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
1
VisHue: Web Page Segmentation for anVisHue: Web Page Segmentation for an
Improved Query Interface for MedlinePlusImproved Query Interface for MedlinePlus
Medical EncyclopediaMedical Encyclopedia
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OutlineOutline
1. 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 Conclusions
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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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5
UML Class
Diagram

OutlineOutline
1. Introduction
2. Background
a) Features
b) Main focus
c) Comparison
4. The Proposal: VisHue Algorithm
5. Query by Segment
7. Discussions
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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 users
i. Assumption → Headings → Query attributes
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2(a). Hierarchical Structure2(a). Hierarchical Structure
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 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
9
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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OutlineOutline
1. Introduction
2. Background
a) Features
b) Main focus
c) Comparison
5. Query by Segment
7. Discussions
1111/12/16

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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OutlineOutline
1. Introduction
2. Background
a) Features
b) Main focus
c) Comparison
5. Query by Segment
7. Discussions
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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 Trends
1. Object based search
2. Block based search
3. Entity based search
Querying: Recent Trends
 Very few efforts have been done
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OutlineOutline
1. Introduction
2. Background
a) Features
b) Main focus
c) Comparison
5. Query by Segment
7. Discussions
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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
27
Traditional search on MedlinePlus
medical encyclopedia
QBT interface
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Title Caus
es
Sympt
oms
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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OutlineOutline
1. Introduction
2. Background
b) Segmentation
a) Features
b) Main focus
c) Comparison
5. Query by Segment
7. Discussions
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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”
33

34
6. (iv). Query Attributes6. (iv). Query Attributes

35
6. (v). Query Results6. (v). Query Results

6. (vi). Quantitative Performance Analysis6. (vi). Quantitative Performance Analysis
36
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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ReferencesReferences
1. 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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Thank youThank you
QuestionsQuestions
4011/12/16

Web Page Segmentation for Querying Healthcare Repository

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