Download as PDF, PPTX
![beetweenness centrality reveals brokers
« A place for good ideas » [Burt 1992] [Burt 2004]
sociograms and analysis](https://image.slidesharecdn.com/leveragingsocialdatawithsemantics-111116094447-phpapp02/85/Leveraging-social-data-with-semantics-3-320.jpg)
![[PhD Guillaume Erétéo]
Semantic Social Network Analysis
SPARQL + Extensions
social data
Social Network Analysis Ontology
FOAF, RELATIONSHIP, SIOC, Domain
DC, SKOS, SCOT, DOAP, MOAT Ontologies
RDF/S, OWL RDFa GRDDL
Wrappers & web 2.0 APIs XML µformats
leveraging the full semantic web stack](https://image.slidesharecdn.com/leveragingsocialdatawithsemantics-111116094447-phpapp02/85/Leveraging-social-data-with-semantics-13-320.jpg)
![[PhD Guillaume Erétéo]
parameterized in‐degree
o
d in <type,length > ( y)](https://image.slidesharecdn.com/leveragingsocialdatawithsemantics-111116094447-phpapp02/85/Leveraging-social-data-with-semantics-14-320.jpg)
![ADD { [PhD Guillaume Erétéo]
?y semsna:hasInDegree _:b0
_:b0 semsna:forProperty param[type]
_:b0 rdf:value ?indegree
_:b0 semsna:hasLength param[length]
}
SELECT ?y count(?x) as ?indegree {
?x $path ?y
filter(match($path, star(param[type])))
filter(pathLength($path)<= param[length])
} group by ?y
parameterized in‐degree
o
d in <type,length > ( y)](https://image.slidesharecdn.com/leveragingsocialdatawithsemantics-111116094447-phpapp02/85/Leveraging-social-data-with-semantics-15-320.jpg)
![[PhD Guillaume Erétéo]
long tail distribution of the betweenness centralities
50 000 projections on 2020 FOAF profiles extracted from flickr.com [Freeman, 1979]](https://image.slidesharecdn.com/leveragingsocialdatawithsemantics-111116094447-phpapp02/85/Leveraging-social-data-with-semantics-16-320.jpg)
![[PhD Freddy Limpens]
e.g. capture bookmarks and their tags
co‐tags extracted from delicious for “ademe”
6054 bookmarks, 16 users, 5153 tags, 5969 resources](https://image.slidesharecdn.com/leveragingsocialdatawithsemantics-111116094447-phpapp02/85/Leveraging-social-data-with-semantics-19-320.jpg)
Uploaded byViệt Long Plaza
Leveraging social data with semantics
This document discusses using semantic web technologies for social network analysis. It describes how social network data can be represented using ontologies like FOAF, SIOC, and relationship to form semantic social graphs. Classic social network analysis metrics like in-degree and betweenness centrality can then be computed on these graphs using SPARQL queries extended with social network analysis functions. Global semantic graphs can also be constructed by linking different domain ontologies and user data from social networks and bookmarks. Some bridges already exist between semantic web and mobile/enterprise applications.
