Web NoisesDetection and Elimination

PengBoDec 3, 2010

What are Web Noises？

主题 Topic

导航 NavGuide

广告 Adv

Call them Noises 虽然这些信息对于人浏览 Web 有用，但常常对自

动 Web 信息处理带来负面影响，比如 Web page clustering, classification, information retrieval and information extraction.

hamper automated information gathering and Web data mining,

“Template Detection via Data Mining and its Applications”

Non-Relevant Data on the Web A fundamental problem on the Web:

“non-relevant” – not directly related to the main topic / functionality of the page

Local (intra-page) noise Irrelevant items within a Web page. E.g., banner ads, navigational guides

Many pages contain lots of non-relevant data

Duplicate data on the Web Another problem on the Web:

Mirrors ， News copy, etc, Global noise

Redundant objects Larger than individual page E.g., mirror sites, duplicated Web pages

There are much duplicate or near duplicate data

Why it influences? Hypertext IR Principles--principles of all link

based IR tools: Relevant Linkage Principle

p links to q q is relevant to p Topical Unity Principle

q1 and q2 are co-cited in p q1 and q2 are related to each other

Lexical Affinity Principle The closer the links to q1 and q2 are the

stronger the relation between them.

Violations of Relevant Linkage Principle

Navigational links http://www.ibm.com/

Download links http://www.beethoven.com/

Advertisement links http://www.yahoo.com/

Endorsement links http://www.ebay.com/

Spam links

Violations of Topical Unity Principle

Violations of the Relevant Linkage Principle

Bookmark pages http://bookmark.yinsha.com/ 网上书签

General resource lists http://sewm.pku.edu.cn/IR-

Guide.txt IR Guide Personal homepages

http://www.cse.iitb.ac.in/~soumen/ Soumen’s Home Page

Violations of Lexical Affinity Principle

Alphabetical index lists Computer and Communication Companies ("M" entries)

HTML representation Adjacent cells in the same column are far from each

other in the HTML text

IR Tool Problems Generalization

Search for “Frequency Division Multiplexing” and get back general Electrical Engineering sites

Topic drift Search for “Finite Model Theory” and get SF

49’ers fan web sites Irrelevance

Get “Yahoo” as a result regardless of the query Bias

Search for “computing companies” and get Microspy highly ranked

Hypertext Improvement Problem

remove violations of the Hypertext IR principles process quickly millions of pages

Develop hypertext processing techniques that:• automatically improve hypertext data• are efficient and scalable

Main Goal

Hypertext Cleaning

Web

Crawler

Hypertext Cleaner

IR Tool

Template detection

DOM Tree

模版 Template

Templates

Templates Detection Semantic Definition: A template is a master HTML

shell page that is used as a basis for composing new pages

Content of new pages plugged into template shell

All pages share common look & feel Usually controlled by a central

authority Not necessarily confined to a

single site May include variety of data

Navigational bars Advertisements Company info and policies

Search pagelet

Navigation pagelet

Services pagelet

Company info pagelet

Ad pagelet

Pagelets Semantic Definition: A pagelet is a maximal region of a page

that has a single topic or functionality Not too large

has only one topic / functionality Not too small

any larger region that contains it has other topics / functionalities

IR with Pagelets

Use pagelets rather than pages as atomic units for information retrieval

Main Idea 1

Main Idea 2

Eliminate pagelets belonging to templates

Pagelets: Syntactic Definition A pagelet is a node in the

HTML parse tree of a page satisfying the following: Its HTML tag is one of the

following: <TABLE>, <OL>, <UL>,

<AREA>, <P>, <DL>, … None of it’s children

contains more than k hyperlinks

None of its ancestor is a pagelet

Templates: Syntactic Definition

A template is a collection T = (p1,…,pk) of pagelets satisfying:

Similarity:p1,…,pk are identical or almost identical

Connectivity Every two pages owning pagelets in T are

reachable from each other (undirectedely) through other pages owning pagelets in T.p1

p3

p5

p2

p4

Template Recognition Problem: Given a set of pages S find all the templates in S.

Template Recognition in Large Sets

Cluster pagelets in S according to shingle

Calculate shingle(p) for each pagelet pS

Discard clusters of size 1

For each remaining cluster C:

Construct graph Gc of pages that own pagelets in C

Find undirected connected components of Gc

Output components of size > 1

Evaluation Question:

How to evaluate the performance/effectiveness of this cleaning algorithm?

