Wrapper induction construct wrappers automatically to extract information from web sources

Wrapper Induction: Construct Outline:
wrappers automatically to extract
information from web sources • What is wrapper
• Wrapper Induction
• WIEN
Hongfei Qu • STALKER
Computing Science Department • Remaining Questions
Simon Fraser University • HTML DOM Tree
• Other Related Works
CMPT 882 Presentation • References
March 28, 2001

What is wrapper What is wrapper
• Wrapper is a procedure to extract all kinds of data • execLR(wrapper(, , , ), page P):
from a specific web source m=0
• First find a vector of strings to delimit the extracted
while there are more occurrences in P of 
text
• <HTML><TITLE>Country Codes</TITLE> m=m+1
<BODY>Congo 242 for each (lk, rk) in {(, ), (, )}
Spain 34 scan in P to the next occurrence of lk in P;
<HR>END</BODY></HTML> save position as bm,k
• To extract pair (country, codes), we find a vector of
scan in P to the next occurrence of rk in P;
strings (, , , ) to distinguish left &
right of extracted text. save position as e m,k
Return label{…(bm,1, e m,1), (bm,2, e m,2)…}

Wrapper Induction Wrapper Induction

• Motivations: hand-coded wrapper is • Actually we are trying to learn a vector of
tedious and error-prone. How about web delimiters, which is used to instantiate some
pages get changed? wrapper classes (templates), which describe
• Wrapper induction –- automatically the document structure
generate wrapper --- is a typical • Free text & Web pages
machine learning technology. • A good wrapper induction system should be:
• Input: a set E of example pages Pn and – Expressiveness: concern how the wrapper handles
a particular web site
the corresponding label pages Ln
– Efficiency: how many samples are needed? How
• Output: a wrapper w such that w(Pn) = much computational is required?
Ln

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WIEN WIEN

• First wrapper induction system implemented • Procedure learnLR(examples E)
by U. Washington. Works for both Web page for each 1<= k <=K
and free text. for each u in Candl(k, E): if u is valid for the kth
• WIEN defines 6 wrapper classes (templates) to attribute in E, then lk = u and terminate the loop
express the structures of web sites. for each 1<= k <=K
• The simplest and powerful one is LR (left- for each u in Candr(k, E): if u is valid for the kth
right) wrapper class. It uses left- and right- attribute in E, then lr = u and terminate the loop
hand delimiter to extract the relevant
return LR wrapper(l1, r1 , …, lk, rk)
information
• Procedure Candl(k, E) returns candidates for lk by
• To extract tuples with K attributes from a set enumerating the suffixes of the shortest string occurring
of examples E, the learning algorithm is: to the left of each attribute k instances

WIEN WIEN

• Procedure Cand r(k, E) returns candidates for lr by • Which wrapper class do we choose for a web site?
enumerating the prefixes of the shortest string • How many examples are required? PAC model
occurring to the right of each attribute k instances; N: number of examples;
• Each wrapper class has a set of validating constraints e: accuracy parameter. 0 < e < 1
• Other wrapper classes: a: confidence parameter. 0 < a < 1
– HLRT: add head delimiter h & tail delimiter t For a learning wrapper W, if we want error(W) < e
with probability at least a, the PAC model for the LR
– OCLR: using open and close delimiers to indicate
class is:
the beginning and end of each tuple
N >= 1/(1-a) * (2K*ln( R ) - ln(1 - a ) ), where R is the
– HOCLRT: combination of HLRT and OCLR length of the shortest example.
– N-LR and N-HLRT: handle nested structure • A way to terminate the learning precedure
• Combination of 6 classes can handle 70% web sites • A loose bound compared with test results

STALKER STALKER

• A wrapper induction project by U. Southern • Landmarks: a sequence of tokens, argument
California. Only works for Web page. of some functions.
• More expressive and efficient than WIEN. SkipTo(): start from beginning, skip
• Treat a web page as a tree-like structure and everything until find landmarks
handle information extraction hierarchically SkipTo()SkipTo()
• Use disjunctions to deal with the variations. • These functions represent the rules to extract
Disjunctive rules are ordered lists of the information
individual disjuncts. The wrapper will • Start rule: identify the beginning of an
successively apply each disjunct in the list attribute
until it finds one that matches • End rule: identify the end of an attribute

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STALKER STALKER
<body>Name:HongfeiID:1111
• These SkipTo( ) functions represent a finite
Address: 4000 Main St, Vancouver, BC, (604)333-3233
state machine model 3000 Hastings St, LA, CA, 1-805-486-5675</body>
• Extraction rules: get information
• Document Extraction rule: SkipTo( )&
landmark SkipTo(</body>)
•
Si Sj

• Iteration rules: handle nested structure • Name ID List of Address
Iteration rule: SkipTo()
& SkipTo()
landmark
•
• St city province area_code phone extraction rule: either
Si SkipTo( ( ) or SkipTo( 1- )
•

STALKER Remaining Questions

• Use a sequential covering algorithm • Find more expressive model to express
• STALKER(examples) document structure
Set setRule be empty
While there are more examples • Select only the informative examples to
Get a disjunct D by learning examples learn a wrapper.(active learning? Data
Remove all examples covered D mining?)
Add D into setRule
Return setRule
• How to generate label pages automatically
• STALKER can handle 90% and more efficient. instead of hand-markup?
• Generate imperfect rules

HTML DOM Tree Other Related Works
• Using a DOM-like tree model on HTML tags • TrIAs---html tree
HTML • SOFTMEALY---first use disjunction rule and
Head Body finite state machine model
• WISK---works for web page and free text, more
Title LI LI LI
expressive than WIEN, decision-making is based
• The navigation methods are similar to XML on limited context. Slower.
DOM tree. Only works for web pages.
• SRV
• Using the tree path to extract information
• CRYSTAL
• Also can follow the document flow like
STALKER to extract information • RAPIER
• Get rid of imperfect rules and more efficient

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References
• Nicholas Kushmerick, Wrapper Induction: Efficiency and
expressiveness, Artificial Intelligence 118, 2000
• Ion Muslea, Steven Minton, Craig A. Knoblock, A Hierarchical
Approach to Wrapper Induction, Conference Autonomous Agents,
Seattle, WA, 1999
• S. Soderland, Learning information extraction rules for semi-
structured and free text, Machine Learning 34, 1999
• C. Hsu, M. Dung, Generating finite-state transducers for
semistructured data extraction from the web, Information Systems
23, 1998
• M. Bauer, D.Dengler, TrIAs—An architecture for trainable
information assistants, Worksshop on AI and Information Integration,
Madison, WI, 1998
• D. Freitag, Information extraction from HTML: Application of a
general machine learning approach, AIII-98, Madison, WI, 1998

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Wrapper induction construct wrappers automatically to extract information from web sources

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Wrapper induction construct wrappers automatically to extract information from web sources