HAMSTER: Using Search Clicklogs for Schema and Taxonomy Matching

HAMSTER: USING SEARCH CLICKLOGS FOR SCHEMA AND TAXONOMY MATCHING

Arnab Nandi Phil BernsteinUNIV OF MICHIGAN MICROSOFT RESEARCH

PRESENTED BYVAIBHAV MEHTA

Scenario

Arnab Nandi & Phil Bernstein

2

Scenario


3

Search over structured dataCommerceentertainment

Data onboarding – merge an XML data feed from a 3rd partyto Microsoft data warehouse.

Scenario


4

query

Search engine + data warehouse

Users

3rd Party Feed

3rd Party Feed

3rd Party Feed

3rd Party Feed

results

“Amazon.com”

•High Precision•(Irrespective of Recall)

•Minimal Human Involvement

•High Precision•(Irrespective of Recall)

•Minimal Human Involvement

Example Feed

-<Movie> <Title Key="Yes">Indiana Jones and The Kingdom of The Crystal Skull</Title> <Release Key="Yes">2008</Release> <Description>Ever…</Description> <RunTime>127</RunTime><Categories>

<Category>Action</Category> <Category>Comedy</Category>

</Categories> <MPAA>PG-13</MPAA> <SiteUrl>http://www.indianajones.com/site/index.html</SiteUrl>-<Persons> <Person Role="Actor" Character="Indiana Jones">Harrison Ford</Person>-</Persons> </Movie>

Warehouse: Movies (Host) 3rd Party Movie Site (Foreign)

<MOVIE> <MOVIE_ID>57590</MOVIE_ID> <MOVIE_NAME>Indiana Jones and the Kingdom of

the Crystal Skull</MOVIE_NAME> <RUNTIME>02:00</RUNTIME> <GENRE1>Action/Adventure</GENRE1> <GENRE2/> <MPAA>NR</MPAA> <ADVISORY/> <URL>http://www.indianajones.com/</URL> <ACTOR1>Harrison Ford</ACTOR1> <ACTOR2>Karen Allen</ACTOR2></MOVIE>

5


Schema Matching






the Crystal Skull</MOVIE_NAME> <RUNTIME>02:00</RUNTIME> <GENRE1>Action/Adventure</GENRE1> <GENRE2/> <RATING>NR</RATING> <ADVISORY/> <URL>http://www.indianajones.com/</URL> <ACTOR1>Harrison Ford</ACTOR1> <ACTOR2>Karen Allen</ACTOR2></MOVIE>

6


Taxonomy Matching






the Crystal Skull</MOVIE_NAME> <RUNTIME>02:00</RUNTIME> <GENRE1>Action/Adventure</GENRE1> <GENRE2/> <RATING>NR</RATING> <ADVISORY/> <URL>http://www.indianajones.com/</URL> <ACTOR1>Harrison Ford</ACTOR1> <ACTOR2>Karen Allen</ACTOR2></MOVIE>

7


Various Problems8

Badly normalized….

Unit conversion…

Formatting choices…

In-band signaling…

Arbitrary labels


Non standard vocabulary / language

Zero documenta

tion

Not enough

instances

Unlike conventional matching…


9

We have web search click data

For both Warehouse & 3rd party website

The databases we are integrating (usually) have a presence on the web

Why not use click data as a feature for schema & taxonomy matching?

query

Search engine + data warehouse

Users

3rd Party Feed

results

Outline10

Scenario

Using Clicklogs Core idea Using Query Distributions Example System Architecture

Results


Core idea11

“If two (sets of) products are searched for by similar queries, then they are similar”

Small laptop

Arnab Nandi & Phil BernsteinWeb Search

Clicklog

Core idea12


Small Lapto

ps

Pro. Laptops

Warehouse

eee ::: small

laptopsSmall laptop

Small laptop

Y

X

Z

Small laptop

Query Distributions


13

click count

Mapping to Taxonomy14

Map URL to product, which belongs to taxonomy

http://www.amazon.com/dp/B001JTA59C

Shopping | Electronics |Netbooks Arnab Nandi & Phil Bernstein

3rd party DB(provided to us)

Aggregating Query Distributions

15


Small Laptop

s

Pro. Laptops

Warehouse

eee ::: small

laptops

Generating Correspondences

Goal: To match two schema elements (or categories), determine if they have the same distribution of queries searching for them.

