1. Word AdHoc Network: Using Google Core Distance to extract the most relevant information
Presenter : Wei-Hao Huang 　
Authors : Ping-I Chen, Shi-Jen Lin
KBS 2010

2. Outlines Motivation Objectives Methodology Experiments Conclusions
Comments

3. Motivation
Most previous research methods need predictive models, which are based on the training data or Web log of the users’ browsing behaviors.
Those are complexity and the keyword extraction methods are limited to certain areas.

4. Objectives
To present a new algorithm called ‘‘Word AdHoc Network’’ (WANET).
This method needs no pre-processing, and all the executions are real-time.
To extract any keyword sequence from various knowledge domains.
Document
WANET System
Relevant Documents

5. Methodology Word AdHoc Network System Architecture
1-gram filtering method
Part-of-speech
Length of the words
Number of Google search results
Google Core Distance
Hop-by-Hop Routing algorithm
PageRank algorithm
BB’s graph-based clustering algorithm

6. WANET System Architecture

7. 1-gram filtering method
Part-of-speech
NN (common noun, singular), NP (proper noun), DT (determiner), or JJ (adjectives)
Length of the words
At least 3 word
Number of Google search results

8. Google Core Distance
The original algorithm NGD
The New algorithm GCD

9. Hop-by-Hop Routing Algorithm
PageRank algorithm

10. Hop-by-Hop Routing Algorithm
BB’s graph-based clustering algorithm
BB score = 1 6

11. Hop-by-Hop Routing Algorithm

12. Experiments Time variance effect of the Google search results
Execution time
Precision and recall rate
Top-k search results analysis
Dataset：
To select four knowledge domains from the Elsevier Web site, and to chose the top 25 most-downloaded papers in each journal.

13. Time variance effect of the Google search results
To use spearman’s footrule to compare the sequences that were extracted by those two algorithm.

14. Execution time

15. Precision and recall rate

16. Top-k search results analysis

17. Conclusions
To propos a new system that can extract the most important keyword sequence to represent a document
To help users automatically find relevant documents or Web pages.
Future work
To hope it can used in a mobile device or an e-book.

18. Comments Advantages Applications
To extract the most important keyword sequence.
Applications
Information retrieval