This document proposes an efficient approach for annotating and summarizing user search results from web data. It involves extracting data from search engine results, aligning similar blocks of content, identifying common separators between lines and blocks, analyzing relationships between data units and text nodes, applying annotators to label groups of semantically similar data, and generating a new annotated search results page to efficiently present the extracted information to users in a summarized form.

1. AN EFFICIENT APPROACH
FOR ILLUSTRATING
WEB DATA OF
USER SEARCH RESULTS

2. INPUT URL
WRAPPER
GENERATION
DATA
EXTRACTION
SEARCH
ENGINE
EXTRACTOR
SEARCH
RESULT
RECORD
CONTENT
LINE
EXTRACTION
DATA
ALIGNMENT
ANNOTATORS
LINE
SEPARATOR
BLOCK
EXTRACTION
ANNOTATION
WRAPPER
ANNOTATED
GROUPS
COMBINING
ANNOTATORS
NEW RESULT
PAGE

3. GOOGLE SEARCH CONTENT LINE
•LINK
•TEXT
•LINK-TEXT
•LINK-HEAD
•TEXT-HEAD
•LINK-TEXT-HEAD
•HR LINE
• BLANK LINE

4. GOOGLE SEARCH BLOCKS
To identify similar blocks we check for block similarity on
basis of-
•TYPE distance
•SHAPE distance
•POSITION distance

5. Candidate Content Line Separators
•blank line (e.g., the <p> tag)
•visual line (e.g. the <HR> tag).
(1) the line following an HR-LINE
(2) if there is only one line starting with a number in a block,
this line is a first line;
(3) if only one line in a block has the smallest position code ,
this line is a first line
(4) if there is only one BLANK line in a block, the line
following the BLANK line is the first line.
Fist line of block

6. Tag path
•Sibling
•child
Tag Tree

7. Wrapper Integration

8. Relationships between data unit (U)
and text node (T):
•One-to-One Relationship
T=U
•One-to-Many Relationship
T )U
•Many-to-One Relationship
T (U
•One-To-Nothing Relationship
T!=U

9. Five common features shared by the
data units
•Tag Path (TP)
•Data Content (DC)
•Data Type (DT)
•Adjacency (AD)
•Presentation Style (PS)

10. Alignment Algorithm
Here we will apply our data alignment
algorithm to align the semantically same
data in a group

11. After Alignment Algorithm
Same semantic data are aligned in a column as shown in fig.

12. Output

13. Annotators
• Table annotator
• Query-based annotator
• Schema value annotator
• Frequency-based annotator
• Same-prefix annotator
• Common knowledge based annotator:

14. Let P(L) be the probability that L is correct in identifying a correct
label for a group of data units when L is
applicable. P(L) is essentially the success
rate of L. Specifically, suppose L is applicable
to N cases and among these cases M are
annotated correctly, then P(L)=M/N
Probability

15. Labelling
Suitable annotator is applied and then label the data

16. Annotation Wrapper
attribute= <label; prefix; suffix; separators; unit index>.
•comparing all the suffixes
•compare the prefixes of all the data units

17. New Result Page
This is a new result page with less no. of result record
but all the result data will be annotated and efficient.

18. Tools
Jsoup is a Java library for working with real-world
HTML. It provides a very convenient API for extracting
and manipulating data, using the best of DOM, CSS,
and jquery-like methods.
•Other Tools:
•Webharvest
•Htmlunit

19. [1] H. Zhao, W. Meng, Z. Wu, V. Raghavan, and C. Yu, “FullyAutomatic Wrapper Generation for Search
Engines,” Proc. Int’l Conf. World Wide Web (WWW), 2005.
[2] Y. Zhai and B. Liu, “Web Data Extraction Based on Partial Tree Alignment,” Proc. 14th Int’l Conf.
World Wide Web (WWW ’05),2005.
[3] Y. Lu, H. He, H. Zhao, W. Meng, and C. Yu, “Annotating Structured Data of the Deep Web,” Proc.
IEEE 23rd Int’l Conf. Data Eng. (ICDE), 2007
[4] J. Wang and F.H. Lochovsky, “Data Extraction and Label Assignment for Web Databases,” Proc. 12th
Int’l Conf. World Wide Web (WWW), 2003.
[5] Yiyao Lu, Hai He, Hongkun Zhao, Weiyi Meng, “Annotating Search Results from Web Database”,
IEEE, 2014
References