Process For each page (URL)

Identify query distribution Identify category / schema element of that page

For each category / schema element C Aggregate over pages in C to get query distribution

For each foreign category / schema element Find host category / schema element with most similar query

distribution

17


Outline18

Scenario


Results


Example: Taxonomy Matching


19

query freq url

laptop 70http://searchengine.com/product/macbookpro

laptop 25http://searchengine.com/product/mininote

laptop 5 http://asus.com/eeepcnetbook 5

http://searchengine.com/product/macbookpro

netbook 20

http://searchengine.com/product/mininote

netbook 15 http://asus.com/eeepccheap netbook 5 http://asus.com/eeepc

Warehouse: Small

Laptops

Warehouse: Professional

Laptops

eee



20

“laptop”: 25/45“netbook”: 20/45

“laptop” : 70 / 75“netbook” : 5/75

“laptop”: 5/25“netbook”: 15/25“cheap laptop”:

5/25

Warehouse: Small

Laptops


Laptops

eee

Distribution Similarity Metric


21

Jaccard(qhost, qforeign) ✕ MinFreq(qhost, qforeign)Σ(all qhost, qforeign combinations)

“small laptops” vs “eee”laptop vs laptop netbook vs netbook laptop vs cheap laptop

1 x (5/25) + 1 x (20/45) + 0.5 x (5/25)

= 0.74



22

Warehouse: Small

Laptops


Laptops

eee

“laptop”: 25/45“netbook”: 20/45

“laptop” : 70 / 75“netbook” : 5/75

“laptop”: 5/25“netbook”: 15/25“cheap laptop”:

5/25

0.74

0.31

Advantages of Clicklogs


23

Resilient to language

Resilient to new domains, data, and features As long as people query & click, we have data to

learn from

Generates mappings previous methods can’t Electronics ▷ Electronics Features ▷ Brands ▷ Texas Instruments

≈ Office Products ▷ Office Machines ▷ Calculators

Software ▷ Categories ▷ Programming ▷ Programming Languages ▷ Visual Basic ≈ Software ▷ Developer Tools

System Design24


Outline25

Scenario


Results


Experimenting with Click Logs


26

Commercial warehouse mapping, 258 products from a 70,000 term Amazon.com taxonomy (613

in gold) to a 6,000 term warehouse taxonomy (40 in gold)

Live.com (now Bing.com) search querylog Amazon to warehouse mapping task,

consecutively halving the clicklog size used 1.8 million clicks to Amazon.com product pages Typically each product had a query distribution

averaging 13 unique (i.e., different) search queries (min 1, max 181, stdev 22).

Summary of Results


27

90% precision / recall possible

Bigger clicklogs imply better recall

Technique isn't very sensitive to similarity metric

Precision / Recall


28

Commercial warehouse mapping, 258 products

from a 70K term Amazon.com taxonomy to a 6,000 term warehouse taxonomy (613

categories used)

Summary of Results


29




Varying Clicklog Size30

Successively decreased clicklog size by half

Recall decreases as clicklog size is decreased


Summary of Results


31




Comparing Query Distributions

32

Jaccard(qhost, qforeign) ✕ MinFreq(qhost, qforeign)

Σ(all qhost, qforeign combinations)

Replace Jaccard with various phrase similarity metrics

Minimal difference due to size of most queries Arnab Nandi & Phil Bernstein

Summary of Results


33


Query distribution is a good similarity metric



Conclusion

Unsupervised mapping is possible very high recall / precision when enough

queries are present

Click logs are promising Finds results that other methods cannot find As clicklog size increases, it will produce

more mappings

Combinable with existing methods

34


Questions?


HAMSTER: Using Search Clicklogs for Schema and Taxonomy Matching

Documents

Transcript of HAMSTER: Using Search Clicklogs for Schema and Taxonomy Matching