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.
Introduction to Multilingual Retrieval Augmented Generation (RAG)
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,
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, …
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
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