1. ONTOLOGY BASED WEB CRAWLER
SUBMITTED BY:
Sachin Murwariya (9910103457)

2. WHAT IS RSS
RSS is a defined standard for syndicating headlines and other content.
RSS is created using XML or eXtensible Markup Language, which is a markup language
similar to HTML. All fields are defined. Tags are used to denote the field’s classification.
Like HTML, proper construction requires that tags are both opened and closed.
Example: <title> Title of Item in Feed </title>
RSS has been around for more than a decade, but only recently the standard has been
embraced by bloggers, webmasters and large news portals as a means of distributing
Information, in a standardized format.

3. WHAT IS ONTOLOGY BASED WEB CRAWLER
 We present News Personalization using the Semantic
Recommender, a news recommender system which applies
Semantic Web technologies to describe and relate news
contents and user preferences in order to produce enhanced
recommendations

4. APPLICATIONS
◦ User profile construction
◦ Semantics based recommendation:
◦ Delevering categorised news items

5. BENEFITS:
 Help in constant update
 Ease of Operation:
 User can collect information from multiple
sources into a single data stream.

6. PROBLEM STATEMENT
 The extremely large volume of online news has created
an urgent need for tools that let users effectively and
efficiently browse topics, detect temporal trends, and
search news of interest.
 For this we are preparing a ONTOLOGY BASED WEB
CRAWLER to extract valuable information from large
online news collections

7. TEST PLAN
The purpose of testing is quality assurance, verification and
validation, or reliability estimation.
Unit Testing
Component testing
Integration testing
Validation Testing
System Testing

8. ARCHITECTURE :

10. METHODS IN USE:
1. Crawling Algorithm
2. Concept Based Algorithm
3. Recommendation Algorithm

11.  CRAWLING ALGORITHM:

12. Concept Based Algorithm

13. RECOMMENDATION ALGORITHM
 Recommender systems typically produce a list of
recommendations in one of two ways - through
collaborative or content-based filtering.
 Collaborative filtering approaches build a model
from a user's past behavior (items previously
purchased or selected )
 Then use that model to predict items that the
user may have an interest in Content-based
filtering approaches utilize a series of discrete
characteristics of an item in order to recommend
additional items with similar properties.

14. IMPLEMENTATION
 Login Page

15. Search using keyword:

16. TEST PLAN
The purpose of testing is quality assurance, verification and
validation, or reliability estimation.
Unit Testing
Component testing
Integration testing
Validation Testing
System Testing

17. REFERENCES
[I] Ching Hsu .Taiwan, National Formosa University,2011.
[2] I.Jntema, F.Frasincar, F.Goossen and F.Hogenboom, Erasmus
University Rotterdam, 2010
[3] M.Shea and M.Levene, University of London, UK, 2011
[4] Z.Rui-juan , Z. Yang-sen, 9th International Conference,2012
[5] S.Saha, A. Sajjanhar, S. Gao, R.Dew and Y. Zhao,0 IEEE 10th
International Conference,2010
[6] Sajjanhar, A. Ying Zhao, ChinaGrid Annual Conference
(ChinaGrid), 2012
[7] S. Sarumathi , (PRIME)International Conference,2012
[8] F.Goossen, W.IJntema, F.Frasincar, F.Hogenboom, U.Kaymak,
Erasmus University Rotterdam.2011

18. THANK YOU