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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)

Class Focus

Extracting, Managing & Personalizing Web Information

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Why Information Extraction• Next-Generation Search

– Citeseer, Google scholar, MSRA Libra– Google product search– Flipdog– Zvents– Zoominfo

• Question Answering

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People

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…Continued

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…Continued Some More

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Making Structured Content • Information Extraction

– E.g. Google Scholar– Cons: Noisy

• Communal Content Creation– E.g. Wikipedia– Cons: Bootstrapping & Incentives

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Why Managing ?• Select• Store, Index, Aggregate• Search, Query, Explore• Share, Collaborate, “Publish”

Example: Personalized Portalscf DBlife, Rexa, Dontcheva UIST-07

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DBlife

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Summaries - 1

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Summaries - 2

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Summaries - 3

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Summaries - 4

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Summaries - 5

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Summaries - 6

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Why Personalize?• Because we can.

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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

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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?)