Crawling the Hidden Web

Crawling the Hidden Web

Authors: Sriram Raghavan

Hector Gracia-Molina

Presented by: Jorge Zamora

Outline• Hidden Web• Crawler Operation Model• HiWE – Hidden Web Exposer• LITE – Layout-based Information Extraction• Experimental Results• Relation to class lectures• Pros/Cons• Conclusion

July 21, 2011 JAZ-2Crawling the Hidden Web

Hidden Web• PIW – Publicly Indexable Web• Deep Web

– 500 times the PIW

• Hidden Crawler– Parse, process and interact with forms

• Task specific approach• Two Steps

– Resource Discovery– Content Extraction


Hidden Crawler – Operation Model


Hidden Crawler – Operation Model • Internal form representation

F = ({{E1, E2,…,En},S,M})

• Task specific database– Formulates search queries

• Matching FunctionMatch(({E1,…,En},S,M),D) = {[E1<-v1,…,En<- Vn]}.

• Response Analysis– Success and error pages, Storage, Tuning


Hidden Crawler – Performance• Challenge

– Wanted to get away from a metric significantly depended on D

• Submission Effiency– Ntotal = total number of forms crawler submits– SEstrict = Nsucess/Ntotal

• Penalizes the crawler which might be correct but did not yield any results

– SElenient = Nvalid/NTotal• Penalized only if the form submission is semantically incorrect.

• Difficult to evaluate - must evaluate every form submission.


HiWE • Hidden Web Exposer• Prototype Hidden Web Crawler built at Stanford• Basic idea

– extract some kind of descriptive information or label for each element in the form

– task-specific which contains a finite set of categories with associated labels

– Matching algorithms attempts to match form labels with database values to form value assignment sets


HiWE – Conceptual Parts


HiWE – Form Representation

• F = ({E1,E2,…,En} S, 0)– Dom(Ei)– Label(Ei)


HiWE – Task specific Database• Organized as a finite set of concepts of

categories• Each concept has one or more labels and

associated values• Each Row in the LVS table is of the form (L, V),

– L is a label– V = {v1,…, vn} is a fuzzy– vi represents a value– Fuzzy set V has associated membership function Mv– Mv(vi) is the crawlers confidence of assignment


HiWE – Matching Function• Label Matching

– All labels are normalized• Common case, Stemming, Stop word removal

– String Matching • with min edit distances, word orderings

– Threshold of Sigma < edit operations. Then set to nil

• Ranking Value Assignments– Min Rho.– Fuzzy Conjunction - Rho fuz– Average – Rho avg– Probabilistic – Rho prob


HiWE – Populating LVS Table• Explicit Initialization• Built-in entries

– Dates, Times, names of months, days of the week

• Wrapped data Sources– Set of Labels, new entries– Set of Values, search similar, expand existing

• Crawling Experience– Finite domain elements– Can be used to fill out the second form more efficiently


HiWE – Computing Weights• Explicit initialization

– Fixed, predefined weights (usually 1) representing maximum confidence in human supplied values

• External data sources or crawler activity– Positive boost – Successful– Negative boost – Unsuccessful

• Initial weights obtained from external data sources are computed by the wrapper


HiWE – Computing Weights• Finite domain

– Case 1 – Crawler Extracts label, Label Match found• Unions the values to the • Boost the weights/confidence of the existing values

– Case 2 – Crawler Extracts label, Label Match = nil• New row is added in LVS table

– Case 3 – Can not extract label• Identify values that most closely resembles Dom(E)• Once located, add values in Dom(E) to value set


HiWE – Explicit Configuration


1 Set of sites to crawl

2 Explicit initialization entries for the LVS table

3 Set of data sources, wrapped if necessary

4 Label matching threshold (σ)

5 Minimum acceptable value assignment rank (ρ min)

6 Minimum form size (α)

7 Value assignment aggregation function

LITE• Layout-based information extraction• Used in automatically extracting semantic

information from search forms.• In addition to text, uses the physical layout of the

page to aid in extraction• Not always reflected in HTML markup


LITE – Usage in HiWE• Used in Label Extraction• Implemented by page

pruning. Isolate elements that directly influence the layout of the form elements and labels


LITE – Steps• Approximate layout of pruned page discarding

images, font styles and style sheets• Identifies pieces of text closest to form element

as candidates• Ranks Each candidate taking into account

position, font size, font style, number of words• Chooses the highest ranked candidate as label

associated with element


Experiment - Parameters

• Task 1 Shown which is for “News articles, reports, press releases, and white papers relating to the semiconductor industry, dated sometime in the last ten years”


PARAMETER VALUENumber of sites visited 50

Number of forms encountered 218Number of forms chosen for submission 94

Label matching threshold (σ) 0.75Minimum form size (α) 3

Value assignment ranking function ρfuzMinimum acceptable value assignment rank (ρmin) 0.6

Results – Value Ranking• Was executed three times with

same parameters, initializations values and parameters but using different ranking function

• Pave might be a better choice for maximum content extraction

• Pfuz is the most efficient• Pprob submits the most forms

but performs poorly


RankingFunction

Task 1

Ntotal Nsuccess SEstrict

ρfuz3214 2853 88.8

ρavg 3760 3126 83.1

ρprob4316 2810 65.1

Results – Form Size


3735

29503214

2853 28002491

1404

78.9%

88.77%

88.96%

90%

Num

ber o

f for

m s

ubm

issi

ons

Results – Crawler additions to LVS


Results – LITE Label Extraction• Elements from 1 to 10• Manually analyzed to

derive correct label• Also ran other label

extraction heuristics– Purely textual analysis– Common ways forms are laid

out

• LITE was 93% vs 72% and 83%


Total number of forms 100

Number of sites from which forms were picked 52

Total number of elements 460

Total number of finite domain elements 140

Average number of elements per form 4.6

Minimum number of elements per form 1

Maximum number of elements per form 12

Relation to Class Notes• Content driven Crawler

– Different crawlers for different purposes

• Contains Similar crawler Metrics– Crawling speed– Scalability– Page importance– Freshness

• Data Transfer– Stored after crawled


Cons• Freshness/Recrawling isn’t addressed• Task specific, human configuration• Login Based, Cookie JAR implementation• Didn’t discuss Hidden fields or Capchas• Didn’t run task 1 results without LITE.• Not using the “name” element tag in form elements• Required fields vs. not required• Wild cards, incomplete forms• Form element decencies.


Pros• First Hidden Crawler Report• Not run at runtime

– VS. shopping and travel sites that do.

• Gets better overtime


Conclusion / Thoughts• Hidden web is much bigger now.• Hidden web reached now with google analytics

and google ads• Now we also have ajax based forms. How do we

deal with ajax based forms?


Thank YouQuestions

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Crawling the Hidden Web