crawling deep web content through query forms

Crawling Deep Web Content Through Query

Forms

Jun Liu, Zhaohui Wu, Lu Jiang, Qinghua Zheng and Xiao Liu

Speaker: Lu JiangXi’an Jiaotong University

P.R.China

Outline

Background Related work Minimum Executable Pattern Adaptive Crawling Algorithm Experimental results Conclusions

What is the Deep Web Deep Web (or Hidden Web) refers to World Wide

Web content that is not part of the surface Web which is directly indexed by search engines.

Why the Deep Web

Data retrieval in Deep Web [Michael K. Bergman,2001]

Organizes high-quality content

Significant piece of the Web

What is the problem?

Ordinary crawlers retrieve content only in Surface Web.

Challenge: make the Deep Web accessible to web search.

A Practical solution: Deep Web crawling

Outline


Related Work The prior knowledge-based query

methods: generate queries under the guidance of

prior knowledge E.g. HIdden Web Exposer [Raghavan, 2001]

The non-prior knowledge methods generate new query by analyzing the data

records returned from the previous queries E.g. Deep Web crawler [Ntoulas, 2005]

Outline


The idea of the MEP Previous work is based on either the

genetic textbox or the entire query form. For genetic textbox: the harvest rate

(capability of obtaining new records) of queries are relatively low and simplex.

For entire form. incorrectness of filling out the entire form is excessive.

A proper granularity of pattern is required.

What is the MEP Query Form. A query form F is a query interface of

Deep Web, which can be defined as a set of all elements in it. where ei is an element of F such as a checkbox, text box or radio button.

Executable Pattern (EP). is an executable pattern if the deep web database returns the corresponding results after the query with value assignments of elements in it is issued.

Minimum Executable Pattern (MEP). Given is an executable pattern ,then it is a MEP iff any proper subset of it is not an executable pattern.

1{ , ..., }

me e

1{ , ..., }

me e

1{ ,... }nF e e

MEP Classification

Two types of the MEP. If there is an infinite domain element

(text box) in MEP set, then the MEP is called infinite domain MEP (IMEP).

If all its element are finite domain (radio button, check boxes), then the MEP is called finite domain MEP (FMEP).

What is the MEP

5 IMEPs

6 FMEPs

1 IMEP

Outline


What is a Query

The ith query to database is implemented using MEP mep and its corresponding keyword vector kv. E.g. qi(mep(keywords),”art”). The harvest rate of a query is the

ability of obtaining new records.

Overall AlgorithmPrepare to submit

the query qi.

If i<s ?

Load a set of most promising values from LVS to corresponding labels.

Using the Probabilistic Ranking Function to pick the keyword vector kv.

Kv matches any mepj in Smep

Predict the pattern harvest rate of each mepj in Smep

Estimate the keyword harvest rate of all possible (kv,mep) pair already known.

Pick out the (kv,mepj) pair which has the max value of Efficient

Return kv and mepj of qi

T F

T

F

Data Accumulation Phase

Prediction Phase

Submit queries Response results

Stage I

Form

Stage II

MEP Set

Extract records

Next query

Query Selector

predictor2

Wrapper

Form Analysis

Deep WebDatabase

Prediction information

Submit queries

sumbitter

How does a Crawler Work

q (mep(keywords),”art”).art

Obtained x new records while accessing y records.

Harvest rate = x/y.The harvest rate and extracted records are used

to evaluate query candidate.Iteration goes on until stop condition is Iteration goes on until stop condition is

satisfiedsatisfied

Overall AlgorithmPrepare to submit

the query qi.

If i<s ?

Load a set of most promising values from LVS to corresponding labels.

Using the Probabilistic Ranking Function to pick the keyword vector kv.

Kv matches any mepj in Smep

Predict the pattern harvest rate of each mepj in Smep

Estimate the keyword harvest rate of all possible (kv,mep) pair already known.

Pick out the (kv,mepj) pair which has the max value of Efficient

Return kv and mepj of qi

T F

T

F

Prediction Phase

Pattern Harvest Rate Pattern harvest rate of the mep,

depends on the pattern mep itself, rather than choice of keyword vectors. E.g. MEP(Keywords) and MEP(Abstract)

Two approaches to predict the value. Continuous prediction Weighted prediction

Keyword Vector Harvest Rate Keyword vector harvest rate represents the

conditional harvest rate of kv among all candidate keyword vectors of the given mep.

E.g. given the MEP(keywords), find out which kv will bring the most new records.

