Leveraging Social data with Semantics

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One of the challenges of social network analysis (SNA) is to understand and exploit on-line social interactions. Research in Semantic Web has provided models to leverage the richness of these interactions that we use to represent these social networks. Classical social network analysis methods have been applied to these semantic representations without fully exploiting their rich expressiveness. Furthermore, we can extend the representation of social links thanks to the semantic relationships found in the vocabularies shared by the members of the social networks. These “enriched” representations of social networks, combined with a similar enrichment of the semantics of the meta-data attached to the shared resources, will allow the elaboration of “shared knowledge graphs”. In this paper we present our approach to analyse such semantic social networks and capture collective intelligence from collaborative interactions.

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  • We presented an 'analysis of a real online social network using semantic web frameworks' at #ISWC2009
    http://www.slideshare.net/ereteog/analysis-of-a-real-online-social-network-using-semantic-web-frameworks
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  • This is the 'in-degree' formula. It counts the number of incoming links and is one of the possible indicators of the reputation of a person. Here links are symmetric therefore in-degree, out-degree and degree values are the same. These slides only give an example of a typical processing in SNA (in-degree calculation) and in RDF/S (transitive propagation of typing across subClassOf and subPropertyOf).
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  • would you please explain this, what we call this Math Function and why we use that on. Slide 9: Fabien Michel d in ( p ) = {x ; rel ( x , p )} ° Guillaume Rémi Marco ° d in (Guillaume ) = 4

    please reply me on najeeb@kth.se

    thx in Advance
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  • Leveraging Social data with Semantics

    1. foster query chats Leveraging Web links mails notify monitor Social profiles data Web 2.0 RDF forum SPARQL tags with approximation networks inference rules Semantics RDFS OWL W3C Workshop on the Future of Social Networking 15‐16 January 2009, Barcelona Fabien Gandon, INRIA,
    2. sociograms and analysis
    3. beetweenness centrality reveals brokers « A place for good ideas » [Burt 1992] [Burt 2004] sociograms and analysis
    4. W3C©
    5. W3C©
    6. W3C®
    7. Fabien Michel Guillaume Rémi Marco Nicolas social network analysis graphs, graphs, graphs, …
    8. Fabien Michel Guillaume Rémi Marco Nicolas social network analysis Man type author creator Person Fabien doc.html sub property sub class title author Man Semantic web is not antisocial semantic web graphs, graphs, graphs, …
    9. Fabien Michel d in ( p ) = {x ; rel ( x , p )} ° Guillaume Rémi Marco ° d in (Guillaume ) = 4 Nicolas social network analysis Man type author creator Person Fabien doc.html sub property sub class title author Man Semantic web is not antisocial semantic web graphs, graphs, graphs, …
    10. Fabien Michel d in ( p ) = {x ; rel ( x , p )} ° Guillaume Rémi Marco ° 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, …
    11. RDF graph classic SNA on semantic web graphs
    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 ( y) in <type,length >
    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 ( y) in <type,length >
    16. [PhD Guillaume Erétéo] long tail distribution of the betweenness centralities [Freeman, 1979] 50 000 projections on 2020 FOAF profiles extracted from flickr.com 
    17. global semantic graphs social
    18. other graphs available too...
    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 industry #bk81 hasLabel hasTag #tag27 global giant graph linking users, actions, knowledge, companies, etc.
    21. #tag92 hasLabel hasTag industries #bk34 hasBookmark #Freddy #Fabien hasBookmark industry #bk81 hasLabel hasTag #tag27 global giant graph linking users, actions, knowledge, companies, etc.
    22. #tag92 hasLabel hasTag industries #bk34 hasBookmark #Freddy #Fabien hasBookmark industry #bk81 hasLabel hasTag #tag27 global giant graph linking users, actions, knowledge, companies, etc.
    23. #tag92 hasLabel hasTag industries #bk34 hasBookmark #Freddy #Fabien hasBookmark industry #bk81 hasLabel hasTag #tag27 global giant graph linking users, actions, knowledge, companies, etc.
    24. link a maximum of graphs
    25. closing messages
    26. open issues
    27. some bridges already exist... POWDER : information about web resource(s) without retrieving the resource(s)
    28. 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
    29. 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
    30. 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.
    31. 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

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