Kevin C. Chang
About the collaboration -- Cazoodle
Coming next week: Vacation Rental Search
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How do you greet people in your culture?
What have you been
searching lately?
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What have you been searching lately?
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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?
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HugeLibrary.
The Web is a Big
Supermarket!
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Queries can be any things, too!
Search Engine
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Are there certain
“regularities” to exploit?
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Let’s try out…
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Survey 1: How likely does a query follow a pattern?
 9 out of 10 samples share a pattern with others!
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Survey 2: How likely do queries in a domain follow
patterns w.r.t. pre-specified attributes?
Over 28,000 manually labeled queries:
 Some domains have as high as 90+% patterned queries.
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Survey 3: How many patterns are there?
 Hundreds of patterns needed to cover 80% queries.
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Simple concept: What is Query Template?
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(this paper) Sequence of keywords and attributes
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#celebrity affairs
#category jobs in #location
#movie showtimes in #zipcode
…
(In general) Patterns that can be induced from queries
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e.g., regular expressions.
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How would such
templates be useful?
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We advocate Rich Query Interpretation.
t = “#category jobs in #location” for Job
q = “accounting jobs in chicago”
By matching query q to template t:
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1) Intent Classifier: recognize intended domain.
 q  Job
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2) Query Parser: recognize associated attributes.
 #category = “accounting”, #location = “chicago”
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Rich query interpretation is useful.
Tailored responses by query patterns:
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Finding results directly  No longer 10 blue links.
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Ranking results  Relevant to attributes desired.
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Dispatching verticals  Bring verticals into search.
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Matching ads  More likely to click.
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Query: Finding flights
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Query: Finding movie showtimes
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Query: Finding weather
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But much more patterns can be leveraged!
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Now, how to
systematically
discover such templates?
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Problem: Query Template Discovery
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Given:
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Query log L
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Domain schema D
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e.g., (#category, #location, #title) with vocabulary.
Incomplete schema can be handled, too.
Seed knowledge (queries, sites, templates, or mix)
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e.g., we use MSN query log 2006.
E.g., 5 queries; or 2 sites; or 2 templates.
Output: “Good” templates T* = {t1, t2, …}
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t1 = #location jobs
t2 = #location #category positions
……..
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Step 1:
Define quality metrics.
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How to measure quality of templates?
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Some templates are more “popular.”
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“#city1 #city2”, “#make #model”
Some templates are more “accurate.”
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“#city1 #city2 flights”, “#location #make used cars”
F
Precision:
Recall:
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Step 2:
From seeds, infer templates
with good quality.
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1) Can P and R be “inferred”? (or, estimated.)
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Probabilistic Recall:
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Probabilistic Precision:
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2) What relationships can we use to infer?
Log
Queries Q
q1: jobs in chicago
q2: jobs in boston
q3: jobs in microsoft
q4: jobs in motorola
q5: marketing jobs in motorola
q6: 401k plans
q7: illinois employment statistics
Sites S
s1: monster.com
s2: motorola.com
s3: us401k.com
Templates T
t1: jobs in #location
t2: jobs in #company
t3: #category jobs in #company
t4: #location employment statistics
QST “Quest” Graph
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s1
5
4
2
q1
q2
q3
s2
s3
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q4
1
1
q6
q7
1
t1
1
t2
1
t3
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q5
2
4
1
t4
3) How to infer on this graph?
Duality of Random Walk:
When we walk back and forth, we are inferring
precision and recall, respectively.
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R(t) is forward random walk from seeds.
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P(t) is backward random walk to seeds.
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Recall is forward random walk from seeds.
D
R0(x)
x
F
q
Iq
Iqt
t
Recall is just like (personalized) PageRank.
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Precision is backward random walk to seeds.
D
P0(x)
x
F
q
Iqt
It
t
Precision is harmonic energy minimization.
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Experimental results
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Quest is effective in finding templates by inferred P and R,
achieving 90% on actual F-measures.
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Top results:
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And they did the real work…
Ganesh Agarwal
Govind Kabra
Thank You!
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