Web Page Segmentation for Querying Healthcare Repository

to Advance Knowledge for Humanityto Advance Knowledge for Humanity

Aastha Madaan, Wanming Chu, Subhash Bhalla

University of Aizu

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VisHue: Web Page Segmentation for an VisHue: Web Page Segmentation for an Improved Query Interface for MedlinePlus Improved Query Interface for MedlinePlus

Medical Encyclopedia Medical Encyclopedia

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OutlineOutline1. Introduction

2. Background

a) Hierarchical structure

b) Page-Level Segmentation

3. Web Page segmentation Algorithms

a) Features

b) Main focus

c) Comparison

4. The Proposal: The VisHue Algorithm

5. Query by Segment

6. Performance Analysis

7. Discussions

8. Summary and Conclusions211/12/16


1.1. IntroductionIntroduction WWW is a common and the largest source of

information

Deep Querying → Gaining importance

Understanding web page semantics → Improves User’s search experience

Within a web page → Identify semantic groups

Important → Discovering these semantic blocks

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1(i). The Statement [1]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

A. Significant → Recognize the semantic and coherent segments Page-level → Segment Level

B. Case Example → Medical Encyclopedia MedlinePlus → various choices of medical encyclopedias

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1(i). The Statement [2]1(i). The Statement [2]

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UML ClassDiagram



2. Background




a) Features

b) Main focus

c) Comparison

4. The Proposal: VisHue Algorithm

5. Query by Segment


7. Discussions



2.2. Background: MedlinePlusBackground: MedlinePlus 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 user’s perception identifies

Main focus → Skilled and Semi-skilled usersi. Assumption → Headings → Query attributes

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2(a). Hierarchical Structure2(a). Hierarchical Structure1. 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 domain knowledge (skilled and semi-skilled users) Captures users perception

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2(a).(i). Segmentation ↔ Semantic Query2(a).(i). Segmentation ↔ Semantic Query

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User →Semantic query

and search(In future)

Common Web User

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2 (b). Page-Level Segmentation2 (b). Page-Level Segmentation

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 text of the segment [1].

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2. Background




a) Features

b) Main focus

c) Comparison


5. Query by Segment


7. Discussions



3. Segmentation algorithms3. Segmentation algorithms

i. History → “segmentation” traces back to the year 2001 (continues till 2011)

ii. Various application domains

iii. Various techniques for segmenting

iv. Various terminologies used

v. Proposed → MedlinePlus → items of user’s focus → Query Attributes

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3 (a). Features of Segmentation Algorithm3 (a). Features of Segmentation Algorithm

A. Match and Identify → a user’s 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

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3(b). Main Focus3(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

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3 (b). (i). Segmentation Methods ↔ Web Technologies3 (b). (i). Segmentation Methods ↔ Web Technologies

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3 (b). (ii). Classification of Algorithms3 (b). (ii). Classification of Algorithms

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3(b). (iii). Timeline ↔Techniques3(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 based Method

[7] 2008

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

Site-Oriented Method [1] 2011

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3(c). Comparison3(c). Comparison

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3(c).(i). Main Focus3(c).(i). Main Focus

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

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3(c).(iii). Content Structure by VisHue3(c).(iii). Content Structure by VisHue

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2. Background




a) Features

b) Main focus

c) Comparison


5. Query by Segment


7. Discussions



4. The Proposal: VisHue Algorithm4. The Proposal: VisHue Algorithm

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4. (i). Query Interfaces4. (i). Query Interfaces

Querying v/s Searching

Searching: Recent Trends1. Object based search

2. Block based search

3. Entity based search

Querying: Recent Trends Very few efforts have been done

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2. Background




a) Features

b) Main focus

c) Comparison


5. Query by Segment


7. Discussions



5. Query by Segment5. 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

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5. (i). The QBT interface5. (i). The QBT interface

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Traditional search on MedlinePlus medical encyclopedia

QBT interface

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Title Causes

Symptoms

Post-Care

…

DB


5. (ii). QBT Interface ↔Hierarchical Structure5. (ii). QBT Interface ↔Hierarchical Structure

Labels → Query Attributes

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 all pages of MedlinePlus

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2. Background


b) Segmentation


a) Features

b) Main focus

c) Comparison


5. Query by Segment


7. Discussions



6. Performance Analysis6. Performance Analysis

i. Qualitative comparison with traditional keyword search

ii. Query formulation and interpretation

iii. Quantitative performance analysis of the interface

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6.(i). QBT vs. Keyword Search6.(i). QBT vs. Keyword Search

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6. (ii). Query Formulation: A Comparison6. (ii). Query Formulation: A Comparison

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6. (iii). Query Example6. (iii). Query Example

Query 1: Cases where patient has hypertension but not high blood pressure

QBT query :

Symptoms: “Hypertension”

Symptoms: NOT “High Blood Pressure”

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6. (iv). Query Attributes6. (iv). Query Attributes


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6. (v). Query Results 6. (v). Query Results


6. (vi). Quantitative Performance Analysis6. (vi). Quantitative Performance Analysis

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QBT Query

Symptom: “Hypertension”

Symptom: NOT “High Blood Pressure

Before Procedure: “Stop”

After Procedure: “Normal”

Cause: “High Blood Pressure”

Symptom: “Heart Attack”

Food Source: “Fish”

Side Effect: “Poisoning”

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7. Discussions7. Discussions Content fragments as perceived by skilled and semi-

skilled domain users → determined by web page segmentation 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

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8. Summary and Conclusions8. Summary and Conclusions

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 information accessible to the end-users

C. Query by Segment or Tag (QBT) → An attempt A. Aim → return the users query-able attributes

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ReferencesReferences1. A Site Oriented Method for Segmenting Web Pages”, David Fernandes, Edleno S. de Moura, Altigran S. da

Silva, Berthier Ribeiro-Neto, Edisson Braga, SIGIR’11, 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 61–70, 2007.

7. "A Densitometric Approach to Web Page Segmentation", Christian Kohlschütter, Wolfgang Nejdl, CIKM’08, October 26–30, 2008

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

9. “Template Detection via Data Mining and its Applications” , Ziv Bar Yossef, Sridhar Rajagopalan, In Proceedings of WWW'02, May 7–11, 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 (ICDE�02).

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Web Page Segmentation for Querying Healthcare Repository

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