vishue: web page segmentation for improved query interface for medlineplus medical encyclopaedia

7/25/2019 VisHue: Web Page Segmentation for Improved Query Interface for MedlinePlus Medical Encyclopaedia

1/40

to Advance Knowledge for Humanity

Aastha Madaan, Wanm ing Chu , Subhash

Bhal la

Universi ty o f Aizu

1

VisHue: Web Page Segmentation for an

Improved Query I nterface for M edlinePlus

Medical Encyclopedia

12/10/2011


2/40


Outl ine

1. In troduc t io n

2. Backg rounda) Hierarchical structure

b) Page-Level Segmentation

3. Web Page segmen tat ion A lgo rit hms

a) Features

b) Main focus

c) Comparison

4. The Proposal: The VisHue A lgo rit hm

5. Qu ery by Segmen t

6. Perfo rmance Analy sis

7. Discuss ions

8. Summary and Conc lu sio ns

212/10/2011


3/40


1. In troduction

WWW is a common and the largest source of

information

Deep Querying Gaining importance

Understanding web page semantics Improves Users

search experience

Within a web page Identify semantic groups

Important Discovering these semantic blocks

312/10/2011


4/40


1(i). The Statement [1]

A. Large variety of HTML pages suitable query and

search ?

B. Basic Requirements searching and querying

Simple querying and searching semantic querying and

searching

C. Significant Recognize the semantic and coherent

segments

Page-level Segment Level

D. Case Example Medical Encyclopedia

MedlinePlus various choices of medical encyclopedias

412/10/2011


5/40


1(i). The Statement [2]

12/10/2011 5

UML Class

Diagram


6/40


Outl ine

1. In troduc t io n




a) Features

b) Main focus

c) Comparison

4. The Proposal: VisHue A lgo rit hm



7. Discuss ions


612/10/2011


7/40


2. Background : Med l inePlus Web page:

i. Relevant content ii. Irrelevant content

a. Relevant Content:

i. Topic headings ii. Topic wise contents

b. Irrelevant Content:Navigation bars, header, footer, advertisements

Headings Identify hierarchical structure

Distinct blocks What a usersperception identifies Main focus Skilled and Semi-skilled users

Assumption Headings Query attributes

712/10/2011


8/40


2(a). Hierarch ical Struc tu re

1. Hierarchical structure logical structure within the

Page(document)

2. Indicates the binary relationships (belongingness)

between a pair of segments

3. Accurate Hierarchical Representation User Level

Query Attributes (in segments)

4. Proposed hierarchical structure based on domainknowledge (skilled and semi-skilled users)

Captures users perception

812/10/2011


9/40


2(a).(i). Segmentation Semantic Query

9

User

Semantic query

and search

(In future)

Common

WebUser

12/10/2011


10/40


2 (b ). Page-Level Segmentat ion

Definition

A self-contained logical region within a Web page that is:

(i) not nested within any other segment;

(ii) represented by a pair (l; c)

Where, l label of the segment

c portion of textof the segment [1].

1012/10/2011


11/40


Outl ine

1. In troduc t io n




a) Features

b) Main focus

c) Comparison




7. Discuss ions


1112/10/2011


12/40


3. Segmentat ion algo r i thms

i. History segmentation traces back to theyear 2001 (continues till 2011)

ii. Various application domains

iii. Various techniques for segmenting

iv. Various terminologies used

v. Proposed MedlinePlus items of users

focus Query Attributes

1212/10/2011


13/40


3 (a). Features of Segm entation A lgo r i thm

A. Match and Identify a users points of focus

B. Discover informative segments

i. Better search and query

ii. Segments become query-able attributes

iii. Skilled users aim to query the informative areas

(only)

C. Generate True hierarchical structure

D. Segmentation Process Low space and time

complexity

1312/10/2011


14/40


3(b ). Main Focus

Find an algorithm best suited for:

1. Generate hierarchical structure

2. Convert segments to attributes in

database

3. Facilitates in-depth querying

12/10/2011 14


15/40


3 (b). (i). Segmentation Methods Web Technologies

1512/10/2011

t Ad K l d f H it


16/40


3 (b ). (i i ). Class if icat ion o f A lgori thms

1612/10/2011

t Ad K l d f H it


17/40


3(b). (iii). Timeline Techniques

Algorithm Year

Technique

Template Detection [9], [6] 2002, 2007

Dom-Node Recognition [8], [11], [10] 2001, 2002, 2006

Visual-DOM based

Rendering

[2] 2003

Visual-Heuristics based

Method

Proposed -

Graph-theoretic Method [3] 2008

Linguistics basedMethod [7] 2008

Image of the Web Page [4], [5] 2010,2009

Site-Oriented Method [1] 2011

1712/10/2011



18/40


3(c). Comparison

1812/10/2011



19/40


3(c ).(i). Main Focus

1912/10/2011



20/40


3.(c ).(i i ).Comparison : Vision based Mtds .

