1. Entity Search Are you searching for what you want? Kevin C. Chang Joint work with: Bin He, Zhen Zhang, Chengkai Li, Govind Kabra, Shui-Lung Chuang, Joe Kelley, Tao Cheng, Bill Davis, Mitesh Patel, Dave Killian

2. 2 What have you been reading lately? Let’s start with the new universal greeting… What have you been searching lately?

3. 3 From the MetaQuerier to WISDM: I am becoming superficial… Access Structure   Deep Web Surface Web Kevin’s 2 projects in the 4-quardants:

4. 4 First Question: Where is U. of Illinois? Can we search it?

5. 5 What have you been searching lately? The university and area of Kevin Chang? The email of Marc Snir? Customer service phone number of Amazon? What profs are doing databases at UIUC? The papers and presentations of ICDE 2007? Due date of SIGMOD 2007? Sale price of “Canon PowerShot A400”? “Hamlet” books available at bookstores?

6. 6 Are we searching for what we want? Challenge of the surface Web: Despite all the glorious search engines…

7. 7 What you search is not what you want.

8. 8 Function follows view: What is “the Web”? Or: How do search engines view the Web?

9. 9 They say: Web is a corpus of PAGES.

10. 10 We take an entity view of the Web:

11. 11 What is an “entity”? Your target of information– or, anything. Phone number Email address PDF Image Person name Book title, author, … Price (of something)

12. 12 From pages to entities Traditional SearchEntity Search

13. 13 Demo. We build Ver. 0.1, to understand the promises and issues. Three scenarios:  Academic: CS sites, DBLP homepages.  ECommerce: Books, Cellphones.  Yellowpage: Comprehensive corpus.

14. 14 Special Thanks: Data from Stanford WebBase.

15. 15 Example application: Question answering Q: Who are DB profs at UIUC? WIS DM query: #dtf-nnuw100(#entity(professor) #entity(university) #entity(research Database Systems, Data Mining, IR)) results: ranked list of (, ) Query Generation Querying Filtering & Validation A: Geneva Belford, Kevin C. Chang, AnHan Doan, Jiawei Han, Marianne Winslett, ChengXiang Zhai

16. 16 Example application: Relation construction … winslett@cs.uiuc.edu 333-3536 Marianne Winslett dewitt@cs.wisc.edu608-263-5489David DeWitt emailphone prof WIS DM tagging: #entity(prof) query: #tf-nnow50(#entity(professor) #tf-nnuw20(#entity(email) #entity(phone))) results: ranked list of (, ) App-specific Entity Tagging Querying Relation Construction

17. 17 Example application: Best-effort integration Price of “Hamlet”? WIS DM query: #od50(#entity(title Hamlet) #entity(price)) results: ranked list of (, ) Buy.com: $ $10.99, Amazon.com: $12.00 … Query Generation Querying Validation & Ranking

18. 18 How different is “ entity search ”? How to define such searches?

19. 19 Why is Entity Search different… Probabilistic entities  v.s. A page is for sure a page. Contextual patterns  v.s. Match a page by its content. Holistic Aggregates  v.s. A page occurs only once. Associative results  v.s. We never search for pairs of pages.

20. 20 Consider the entire process: Page Retrieval 1. Input : pages. 2. Criteria : content keywords. 3. Scope : Each page itself. 4. Output : one page per result. Marc Snir Marc Snir

21. 21 Entity search is thus different… Entity Search 1. Input : probabilistic entities. 2. Criteria : contextual patterns. 3. Scope : holistic aggregates. 4. Output : associative results.

22. 22 What are technical challenges? Or, how to write (reviewer-friendly) papers?

23. 23 Issue #1. EntityRank : How to rank entities? Say, Jiawei Han with #email, #phone, #researcharea Entity matters  Is “jhan@” an email? Is “2-3457” a phone? Context matters:  Order, distance Frequency matters:  How often is Jiawei Han – “data mining”? Associativity matters:  “webmaster@cs.uiuc.edu”  “algorithm” Source matters:  Where did you get this info from?

24. 24 Issue #2: Query Processing: How to optimize?  phone tf  #entity(professor)  prof=“…” “fax”-#entity(phone) nnow50 Q: #tf-nnow50(#entity(professor[David DeWitt]) fax #entity(phone)) (pre-materialized context index)

25. 25 Conclusion: One step at a time towards … IntegrationMining Search surface deep What You Search Is What You Want!

26. 26 Thank You! Chengkai LiZhen Zhang ShuiLung Chuang Tao ChengGovind Kabra And the warriors behind … Arpit Jain Amit BehalDavid Killian Yuping Tseng Hanna ZhongNgoc BuiSonia Jahid Aniruddh NathPaul YuanRaj Sodhi Quoc Le Hemanta MajiSung-Eun Kim

27. 27 Thank You! Chengkai LiZhen Zhang ShuiLung Chuang Tao ChengGovind KabraArpit Jain Amit BehalDavid Killian Yuping Tseng Hanna ZhongNgoc BuiSonia Jahid Aniruddh NathPaul YuanRaj Sodhi Quoc Le Hemanta MajiSung-Eun Kim And the warriors behind …