Evolutionary & Swarm Computing for the Semantic Web
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Transcript of Evolutionary & Swarm Computing for the Semantic Web
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