1. CSCE822 Data Mining and Warehousing
Lecture 19
Web Data Mining
MW 4:00PM-5:15PM
Dr. Jianjun Hu mleg.cse.sc.edu/edu/csce822
University of South Carolina
Department of Computer Science and Engineering

2. Outline Web mining applications Background on Web Search
VIPS (VIsion-based Page Segmentation)
Block-based Web Search
Block-based Link Analysis
Noise:
First, web pages usually do not contain pure content. A web page typically contains various types of materials that are not related to the topic of the web-page.
Multiple topics:
Secondly, a web page usually contains multiple topics, for example, a news page containing many different comments on a particular event or politician, or a conference web page containing sponsors from different companies and organizations. Although traditional documents also often have multiple topics, they are less diverse so that the impact on retrieval performance is smaller.
There exist some new characteristics in web pages:
Two-Dimension Logical Structure – Different from free-text documents, web pages have a 2-D view and a more sophisticated internal content structure. Each block of a web page could have relationships with blocks from up to four directions and contain or be contained in some other blocks. A content structure in semantic level exists for most pages and can be used to enhance retrieval.
Visual Layout Presentation – To facilitate browsing and attract attention, web pages usually contain much visual information in the tags and properties in HTML [20]. Typical visual hints include lines, blank areas, colors, pictures, fonts, etc. Visual cues are very helpful to detect the semantic regions in web pages.
Data Mining: Principles and Algorithms
5/12/2018

3. Web mining Categories of mining
Web usage mining: analyse and discover interesting patterns of user’s usage data on the web. Server logs
Web content mining: discover useful information from text, image, audio or video data in the web
Text mining, image mining, etc
Web structure mining: using graph theory to analyse the node and connection structure of a web site
Link mining in search engines

4. Research Problems in Web Mining
Google: what is the next step?
How to find the pages that match approximately the sohpisticated documents, with incorporation of user-profiles or preferences?
Look back of Google: inverted indicies
Construction of indicies for the sohpisticated documents, with incorporation of user-profiles or preferences
Similarity search of such pages using such indicies
Data Mining: Principles and Algorithms
5/12/2018

5. Search Engine – Two Rank Functions
Ranking based on link structure analysis
Web Pages
Meta Data
Forward
Index
Inverted
Link
Backward Link
(Anchor Text)
Web Topology
Graph
Web Page Parser
Indexer
Anchor Text
Generator
Web Graph
Constructor
Importance Ranking
(Link Analysis)
Rank Functions
URL
Dictioanry
Term Dictionary
(Lexicon)
Search
Relevance Ranking
Similarity based on content or text
Data Mining: Principles and Algorithms
5/12/2018

6. Relevance Ranking Inverted index
- A data structure for supporting text queries
- like index in a book
aalborg , , ….. .
arm , 19, 29, 98, 143, ...
armada , 457, 789, ...
armadillo , 2134, 3970, ...
armani , 256, 372, 511, ...
zz , 1189, 3209, ...
indexing
disks with
documents
inverted index

7. Page Rank Algorithm (Intuitive)
Basic idea
significance of a page is determined by the significance of the pages linking to it

8. The PageRank Algorithm
More precisely:
Link graph: adjacency matrix A,
Constructs a probability transition matrix M by renormalizing each row of A to sum to 1
Treat the web graph as a markov chain (random surfer)
The vector of PageRank scores p is then defined to be the stationary distribution of this Markov chain. Equivalently, p is the principal right eigenvector of the transition matrix
Data Mining: Principles and Algorithms
5/12/2018

9. Layout Structure
Compared to plain text, a web page is a 2D presentation
Rich visual effects created by different term types, formats, separators, blank areas, colors, pictures, etc
Different parts of a page are not equally important
Title: CNN.com International
H1: IAEA: Iran had secret nuke agenda
H3: EXPLOSIONS ROCK BAGHDAD …
TEXT BODY (with position and font type): The International Atomic Energy Agency has concluded that Iran has secretly produced small amounts of nuclear materials including low enriched uranium and plutonium that could be used to develop nuclear weapons according to a confidential report obtained by CNN…
Hyperlink:
URL:
Anchor Text: AI oaeda…
Image:
URL:
Alt & Caption: Iran nuclear …
Anchor Text: CNN Homepage News …
Data Mining: Principles and Algorithms
5/12/2018

10. Web Page Block—Better Information Unit
Web Page Blocks
Importance = Med
Importance = Low
Importance = High
Data Mining: Principles and Algorithms
5/12/2018

