Personalized Web Information Extraction and Management Strategies

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  1. CSE 574 Extracting, Managing & Personalizing Web Information • Staffing • Dan Weld • Raphael Hoffmann • Content • Intersection of AI, ML, DB & HCI • Student Responsibilities • Reading, Reports, Discussion • Project (for those taking 3 credits)

  2. Class Focus Extracting, Managing & Personalizing Web Information

  3. Why Information Extraction • Next-Generation Search • Citeseer, Google scholar, MSRA Libra • Google product search • Flipdog • Zvents • Zoominfo • Question Answering

  4. People

  5. …Continued

  6. …Continued Some More

  7. Making Structured Content • Information Extraction • E.g. Google Scholar • Cons: Noisy • Communal Content Creation • E.g. Wikipedia • Cons: Bootstrapping & Incentives

  8. Why Managing ? • Select • Store, Index, Aggregate • Search, Query, Explore • Share, Collaborate, “Publish” Example: Personalized Portals cf DBlife, Rexa, Dontcheva UIST-07

  9. DBlife

  10. Summaries - 1

  11. Summaries - 2

  12. Summaries - 3

  13. Summaries - 4

  14. Summaries - 5

  15. Summaries - 6

  16. Why Personalize? • Because we can.

  17. Preliminary Schedule • Information Extraction • Traditional Machine Learning Approaches • Self-Supervised Methods • Other Issues: Coreference & Ontology • Collaborative Content Creation & UI Issues • Applying Contraints from Interaction to Learning • Decision Theoretic Interaction • Faceted Interfaces • Community Information Management • Extraction over Evolving Text • Data Provenance • Mashups & Personalized Web • Next-Generation Search • Inference, Textual Entailment, Machine Reading • Entity Search

  18. For next time • Read • Agichtein, Gravano. Snowball: Extracting Relations from Large Plain-Text Collections. • Add yourself to mailing list • Look at papers on website wiki • Add new ones • Add summary (different from report) • Notate if you wish to present one • Think about project / (form a group?)

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