1. Kevin C. Chang

2. About the collaboration -- Cazoodle 2 Coming next week: Vacation Rental Search

3. 3 How do you greet people in your culture? What have you been searching lately?

4. 4 The university and areas 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 SIGMOD 2007? Due date of SIGMOD 2008? Sale price of “Canon PowerShot A400”? “Hamlet” books available at bookstores?

5. 5 The Web is a Big Library. Huge Supermarket!

6. 6 Queries can be any things, too! Search Engine

7. 7 Are there certain “regularities” to exploit?

8. Let’s try out… 8

9. Survey 1: How likely does a query follow a pattern? 9  9 out of 10 samples share a pattern with others!

10. Survey 2: How likely do queries in a domain follow patterns w.r.t. pre-specified attributes? 10 Over 28,000 manually labeled queries:  Some domains have as high as 90+% patterned queries.

11. Survey 3: How many patterns are there? 11  Hundreds of patterns needed to cover 80% queries.

12. Simple concept: What is Query Template ? (this paper) Sequence of keywords and attributes  #celebrity affairs  #category jobs in #location  #movie showtimes in #zipcode  … (In general) Patterns that can be induced from queries  e.g., regular expressions. 12

13. 13 How would such templates be useful?

14. We advocate Rich Query Interpretation. t = “#category jobs in #location” for Job q = “accounting jobs in chicago” By matching query q to template t: 1) Intent Classifier : recognize intended domain.  q  Job 2) Query Parser : recognize associated attributes.  #category = “accounting”, #location = “chicago” 14

15. Rich query interpretation is useful. Tailored responses by query patterns: Finding results directly  No longer 10 blue links. Ranking results  Relevant to attributes desired. Dispatching verticals  Bring verticals into search. Matching ads  More likely to click. 15

16. Query: Finding flights 16

17. Query: Finding movie showtimes 17

18. Query: Finding weather 18

19. But much more patterns can be leveraged! 19

20. 20 Now, how to systematically discover such templates?

21. Problem: Query Template Discovery Given:  Query log L e.g., we use MSN query log 2006.  Domain schema D e.g., ( #category, #location, #title ) with vocabulary. Incomplete schema can be handled, too.  Seed knowledge (queries, sites, templates, or mix) E.g., 5 queries; or 2 sites; or 2 templates. Output: “Good” templates T* = { t1, t2, … }  t1 = #location jobs  t2 = #location #category positions  …….. 21

22. 22 Step 1: Define quality metrics.

23. How to measure quality of templates? Some templates are more “popular.”  “#city1 #city2”, “#make #model” Some templates are more “accurate.”  “#city1 #city2 flights”, “#location #make used cars” 23  Precision : Recall :

24. 24 Step 2: From seeds, infer templates with good quality.

25. 1) Can P and R be “inferred”? (or, estimated.) Probabilistic Recall: Probabilistic Precision: 25

26. Sites S s 1 : monster.com s 2 : motorola.com s 3 : us401k.com Queries Q q 1 : jobs in chicago q 2 : jobs in boston q 3 : jobs in microsoft q 4 : jobs in motorola q 5 : marketing jobs in motorola q 6 : 401k plans q 7 : illinois employment statistics Templates T t 1 : jobs in #location t 2 : jobs in #company t 3 : #category jobs in #company t 4 : #location employment statistics t1t1 t2t2 t3t3 t4t4 q1q1 q2q2 q3q3 q4q4 q5q5 q6q6 q7q7 s1s1 s2s2 s3s3 10 2 5 12 4 4 2 4 1 1 1 1 1 1 1 2) What relationships can we use to infer? LogQST “Quest” Graph

27. 3) How to infer on this graph? Duality of Random Walk: When we walk back and forth, we are inferring precision and recall, respectively. R(t) is forward random walk from seeds. P(t) is backward random walk to seeds. 27

28. Recall is forward random walk from seeds. 28 t q x IqIq I qt D R0(x)R0(x)  Recall is just like (personalized) PageRank.

29. Precision is backward random walk to seeds. 29  Precision is harmonic energy minimization. t q x ItIt I qt D P0(x)P0(x)

30. Experimental results Quest is effective in finding templates by inferred P and R, achieving 90% on actual F-measures. Top results: 30

31. 31 Thank You! And they did the real work… Ganesh AgarwalGovind Kabra