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Extracting, Managing, and Personalizing Web Information: AI, ML, and Data Interaction

CSE 574 focuses on the intersection of AI, Machine Learning, Database systems, and Human-Computer Interaction. The course explores information extraction and management to develop personalized web information systems. Students will engage in readings, discussions, and report writing, culminating in a project for those seeking 3 credits. Topics include next-generation search technologies, communal content creation, and personalized user interfaces. Participants will analyze methods like traditional machine learning, self-supervised methods, and collaboration in dynamic information environments.

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Extracting, Managing, and Personalizing Web Information: AI, ML, and Data Interaction

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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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