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- 1. Sieu thi dienmay Viet Long - www.vietlongplaza.com.vn foster query chats Leveraging Web links mails notify monitor profiles Social RDF data SPARQL forum Web 2.0 tags networks with approximation inference rules Semantics OWL RDFS W3C Workshop on the Future of Social Networking 15‐16 January 2009, Barcelona Fabien Gandon, INRIA,
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- 3. beetweenness centrality reveals brokers « A place for good ideas » [Burt 1992] [Burt 2004] sociograms and analysis
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- 7. Fabien Michel Marco Guillaume Rémi Nicolas social network analysis graphs, graphs, graphs, …
- 8. Fabien Michel Marco Guillaume Rémi Nicolas social network analysis Man type author creator Person Fabien doc.html title sub property sub class Semantic web is not antisocial author Man semantic web graphs, graphs, graphs, …
- 9. Fabien Michel Marco Guillaume Rémi d in ( p ) = {x ; rel ( x , p )} ° ° d in (Guillaume ) = 4 Nicolas social network analysis Man type author creator Person Fabien doc.html title sub property sub class Semantic web is not antisocial author Man semantic web graphs, graphs, graphs, …
- 10. Fabien Michel Marco Guillaume Rémi d in ( p ) = {x ; rel ( x , p )} ° ° d in (Guillaume ) = 4 Nicolas social network analysis Man creator type author creator Person Fabien doc.html type sub property sub class title Person author Man Semantic web is not antisocial semantic web graphs, graphs, graphs, …
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- 12. RDF graph non‐typed graphs classic SNA on semantic web graphs
- 13. [PhD Guillaume Erétéo] Semantic Social Network Analysis SPARQL + Extensions social data Social Network Analysis Ontology FOAF, RELATIONSHIP, SIOC, Domain DC, SKOS, SCOT, DOAP, MOAT Ontologies RDF/S, OWL RDFa GRDDL Wrappers & web 2.0 APIs XML µformats leveraging the full semantic web stack
- 14. [PhD Guillaume Erétéo] parameterized in‐degree o d in <type,length > ( y)
- 15. ADD { [PhD Guillaume Erétéo] ?y semsna:hasInDegree _:b0 _:b0 semsna:forProperty param[type] _:b0 rdf:value ?indegree _:b0 semsna:hasLength param[length] } SELECT ?y count(?x) as ?indegree { ?x $path ?y filter(match($path, star(param[type]))) filter(pathLength($path)<= param[length]) } group by ?y parameterized in‐degree o d in <type,length > ( y)
- 16.
- 17. global semantic graphs social
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- 19. [PhD Freddy Limpens] e.g. capture bookmarks and their tags co‐tags extracted from delicious for “ademe” 6054 bookmarks, 16 users, 5153 tags, 5969 resources
- 20. #Freddy hasBookmark #bk81 industry hasLabel hasTag #tag27 global giant graph linking users, actions, knowledge, companies, etc.
- 21. #tag92 hasLabel hasTag industries #bk34 hasBookmark #Freddy #Fabien hasBookmark #bk81 industry hasLabel hasTag #tag27 global giant graph linking users, actions, knowledge, companies, etc.
- 22. #tag92 hasLabel hasTag industries #bk34 hasBookmark #Freddy #Fabien hasBookmark #bk81 industry hasLabel hasTag #tag27 global giant graph linking users, actions, knowledge, companies, etc.
- 23. #tag92 hasLabel hasTag industries #bk34 hasBookmark #Freddy #Fabien hasBookmark #bk81 industry hasLabel hasTag #tag27 global giant graph linking users, actions, knowledge, companies, etc.
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- 29. some bridges already exist... POWDER : information about web resource(s) without retrieving the resource(s)
- 30. some bridges already exist... POWDER : information about web resource(s) without retrieving the resource(s) Vocabularies : Device Description Vocabulary (MWI), Delivery Context Ontology (UWA), CC/PP Structure and Vocabularies
- 31. some bridges already exist... POWDER : information about web resource(s) without retrieving the resource(s) Vocabularies : Device Description Vocabulary (MWI), Delivery Context Ontology (UWA), CC/PP Structure and Vocabularies Semantic Web applications on mobiles: DBPedia Mobile, i‐MoCo (250 million triples), myCampus
- 32. ISICIL project social web applications and semantic web frameworks for corporate applications. • enterprise social networking; • business intelligence, watching, monitoring; • communities of interest, of practice; • web 2.0 & corporate processes integration; • trust, privacy, confidentiality.
- 33. http://www.slideshare.net Person slidesOn Fabien Gandon type name http://ns.inria.fr/fabien.gandon/foaf#me identifies email Fabien.Gandon@sophia.inria.fr