11. Motivation for VIPS (VIsion-based Page Segmentation)
Problems of treating a web page as an atomic unit
Web page usually contains not only pure content
Noise: navigation, decoration, interaction, …
Multiple topics
Different parts of a page are not equally important
Web page has internal structure
Two-dimension logical structure & Visual layout presentation
> Free text document
< Structured document
Layout – the 3rd dimension of Web page
1st dimension: content
2nd dimension: hyperlink
Noise:
First, web pages usually do not contain pure content. A web page typically contains various types of materials that are not related to the topic of the web-page.
Multiple topics:
Secondly, a web page usually contains multiple topics, for example, a news page containing many different comments on a particular event or politician, or a conference web page containing sponsors from different companies and organizations. Although traditional documents also often have multiple topics, they are less diverse so that the impact on retrieval performance is smaller.
There exist some new characteristics in web pages:
Two-Dimension Logical Structure – Different from free-text documents, web pages have a 2-D view and a more sophisticated internal content structure. Each block of a web page could have relationships with blocks from up to four directions and contain or be contained in some other blocks. A content structure in semantic level exists for most pages and can be used to enhance retrieval.
Visual Layout Presentation – To facilitate browsing and attract attention, web pages usually contain much visual information in the tags and properties in HTML [20]. Typical visual hints include lines, blank areas, colors, pictures, fonts, etc. Visual cues are very helpful to detect the semantic regions in web pages.
Data Mining: Principles and Algorithms
5/12/2018

12. Is DOM a Good Representation of Page Structure?
Page segmentation using DOM
Extract structural tags such as P, TABLE, UL, TITLE, H1~H6, etc
DOM is more related content display, does not necessarily reflect semantic structure
How about XML?
A long way to go to replace the HTML
DOM in general provides a useful structure for a web page. But tags such as TABLE and P are used not only for content organization, but also for layout presentation. In many cases, DOM tends to reveal presentation structure other than content structure, and is often not accurate enough to discriminate different semantic blocks in a web page.
Data Mining: Principles and Algorithms
5/12/2018

13. VIPS Algorithm Motivation:
In many cases, topics can be distinguished with visual clues. Such as position, distance, font, color, etc.
Goal:
Extract the semantic structure of a web page based on its visual presentation.
Procedure:
Top-down partition the web page based on the separators
Result
A tree structure, each node in the tree corresponds to a block in the page.
Each node will be assigned a value (Degree of Coherence) to indicate how coherent of the content in the block based on visual perception.
Each block will be assigned an importance value
Hierarchy or flat
Data Mining: Principles and Algorithms
5/12/2018

14. VIPS: An Example A hierarchical structure of layout block
A Degree of Coherence (DOC) is defined for each block
Show the intra coherence of the block
DoC of child block must be no less than its parent’s
The Permitted Degree of Coherence (PDOC) can be pre-defined to achieve different granularities for the content structure
The segmentation will stop only when all the blocks’ DoC is no less than PDoC
The smaller the PDoC, the coarser the content structure would be
Data Mining: Principles and Algorithms
5/12/2018

15. Example of Web Page Segmentation (1)
( DOM Structure )
( VIPS Structure )
Data Mining: Principles and Algorithms
5/12/2018

16. Example of Web Page Segmentation (2)
( DOM Structure )
( VIPS Structure )
Can be applied on web image retrieval
Surrounding text extraction
Data Mining: Principles and Algorithms
5/12/2018

17. Web Page Block—Better Information Unit
Page Segmentation
Vision based approach
Block Importance Modeling
Statistical learning
Web Page Blocks
Importance = Med
Importance = Low
Importance = High
Data Mining: Principles and Algorithms
5/12/2018

18. Block-based Web Search
Index block instead of whole page
Block retrieval
Combing DocRank and BlockRank
Block query expansion
Select expansion term from relevant blocks
Data Mining: Principles and Algorithms
5/12/2018

19. Experiments Dataset TREC 2001 Web Track TREC 2002 Web Track
WT10g corpus (1.69 million pages), crawled at 1997.
50 queries (topics )
TREC 2002 Web Track
.GOV corpus (1.25 million pages), crawled at 2002.
49 queries (topics )
Retrieval System
Okapi, with weighting function BM2500
Preprocessing
Stop-word list (about 220)
Do not use stemming
Do not consider phrase information
Tune the b, k1 and k3 to achieve the best baseline
Data Mining: Principles and Algorithms
5/12/2018

