Semantic Web will be the next big thing in the world of internet. This presentation talks about various approaches that can be used to query the underlying triple store that has all the information.

1. Evolutionary & Swarm
Computing for the Semantic Web
By:
ANKIT A SOLANKI
ANUJ IYER
PRATIK K SHAH

2. What is Web x.0
• Web 1.0
• Web 2.0
• Web 3.0 also known as semantic web.

3. Semantic Web
Why we need it?
Semantic Search. I say ‘google’ is dumb!
Yes I typed there ‘google’

4. Facebook as an example.

7. Concepts
• Triples- Subject Predicate Object.
• Ontology
• Swarm Computing
• Evolutionary Computing
• Local Search

8. A few important challenges
• Storage
• Time
• Storage vs Time
• Distributed nature
• Ownership

9. Evolutionary Algorithms for Querying
http://www.seoskeptic.com/wp-content/uploads/2011/01/rdf-triple.jpg

10. a)Evolutionary Algorithms for
Querying
1) ASK
2) GET
3)Optimizer
http://www.seoskeptic.com/wp-content/uploads/2011/01/rdf-triple.jpg
eRDF Framework

11. ASK
GET(<*,p,o>)
GET(<s,*,o>)
GET(<s,p,*>)

12. GET
• GET(<*,p,o>)
• GET(<s,*,o>)
• GET(<s,p,*>)
<s, p, o>

13. A simplified bookstore data (dataset“A”)
ID Author Title Publisher Year
ISBN0-00-651409-X The Glass Palace 2000id_xyz id_qpr
ID Name Home Page
ID City
Harper Collins London
id_xyz Ghosh, Amitav http://www.amitavghosh.com
Publ. Name
id_qpr
Reference: http://www.w3.org/2009/Talks/1030-Philadelphia-IH/

14. 1st: export your data as a set of relations
Reference: http://www.w3.org/2009/Talks/1030-Philadelphia-IH/

15. Another bookstore data (dataset “F”)
A B D E
1 ID Titre Original
2
ISBN0 2020386682 A13 ISBN-0-00-651409-X
3
6 ID Auteur
7 ISBN-0-00-651409-X A12
11
12
13
Traducteur
Le Palais
des
miroirs
Nom
Ghosh, Amitav
Besse, Christianne
Reference: http://www.w3.org/2009/Talks/1030-Philadelphia-IH/

16. 2nd: export your second set of data
Reference: http://www.w3.org/2009/Talks/1030-Philadelphia-IH/

17. 3rd: start merging your data
Reference: http://www.w3.org/2009/Talks/1030-Philadelphia-IH/

18. 3rd: start merging your data (cont.)
Reference: http://www.w3.org/2009/Talks/1030-Philadelphia-IH/

19. 3rd: merge identical resources
Reference: http://www.w3.org/2009/Talks/1030-Philadelphia-IH/
Relations can be
generated on-the-fly at
query time
 via SQL “bridges”
 scraping HTML pages
 extracting data from
Excel sheets
 etc.

20. Conflict in f:author V/S a:author
Reference: http://www.w3.org/2009/Talks/1030-Philadelphia-IH/

21. Merge with different datasets
e.g., the “dbpedia” project can
extract the “infobox” information
from Wikipedia already…
Reference: http://www.w3.org/2009/Talks/1030-Philadelphia-IH/

22. Merge with Wikipedia data
Reference: http://www.w3.org/2009/Talks/1030-Philadelphia-IH/

23. Merge with Wikipedia data
Reference: http://www.w3.org/2009/Talks/1030-Philadelphia-IH/

24. Simple SPARQL example
Reference: http://www.w3.org/2009/Talks/1030-Philadelphia-IH/

25. Simple SPARQL example
SELECT ?isbn ?price ?currency # note: not ?x!
WHERE { ?isbn a:price ?x. ?x rdf:value ?price. ?x p:currency ?currency.
FILTER(?currency == € }
 Returns: [[<..409X>,50,€], [<..6682>,60,€]]
Reference: http://www.w3.org/2009/Talks/1030-Philadelphia-IH/

26. Optimizer

27. Evolutionary Algorithm
a)Population Evaluation
b)Survivor Selection
c)Offspring Generation

28. b)Computing for Logical Entailment
• Swarm computing – Ants, Bees, Termites etc

29. Ant Colony Optimization

30. Reasoning as a graph traversal

31. Reasoning as a graph traversal

32. Reasoning Agent
• Agent can have schema triple in memory to do
reasoning
• Movement routing can be done either
– Based on properties
– Based on pheromones
– Random
• To control movement, use happiness function

33. Proof of concept

34. THANK YOU