Benefits of template detection

Cleaning via feature weighting

Cleaning via feature weightingCleaning via feature weighting

In a given Web site Noisy blocks — Share

common contents or presentation styles

Meaningful (or main) blocks — diverse in contents and presentation style

Weighting features makes cleaning automatic (nothing is eliminated)“Eliminating noisy

information in Web pages for data mining”

DOM treesDOM trees

<BODY bgcolor=WHITE> <TABLE width=800 height=200 > … </TABLE> <IMG src="image.gif" width=800> <TABLE bgcolor=RED> … </TABLE></BODY>

bc=red

bc=white

IMG TABLE

BODY

root

width=800 height=20

0TABLEwidth=800

Build Site style tree (SST)

common

SST

Style Node S = (ELEMENTs, n) ELEMENTs — a sequence of element nodes n — number of pages that has this style

Element Node E = (Tag, Attr, STYLEs) Tag — tag name. E.g., TABLE, IMG; Attr — display attributes of Tag. E.g., bgcolor=RED STYLEs — style nodes below E

Quantify the importance

Inner Node

Leaf Node

Weighting policy Inner Node Importance

(1) l = |E.STYLEs| m = number of pages containing E, |E.parent.n| pi — percentage of tag nodes (in E.parent.n)

using the i-th presentation style Inner NodeImp(E) — diversity of presentation

styles

11

1

log)(1 mif

mifppENodeImpl

iimi

NodeImp(Body) = -1log1001 = 0NodeImp(Table)

= -(0.35log1000.35 + 2*0.25log1000.25+ 0.15log1000.15) = 0.29 >0

11

1

log)(1 mif

mifppENodeImpl

iimi

Weighting policy

Features( terms) of Leaf Node Importance of Leaf Node’s Features

(3)

m = number of pages containing E, |E.parent.n| pij — probability of ai appears in E of page j HE(ai) — information entropy of ai

the higher HE(ai), the less important ai

11

log

0)(

1mifmif

ppaH m

jijmij

iE

Weighting policy

Leaf Node Importance

(2) N — number of features in E ai — a feature of content in E (1-HE(ai)) — information contained in ai Leaf NodeImp(E) —content diversity of E

N

aH

N

aHENodeImp

N

iiE

N

iiE

11

)(1

))(1()(

Et1:PCMag,samsung

t2:PCMag,epson

t3:PCMag,canon

TABLEEp

SST:

IMG

root

3

m = 3N = |{PCMag, samsung,

epson, canon}| = 4HE(PCMag) =

-3 * (1/3log31/3) = 1HE(samsung)=HE(epson) =HE(canon) = -(0+0+1log31) = 0NodeImp(E) = ((1-1) + 3*(1-0))/4

= 0.75

11

log

0)(

1mifmif

ppaH m

jijmij

iE

Transitive Weighting policy

Composite Importance

0

0.290

0.75

Page nosie noisy element node

For an element node E in the SST, if all of its descendents and itself have composite importance less than a specified threshold t, then we say element node E is noisy.

Maximal noisy element node meaningful element node :

If an element node E in the SST does not contain any noisy descendent, we say that E is meaningful.

Maximal meaningful element node

Web page cleaning via block elimination

We can use SST (site style tree) to identify & eliminate noise content blocks in a page. Build SST by sample pages crawled from a site. Computing an importance value for each block,

using a specified threshold t to decide noisy or not noisy

Matching to noisy blocks and not noisy blocks in the tree, given a new page.

Noise Detection and Elimination

Table Img Table

Body

Table

TrTr

root

Text

Text

AP

P P P A P Img

AImg

A A A AA

After simplification

Table Img Table

Body

Table

TrTr

root

Text

Summary of the technique Evaluate Common and Diversity of content

and styles DOM trees SST Information Entropy Based Evaluation

Node Importance Composite Importance

Noise detection and automatic matching

Near duplicate detection

Syntactic clustering of the web contentsWWW6,1997

Document Representation

How to represent a document? Represent document content by a feature

set ， preparing the computations of resemblance or similarity.

For document D, extract it’s feature set as S(D)

Defining similarity of documents

How to express the concept “roughly the same” precisely?

Quantity Definition: resemblance The resemblance fo two documents A and B is a

number between 0 and 1.

