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6/14/2015 8:20 PM1 CSE 574 Extracting, Managing & Personalizing Web Information Staffing –Dan Weld –Raphael Hoffmann Content –Intersection of AI, ML, DB.

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Presentation on theme: "6/14/2015 8:20 PM1 CSE 574 Extracting, Managing & Personalizing Web Information Staffing –Dan Weld –Raphael Hoffmann Content –Intersection of AI, ML, DB."— Presentation transcript:

1 6/14/2015 8:20 PM1 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 6/14/2015 8:20 PM2

3 Why Information Extraction Next-Generation Search –Citeseer, Google scholar, MSRA Libra –Google product search –Flipdog –Zvents –Zoominfo Question Answering 6/14/2015 8:20 PM3

4 4 CiteSeer vs. Scholar

5 6/14/2015 8:20 PM5

6 People 6/14/2015 8:20 PM6

7 …Continued 6/14/2015 8:20 PM7

8 …Continued Some More 6/14/2015 8:20 PM8

9 Making Structured Content Information Extraction –E.g. Google Scholar –Cons: Noisy Communal Content Creation –E.g. Wikipedia –Cons: Bootstrapping & Incentives 6/14/2015 8:20 PM9

10 Why Managing ? Select Store, Index, Aggregate Search, Query, Explore Share, Collaborate, “Publish” Example: Personalized Portals cf DBlife, Rexa, Dontcheva UIST-07 6/14/2015 8:20 PM10

11 DBlife 6/14/2015 8:20 PM11

12 Summaries - 1 6/14/2015 8:20 PM12

13 Summaries - 2 6/14/2015 8:20 PM13

14 Summaries - 3 6/14/2015 8:20 PM14

15 Summaries - 4 6/14/2015 8:20 PM15

16 Summaries - 5 6/14/2015 8:20 PM16

17 Summaries - 6 6/14/2015 8:20 PM17

18 Why Personalize? Because we can. 6/14/2015 8:20 PM18

19 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 6/14/2015 8:20 PM19

20 6/14/2015 8:20 PM20 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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