The estimation of kv harvest rate consists of two parts Calculate how many records containing kv has

been downloaded (SampleDF) Sampling Estimate how many records containing kv reside in

Deep Web (Keyword Capability) Zipf Law Keyword Vector Harvest rate = Keyword Capability

– SampleDF

1k

k k k

S aa a m

S

Convergence Analysis When to terminate crawling the Deep web

database, especially when the size of target database is unknown?

S is the record numeber of Deep Web Database

ak is the cumulated fraction of new records

mk is the fraction of records returned by the kth query

If we assume mk is constant, We have:

1/ 1 (1 )kk

ma S

S

Crawler Bottleneck!

Outline


Effectiveness

URL Size Harvest NO. of

Querieshttp://www.jos.org.cn 1,380 1,380 143

http://cjc.ict.ac.cn 2,523 2,523 13

http://www.jdxb.cn 424 424 16

http://www.paperopen.com

743,444

730,000 399

http://vod.xjtu.edu.cn 700 700 311

http://music.xjtu.edu.cn

154,000

146,967 386

0

0. 1

0. 2

0. 3

0. 4

0. 5

0. 6

0. 7

0. 8

0. 9

1 22 43 64 85 106 127 148 169 190 211 232 253 274 295 316 337 358 379 400 421 442

query number

cove

rage

of

deep

web

dat

abas

e

MEP

I DE

Comparison with state of art method

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1 19 37 55 73 91 109 127 145 163 181 199 217 235 253 271 289query number

cove

rage

of

deep

web

dat

abas

e

MEPI DE1I DE2I DE3

We believe MEP method with multi-MEP We believe MEP method with multi-MEP outperforms than that with a single one of outperforms than that with a single one of the multi-MEPthe multi-MEP

Outline


Conclusion

The novel concept of MEP provides a foundation to study Deep Web crawling through query forms.

The adaptive crawling method and its related prediction algorithm offer a efficient way to crawling Deep Web content through query forms.

Thanks You!

Appendix

Here comes the Appendix

MEP Generation Algorithm

Examples of Prediction

Comparison with LVS method

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1 10 19 28 37 46 55 64 73 82 91 100 109 118 127 136query number

cove

rage

of

deep

web

dat

abas

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MEP

Enhanced LVS

Cl assi cal LVS

Continues Prediction

The current harvest rate of a MEP totally depends on the harvest rate of the latest issued query by the MEP.

0.33 0.33 0.33

mep1 mep2 mep3Issue a query via mep1 and get 200 record

assessing 250 records

Accessing new record rate = 200/250 = 0.8

mep1 = 0.8/(0.33+0.33+0.8) = 0.55

mep2 = 0.33/(0.33+0.33+0.8) = 0.22

mep3 = 0.33/(0.33+0.33+0.8) = 0.22

0.55 0.22 0.22

Issue a query via mep1 and get 30 record assessing 100 records

Accessing new record rate = 30/100 = 0.3

mep1 = 0.3/(0.22+0.22+0.3) = 0.40

mep2 = 0.22/(0.22+0.22+0.3) = 0.29

mep3 = 0.22/(0.22+0.22+0.3) = 0.290.40 0.29 0.29

Weighted Prediction

The current harvest rate of a MEP depends on all its previous harvest rates of issued query by the MEP.

SampleDF Calculation document frequency of observed keyword

vector kv in sample croups {d1,...,ds}.

where kvxk is the corresponding Boolean vector of kv in dk, and similarly mepx is the Boolean vector of mep.

ith dimension of vector kv contains in document corresponding dimension of vector kvx is assigned to 1. 0 otherwise;

ith dimension of mep is infinite domain mep then the corresponding position is assigned to 1. 0 otherwise.

SampleDF Calculation Example

kx = (a,b) mep = (student id, exam id, subject)

Four documents D1,D2,D3 and D4 D1 has both Student ID a and Exam

ID b D2 has only Student ID a D3 has only Exam ID b D4 has neither Student ID a and

Exam ID b mepx = (1,1,0) D1 kvx1 (1,1,0) cos<(1,1,0),(1,1,0)> = 1 D2 kvx2 (1,0,0) cos<(1,0,0),(1,1,0)> = 0.707 D3 kvx3 (0,1,0) cos<(0,1,0),(1,1,0)> = 0.707 D4 kvx4 (0,0,0) cos<(0,0,0),(1.1.0)> = 0 SampleDF((a,b)| mep) = 1+0.707+0.707+0 = 2.414

Keyword Capability Estimation

Keyword capability denote capability of obtaining records. (differ from harvest rate)

|Dt| is Cartesian product of values of finite element in MEP

For FMEP: f = 1

For IMEP: Zipf-Mandelbrot Law to estimate f

Keyword capability

= 1

| |n

t

t

f

D

crawling deep web content through query forms

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