2012/10/2011



21/40


3(c ).(i i i ). Content Struc tu re by VisHue

2112/10/2011



22/40


Outl ine

1. In troduc t io n




a) Features

b) Main focus

c) Comparison




7. Discuss ions


2212/10/2011



23/40


4. The Proposal: VisHue A lgo r i thm

12/10/2011 23



24/40


4. (i). Query In ter faces

Querying v/s Searching

Searching: Recent Trends

1. Object based search2. Block based search

3. Entity based search

Querying: Recent Trends

Very few efforts have been done

2412/10/2011



25/40


Outl ine

1. In troduc t io n




a) Features

b) Main focus

c) Comparison




7. Discuss ions


2512/10/2011



26/40

g y

5. Query by Segment

Query by Segment as Query by Tag (Heading) QBT

Based on Content Structure (VisHue algorithm) :

Query by Attributes

MedlinePlus medical encyclopedia 3886 web pages

Target Focused and explicit querying

i. Beneficial skilled and semi-skilled users

ii. Medical encyclopedia result of years of efforts

by experts

2612/10/2011



27/40

g y

5. (i). The QBT in ter face

27

Traditional search on MedlinePlus

medical encyclopedia

QBT interface

12/10/2011

Title Caus

es

Sympt

oms

Post-

Care

DB



28/40

5. (ii). QBT In terface Hierarch ical Struc tu re

Labels QueryAttributes

QBT interface: Search and Query

Child nodes search attributes

Left siblings limit the scope of search of right

siblings in the interface

Segments Attributes for Deep Query over allpages of MedlinePlus

2812/10/2011



29/40

Outl ine

1. In troduc t io n


b) Segmentation


a) Features

b) Main focus

c) Comparison




7. Discuss ions


2912/10/2011



30/40

6. Perfo rmance Analys is

i. Qualitative comparison with traditional

keyword search

ii. Query formulation and interpretation

iii. Quantitative performance analysis of theinterface

3012/10/2011



31/40

6.(i). QBT vs . Keyword Search

3112/10/2011



32/40

6. (i i ). Query Form ulat ion : A Comparison

3212/10/2011



33/40

6. (i i i). Query Example

Query 1:Cases where patient has

hypertension but not high blood pressure

QBT query :

Symptoms: Hypertension

Symptoms:NOT High Blood Pressure

33



34/40

34

6. (iv ). Query A ttr ibu tes



35/40

35

6. (v ). Query Resu lts



36/40

6. (v i). Quanti tat ive Perform ance Analys is

36

QBT Query

Symptom: Hypertension

Symptom:NOT High

Blood Pressure

Before Procedure: Stop

After Procedure:Normal

Cause: HighBlood

Pressure

Symptom: Heart Attack

Food Source: Fish

Side Effect: Poisoning

12/10/2011



37/40

7. Discuss ions

Content fragments as perceived by skilled and semi-

skilled domain users determined by web pagesegmentation process

Proposed effort Formulating a generic heuristic

design-rule and visual features based algorithm

The QBT interface Query over user identified

segments (attributes)

Aim Convert MedlinePlus pages DB

Contention web page good source easy to use

new query language interface for segments

3712/10/2011


8 S C


38/40

8. Summary and Conclus ions

A. Heuristics + visual features based segmentation

turning point:

A. Provides independent solution

B. Improves Query interfaces for chosen domain

B. The medical domain need to make the informationaccessible to the end-users

C. Query by Segment or Tag (QBT) Anattempt

A. Aim return the users query-able attributes

3812/10/2011


R f


39/40

References1. A Site Oriented Method for Segmenting Web Pages, David Fernandes, Edleno S. de Moura, Altigran S.

da Silva, Berthier Ribeiro-Neto, Edisson Braga, SIGIR11, July 24-28, 2011.

2. Extracting Content Structure for Web Pages based on Visual Representation, Deng Cai, Shipeng Yu, Ji-

Rong Wen and Wei-Ying Ma, Web Technologies and Applications: 5th Asia-Pacific Web Conference,APWeb 2003, Xian, China, April 23-25, 2003. Proceedings (2003), pp. 596-596.

3. Graph-Theoretic Approach to Webpage Segmentation, Deepayan Chakrabarti, Ravi Kumar, Kunal

Punera, WWW 2008 / Refereed Track: Search - Corpus Characterization & Search Performance, Beijing,

China.

4. A segmentation method for web page analysis using shrinking and dividing, Jiuxin Cao, Bo Mao &

Junzhou Luo (2010): International Journal of Parallel, Emergent and Distributed Systems, 25:2, 93-104.

5. Web Page Layout via Visual Segmentation,Ayelet Pnueli, Ruth Bergman, Sagi Schein, Omer Barkol, HP

Laboratories, 2009.

6. Page-level template detection via isotonic smoothing. D. Chakrabarti, R. Kumar, and K. Punera. In 16th

WWW, pages 6170, 2007.

7. "A Densitometric Approach to Web Page Segmentation", Christian Kohlschtter, Wolfgang Nejdl, CIKM08,

October 2630, 2008

8. HTML Page Analysis Based on Visual Cues , Yudong Yang and HongJiang Zhang, IEEE 2001

9. Template Detection via Data Mining and itsApplications , Ziv Bar Yossef, Sridhar Rajagopalan, In

Proceedings of WWW'02, May 711, 2002, Honolulu, Hawaii, USA.

10. "DeSeA: A Page Segmentation based Algorithm for Information Extraction", He Juan, Gao Zhiqiang, Xu

Hui, Qu Yuzhong, Proceedings of the First International Conference on Semantics, Knowledge, and Grid

(SKG 2005).

11. "Reverse Engineering for Web Data: From Visual to Semantic Structures", Christina Yip Chung, Michael

Gertz, Neel Sundaresan, In proceedings of the 18th International Conference on Data Engineering

(ICDE02).

3912/10/2011



40/40

Thank you

Quest ions

4012/10/2011

vishue: web page segmentation for improved query interface for medlineplus medical encyclopaedia

Documents