Defining similarity of documents(cont’d)

Resemblance

Symmetric, reflexive, not transitive, not a metric

Note r (A,A) = 1 But r (A,B)=1 does not mean A and B are identical!

Forgives any number of occurrences and any permutations of the terms.

Resemblance distance

)()()()(

),(BSASBSAS

BAr

),(1),( BArBAd

Jaccard coefficient

Feature Selection Assume:

we have converted page into a sequence of tokens Eliminate punctuation, HTML markup, lower

case, etc How to do feature selection, S(D)=?

Document level Character/word level Shingle level

Shingling A contiguous subsequence contained in D is called a shingle. Given a document D we define its w-shingling S(D, w) as the set of all unique shingles of size w contained in D.

D = (a,rose,is,a,rose,is,a,rose) S(D,4) = {(a,rose,is,a),(rose,is,a,rose),

(is,a,rose,is)} “a rose is a rose is a rose” => a_rose_is_a rose_is_a_rose is_a_rose_is

Why shingling? S(D,4) .vs. S(D,1)What is a good value for w?

Sketches Set of all shingles is large

Bigger than the original document

Can we create a document sketch by sampling only a few shingles?

Requirement Sketch resemblance should be

a good estimate of document resemblance

Choosing a sketch Random sampling

E.g., suppose we have identical documents A & B each with n shingles

M(A) = set of s shingles from A, chosen uniformly at random; similarly M(B)

Does it work? For s=1: E[|M(A) M(B)|] = 1/n

But r(A,B) = 1 So the sketch overlap is an underestimate

Verify that this is true for any value of s

Choosing a sketch Improvements:

Random sampling + compare “special” item Random permutations + compare “smallest”

shingle Random permutation

Let be a set (1..N e.g.) Pick a permutation : uniformly at random

={3,7,1,4,6,2,5} A={2,3,6} MIN((A))=?

Estimating Jaccard Coefficient Theorem : If permutations are picked uniformly at

random from the n! possible permutations,

Choosing a sketch Create a “sketch vector” (e.g., of size 200)

for each document Documents which share more than t (say 80%)

corresponding vector elements are similar For doc d, sketchd[i] is computed as follows:

Let f map all shingles in the universe to 0..2m

Let i be a specific random permutation on 0..2m

Pick MIN i (f(s)) over all shingles s in d

Computing Sketch[i] for Doc1

Document 1

264

264

264

264

Start with 64 bit shingles

Permute on the number linewith i

Pick the min value

Test if Doc1.Sketch[i] = Doc2.Sketch[i]

Test for 200 random permutations: , ,… 200

Are these equal?

Document 1

264

264

264

264A

Document 2

264

264

264

264A

However…

A = B iff the shingle with the MIN value in the union of Doc1 and Doc2 is common to both (I.e., lies in the intersection)This happens with probability: Size_of_intersection / Size_of_union

BA

Document 1

264

264

264

264A

Document 2

264

264

264

264A

Finding all near-duplicates Naïve implementation makes O(N^2)

sketch comparisons Suppose N=100 million

How can you do it faster?

本次课小结 Web Noises

Hypertext IR Principles Template Detection

Semantic and Syntactic Definition

Information Entropy of Features Weighting

SST Near duplicates

detection Jaccard similarity Shingling sketch

Document 1

264

264

264

26

4

A

Document 2

264

264

264

264A

Table

Img Table

Body

Table

TrTr

root

Text

References [1] B.-Y. Ziv and R. Sridhar, "Template detection via data

mining and its applications," in Proceedings of the 11th international conference on World Wide Web. Honolulu, Hawaii, USA: ACM Press, 2002.

[2] Y. Lan, L. Bing, and L. Xiaoli, "Eliminating noisy information in Web pages for data mining," in Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining. Washington, D.C.: ACM Press, 2003.

[3] G. David, P. Kunal, and T. Andrew, "The volume and evolution of web page templates," in Special interest tracks and posters of the 14th international conference on World Wide Web. Chiba, Japan: ACM Press, 2005.

[4] Z. B. Andrei, C. G. Steven, S. M. Mark, and Z. Geoffrey, "Syntactic clustering of the Web," in Selected papers from the sixth international conference on World Wide Web. Santa Clara, California, United States: Elsevier Science Publishers Ltd., 1997.