20. Block Retrieval on TREC 2001 and TREC 2002
These figures and tables shows the experimental results on block retrieval using different page segmentation methods. FullDoc is not listed here since it will always get the baseline. The third column shows the results of using single-best block rank, and the last column shows the results of combining block rank and document rank, with α being optimal for each specific method. The dependency between and α is illustrated in Figure 3, in which all the curves converge to the baseline when α = 1.
As can be seen from Figures and tables, if only the best block from each document is used to rank pages, DomPS performs the worst and FixedPS a little bit better, both of which are worse than the baseline for both data sets. VIPS is slightly better than baseline in TREC 2001 but fails to exceed baseline in TREC 2002, though it is the best among all the methods. CombPS wins TREC 2001, but is worse than VIPS in TREC For TREC 2002, no method can outperform the baseline.
When block rank is combined with the original document rank, the performance of all these four methods increases significantly and is better than the baseline. This shows the effect of rank combination, similar to traditional passage retrieval [4]. DomPS is still the worst, and FixedPS is slightly better. VIPS and CombPS are still better than the former two and show similar comparison characteristics to the non-combining situations, except that result of CombPS (0.2379) is now much closer to that of VIPS (0.2408) in TREC 2002.
Furthermore, from Figures it can be seen that the winner for either data set shows a consistent improvement compared to the other methods, and thus does not win by chance. For TREC 2001 CombPS gets better performance almost in every combination, and for TREC 2002 CombPS shares rather similar trends as VIPS when α exceeds 0.4.
TREC 2001 Result
TREC 2002 Result
Data Mining: Principles and Algorithms
5/12/2018

21. Query Expansion on TREC 2001 and TREC 2002
We perform each web page segmentation method and choose top-ranked blocks (top-ranked documents for FullDoc) to do query expansion. In Table 4, the value for each segmentation method is the best performance achieved seen from Figure 5. Figure 5 illustrates the values given different number of blocks (documents in FullDoc). Figure 6 also shows the same comparison for TREC 2001 by using average precision as evaluation metric.
From the experimental results, a general conclusion can be made that partitioning pages into blocks can improve the performance of query expansion, regardless of which page segmentation method is used. Furthermore, “good” segmentation method can improve the performance significantly and stably. Among all the page segmentation methods, FullDoc does nothing and thus may get good results (in TREC 2001) or bad results (in TREC 2002), but FixedPS, VIPS and CombPS can always get better results. DomPS is still unstable and sometimes even worse than the baseline. The performance of VIPS and FixedPS is similar, except that VIPS shows better performance in AvP, and that normally they achieve the peak at different number of blocks. CombPS, on the other hand, is always the best method and could achieve at most 17.3% improvement in and 28.5% in AvP.
TREC 2001 Result
TREC 2002 Result
Data Mining: Principles and Algorithms
5/12/2018

22. Block-level Link Analysis
Data Mining: Principles and Algorithms
5/12/2018 C

23. A Sample of User Browsing Behavior
Data Mining: Principles and Algorithms
5/12/2018

24. Improving PageRank using Layout Structure
Z: block-to-page matrix (link structure)
X: page-to-block matrix (layout structure)
Block-level PageRank:
Compute PageRank on the page-to-page graph
BlockRank:
Compute PageRank on the block-to-block graph
Data Mining: Principles and Algorithms
5/12/2018

25. Using Block-level PageRank to Improve Search
Search = a * IR_Score + (1- a) * PageRank
Block-level PageRank achieves 15-25% improvement over PageRank (SIGIR’04)
Data Mining: Principles and Algorithms
5/12/2018

26. Mining Web Images Using Layout & Link Structure (ACMMM’04)
Data Mining: Principles and Algorithms
5/12/2018

27. Image Graph Model & Spectral Analysis
Block-to-block graph:
Block-to-image matrix (container relation): Y
Image-to-image graph:
ImageRank
Compute PageRank on the image graph
Image clustering
Graphical partitioning on the image graph
5/12/2018
Data Mining: Principles and Algorithms

28. ImageRank Relevance Ranking Importance Ranking Combined Ranking
5/12/2018
Data Mining: Principles and Algorithms

29. ImageRank vs. PageRank Dataset 26.5 millions web pages
11.6 millions images
Query set
45 hot queries in Google image search statistics
Ground truth
Five volunteers were chosen to evaluate the top 100 results re-turned by the system (iFind)
Ranking method
Data Mining: Principles and Algorithms
5/12/2018

30. ImageRank vs PageRank
Image search accuracy using ImageRank and PageRank. Both of them achieved their best results at =0.25.
Data Mining: Principles and Algorithms
5/12/2018

31. Summary Papers VIPS demo & dll
More improvement on web search can be made by mining webpage Layout structure
Leverage visual cues for web information analysis & information extraction
Demos:
Papers
VIPS demo & dll
Data Mining: Principles and Algorithms
5/12/2018

32. Slides Credits
Slides in this presentation are partially based on the work of
Han. Textbook Slides