ICEIS 2013 Keynote talk on semantic and social (intra)webs

1. semantic and social (intra)webs
fabien gandon, @fabien_gandon, http://fabien.info
leader of the Wimmics research team (INRIA, CNRS, Univ. Nice)
W3C Advisory Committee Representative for INRIA

2. eb- nstrumented
an- achine nteractions,
ommunities, and emantics
a joint research team between INRIA Sophia Antipolis – Méditerranée
and I3S (CNRS and University Nice Sophia Antipolis).

3. membersHead (and INRIA contact): Fabien Gandon
Vice Head (and I3S contact): Catherine Faron-Zucker
Researchers:
1. Michel Buffa, MdC (UNS)
2. Olivier Corby, CR1 (INRIA)
3. Alain Giboin, CR1 (INRIA)
4. Nhan Le Thanh, Pr. (UNS)
5. Isabelle Mirbel, MdC, HDR (UNS)
6. Peter Sander, Pr. (UNS)
7. Andrea G. B. Tettamanzi, Pr. (UNS)
8. Serena Villata, RP (INRIA)
Post-doc:
1. Zeina Azmeh (I3S)
2. Elena Cabrio (CORDIS)
Research engineers:
1. Julien Cojan (INRIA, Ministry of Culture)
2. Christophe Desclaux (Boost your code)
3. Amosse Edouard (I3S)
PhD students:
1. Pavel Arapov, 3rd year (EDSTIC-I3S)
2. Franck Berthelon, 4th year (UNS-EDSTIC)
3. Ahlem Bouchahda, 3rd year (UNS-SupCom Tunis)
4. Khalil Riad Bouzidi, 3rd year (UNS-CSTB)
5. Luca Costabello, 3rd year (INRIA-CORDI)
6. Papa Fary Diallo, 2nd year (AUF-UGB-INRIA)
7. Corentin Follenfant, 3rd year (SAP)
8. Zide Meng, 1st year (INRIA ANR OCTOPUS)
9. Maxime Lefrançois, 3rd year (EDSTIC-INRIA)
10. Nguyen Thi Hoa Hue, 2nd year (Vietnam-CROUS)
11. Nicolas Marie, 3rd year (Bell-ALU, INRIA)
12. Rakebul Hasan, 2nd year (INRIA ANR-Kolflow)
13. Oumy Seye, 2nd year, (INRIA Rose Dieng allocation)
Assistants:
• Christine Foggia (INRIA)
• Magali Richir (I3S)

4. research problem
socio-semantic networks: combining formal
semantics and social semantics on the web

5. web landscape and graphs
(meta)data of the relations and the resources of the web
…sites …social …of data …of services
+ + + +…
web…
= +
…semantics
+ + + +…= +
typed
graphs
web
(graphs)
networks
(graphs)
linked data
(graphs)
workflows
(graphs)
schemas
(graphs)

6. challengesanalyzing, modeling, formalizing and
implementing graph-based social semantic web
applications for communities
 multidisciplinary approach for analyzing and modeling
the many aspects of intertwined information systems
communities of users and their interactions
 formalizing and reasoning on these models
new analysis tools and indicators
new functionalities and better management

7. diffusion
Master IFI: from KIS to Web
– gradual changes to the courses
– then replace the master by a new one
Standardization participation
– Working groups: RDF 1.1, SPARQL 1.1
– INRIA Advisory Committee Representative
Open-source and CeCILL-C free software

8. CORESE/ KGRAM

9. projectsisicil.inria.fr (ANR)
 enterprise social networking
 business intelligence, watching, monitoring
 communities of interest, of practice, of experts
datalift.org (ANR)
 from raw public data to interlinked data and schemas
 a platform and documentation to assist the process
 validation on real datasets
kolflow.univ-nantes.fr (ANR)
 reduce the overhead of communities building knowledge
 federated semantic: distributed blackboard for man-machine coop.
dbpedia.fr (Ministry of Culture)
 extract and publish data and facts from French version of wikipedia
ocktopus (ANR)
 dynamic typed network of user-generated web sites (e.g. forums)
DATALIFT
...