[5] N. Shivakumar and H. Garca-Molina, "Finding near-replicas of documents on the web," presented at Proceedings of Workshop on Web Databases (WebDB'98), Mar, 1998.

Related Resources Html-tidy Code

http://code.google.com/p/html-tidy/ Shingle Code

http://research.microsoft.com/research/downloads/Details/4e0d0535-ff4c-4259-99fa-ab34f3f57d67/Details.aspx?0sr=d

Thank You!Q&A

阅读材料 [1] IIR Chapter 19.6 [2] G. Salton and C. Buckley, "Term-

weighting approaches in automatic text retrieval," Inf. Process. Manage., vol. 24, pp. 523, 1988.

DOM Tree

W3C Document Object Model allow programs and scripts to dynamically access

and update the content, structure and style of documents. The document can be further processed and the results of that processing can be incorporated back into the presented page.

Information Entropy In information theory,

entropy is a measure of the uncertainty associated with a random variable. The term by itself in this context usually refers to the Shannon entropy, which quantifies, in the sense of an expected value, the information contained in a message, usually in units such as bits.

Estimating algorithm

1. Generate a set of m random permutations

2. for each do3. compute and 4. check if5. end for6. if equality was observed in k cases,

estimate

mkddr ),(' 21

))((min))((min 21 dSdS ))(( 1dS ))(( 2dS

Some other approaches For set W of shingles, let MINs(W) = set of s

smallest shingles in W Assume documents have at least s shingles

Define M(A) = MINs(S(A)) M(AB) = MINs(M(A) M(B)) r’(A,B) = |M(AB) M(A) M(B)| / s

By increasing sample size (s) we can make it very unlikely r’(A,B) is significantly different from r(A,B)

100-200 shingles is sufficient in practice Compute a fingerprint f for each shingle (e.g.,

Rabin fingerprint) 40 bits is usually enough to keep estimates reasonably

accurate Fingerprint also eliminates need for random permutation

Finding all near-duplicates Naïve implementation makes O(N^2)

sketch comparisons Suppose N=100 million

Divide-Compute-Merge (DCM) Divide data into batches that fit in memory Operate on individual batch and write out

partial results in sorted order Merge partial results

Generalization of external sorting

doc1: s11,s12,…,s1kdoc2: s21,s22,…,s2k…

DCM Stepss11,doc1s12,doc1…s1k,doc1s21,doc2…

Invert t1,doc1t1,docX…t2,doc1t2,docY…

sort onshingle_fp

doc1,docX,1doc1,docZ,1…doc1,docY,1…

Invert and pair

doc1,docX,1doc1,docX,1…doc1,docY,1… sort on

<docid1,docid2>

doc1,docX,2doc1,docY,10…

Merge

Finding all near-duplicates1. Calculate a sketch for each document2. For each document, write out the pairs <shingle_fp,

docId>3. Sort by shingle_fp (DCM)4. In a sequential scan, generate triplets of the form

<docId1,docId2,1> for pairs of docs that share a shingle (DCM)

5. Sort on <docId1,docId2> (DCM)6. Merge the triplets with common docids to generate

triplets of the form <docId1,docId2,count> (DCM)7. Output document pairs whose resemblance exceeds

the threshold

DCM algorithm1. for each random permutation do2. create a file3. for each document d do4. write out to 5. end for6. sort using key s -- this results in contiguous blocks with

fixed s containing all associated

7. create a file8. for each pair within a run of having a given s do9. write out a document-pair record to10. end for11. sort on key 12. end for13. merge for all in order, counting the number of

entries

),( 21 dd

sd

ddSs )),((min

f

f

f

g

f),( 21 dd

g ),( 21 dd

g ),( 21 dd ),( 21 dd

g

Some optimizations “Invert and Pair” is the most expensive

step We can speed it up eliminating very

common shingles Common headers, footers, etc. Do it as a preprocessing step

Also, eliminate exact duplicates up front Probabilistic Counting [5]

Detecting duplicate pages

State of the art Technology

Volume and Evolution of Page Templates

Our results show that 40–50% of the content on the web is template content.

Over the last eight years, the fraction of template content has doubled, and the growth shows no sign of abating.

Text, links, and total HTML bytes within templates are all growing as a fraction of total content at a rate of between 6 and 8% per year. Question:

how to design the experiment to reach these conclusions?