Evolutionary & Swarm Computing for the Semantic Web

By:

ANKIT A SOLANKIANUJ IYER

PRATIK K SHAH

What is Web x.0

• Web 1.0• Web 2.0• Web 3.0 also known as semantic web.

Semantic Web

Why we need it?Semantic Search. I say ‘google’ is dumb!

Yes I typed there ‘google’

Facebook as an example.

Concepts

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

A few important challenges

• Storage• Time• Storage vs Time• Distributed nature• Ownership

Evolutionary Algorithms for Querying

http://www.seoskeptic.com/wp-content/uploads/2011/01/rdf-triple.jpg

a)Evolutionary Algorithms for Querying

1) ASK2) GET3)Optimizer

http://www.seoskeptic.com/wp-content/uploads/2011/01/rdf-triple.jpg

eRDF Framework

ASK

GET(<*,p,o>)GET(<s,*,o>)GET(<s,p,*>)

GET

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

A simplified bookstore data (dataset“A”)

ID Author Title Publisher YearISBN0-00-651409-X The Glass Palace 2000id_xyz id_qpr

ID Name Home Page

ID CityHarper Collins London

id_xyz Ghosh, Amitav http://www.amitavghosh.com

Publ. Nameid_qpr

Reference: http://www.w3.org/2009/Talks/1030-Philadelphia-IH/

1st: export your data as a set of relations

Reference: http://www.w3.org/2009/Talks/1030-Philadelphia-IH/

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 Auteur7 ISBN-0-00-651409-X A12

11

12

13

TraducteurLe Palais des miroirs

NomGhosh, AmitavBesse, Christianne

Reference: http://www.w3.org/2009/Talks/1030-Philadelphia-IH/

2nd: export your second set of data

Reference: http://www.w3.org/2009/Talks/1030-Philadelphia-IH/

3rd: start merging your data

Reference: http://www.w3.org/2009/Talks/1030-Philadelphia-IH/

3rd: start merging your data (cont.)

Reference: http://www.w3.org/2009/Talks/1030-Philadelphia-IH/

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.

Conflict in f:author V/S a:author

Reference: http://www.w3.org/2009/Talks/1030-Philadelphia-IH/

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/

Merge with Wikipedia data

Reference: http://www.w3.org/2009/Talks/1030-Philadelphia-IH/

Merge with Wikipedia data

Reference: http://www.w3.org/2009/Talks/1030-Philadelphia-IH/

Simple SPARQL example

Reference: http://www.w3.org/2009/Talks/1030-Philadelphia-IH/

Simple SPARQL exampleSELECT ?isbn ?price ?currency # note: not ?x!WHERE { ?isbn a:price ?x. ?x rdf:value ?price. ?x p:currency ?currency. FILTER(?currency == € }

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/

Optimizer

Evolutionary Algorithm

a)Population Evaluation

b)Survivor Selection

c)Offspring Generation

b)Computing for Logical Entailment

• Swarm computing – Ants, Bees, Termites etc

Ant Colony Optimization

Reasoning as a graph traversal

Reasoning as a graph traversal

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

Proof of concept

THANK YOU