10. ISICILsemantic and social intraweb for
corporate intelligence and watch
ANR project CONTINT 2009-2011

11. <background_knowledge>

13. based on Clay Shirky, TED 2009
1↔1
1→n 1→n
1→n

14. read-write web
internet n↔n
classical web 1↔n
wiki, (µ)blog, forum, etc. n↔n
Ward Cunningham, 94

15. social web networks

16. social networks
networking is not that new e.g. commerce
social network analysis
beginning of the 20th century

17. collaboratively create
and manage tags to
annotate and
categorize content
SOCIAL TAGGING

19. a crowd of users creating massive
categorizations
U
T
R

20. semantic web
mentioned by Tim BL
in 1994 at WWW
[Tim Berners-Lee 1994, http://www.w3.org/Talks/WWW94Tim/]

21. W3C®SEMANTIC WEB STACK

22. W3C®
A WEB OF
LINKED DATA

23. RDFstands for
Resource: pages, images, videos, ...
everything that can have a URI
Description: attributes, features, and
relations of the resources
Framework: model, languages and
syntaxes for these descriptions

24. RDFis a triple model i.e. every
piece of knowledge is broken down into
( subject , predicate , object )

25. doc.html has for author Fabien
and has for theme Music

26. doc.html has for author Fabien
doc.html has for theme Music

27. ( doc.html , author , Fabien )
( doc.html , theme , Music )
( subject , predicate , object )

28. RDFtriples can be seen as arcs
of a graph (vertex,edge,vertex)

29. Fabien
author
doc.html
theme
Music

31. http://ns.inria.fr/fabien.gandon#me
http://inria.fr/schema#author
http://inria.fr/rr/doc.html
http://inria.fr/schema#theme
"Music"

32. principles
 use RDF as data format
 use URIs as names for things
 use HTTP URIs so that people can look up those names
 when someone looks up a URI, provide useful information
(RDF, HTML, etc.) using content negotiation
 include links to other URIs so that related things can be
discovered

33. May 2007 April 2008 September 2008
March 2009
September 2010
Linking Open Data
Linking Open Data cloud diagram, by Richard Cyganiak and Anja Jentzsch. http://lod-cloud.net/
September 2011
0
100
200
300
400
10/10/2006 28/04/2007 14/11/2007 01/06/2008 18/12/2008 06/07/2009 22/01/2010 10/08/2010 26/02/2011 14/09/2011 01/04/2012

34. query with SPARQL
SPARQL Protocol and RDF
Query Language

35. e.g. DBpedia

36. ->
Ontology ontologies

37. W3C®
PUBLISHED
SEMANTICS
OF SCHEMAS

38. RDFS to declare classes of
resources, properties, and
organize their hierarchy
Document
Report
creator
author
Document Person

39. OWLin one…
enumeration
intersection
union
complement
 disjunction
restriction!
cardinality
1..1
algebraic properties
equivalence
[>18]
disjoint union
value restrict.
disjoint properties
qualified cardinality
1..1
!
individual prop. neg
chained prop.


keys
…

40. SKOS
knowledge
thesauri,
classifications,
subjects,
taxonomies,
folksonomies,
... controlled
vocabulary
40

41. natural language expressions to refer to concepts
41
inria:CorporateSemanticWeb
skos:prefLabel "corporate semantic web"@en;
skos:prefLabel "web sémantique d'entreprise"@fr;
skos:altLabel "corporate SW"@en;
skos:altLabel "CSW"@en;
skos:hiddenLabel "web semantique d'entreprise"@fr.
labels

42. between conceptsinria:CorporateSemanticWeb
skos:broader w3c:SemanticWeb;
skos:narrower inria:CorporateSemanticWiki;
skos:related inria:KnowledgeManagement.
relations

43. </background_knowledge>

44. Social web
Semantic web
Linked data
Social semantic web
ISICIL: semantic social intraweb

45. isicil.inria.fr
• enterprise social networking
• business intelligence, watching, monitoring
• communities of interest, of practice, of experts

46. proposed overview…
integrating requirement analysis methods
examples of challenges and derived functionalities
overview of this open-source platform
http://isicil.inria.fr

47. MERGING METHODOLOGIES
extracts of the requirement analysis and specifications

48.  analyze and model key business processes
 Analyze interactions between members of the group
ADEME « roadmap for urban mobility »
 campaigns of questionnaires at Orange Labs
 trend analysis of intelligence market and watch
 comparison of the APIs, widgets and other applications
usage analysis an specification

49. ex. study of transformations: existing  target

50. convergence matrix
detections of needs or redundancies in key scenarios
Etapes des scénarios Fonctionnalités identifiées Fonctions SI
Présenter problématique au SVIC Mailing, Q&A Envoyer
Demander ce qui est
incontournable et ce que font les
autres ingénieurs
Consultation d’experts
Extraire, filtrer
Prendre en compte demande Workflow, Outils de collaboration communiquer
Préparer requêtes
Moteur de recherche, équation de
recherche
rechercher
Recueillir résultats Abonnement, push… Extraire, annoter
Vérifier pertinence des résultats Analyses, outils de filtrage filtrer
Informer l'ingénieur
Messagerie électronique, chat,
vidéo-conférence
envoyer
S'approprier les résultats et les
requêtes
Equation de recherche, profil, tags Annoter, organiser
Devenir le destinataire des
alertes
Diffusion par profil diffuser

51. business intelligence market analysis

52. proposing functionalities & prototypes
Prioritization of functionalitiesFrequent functionalities and
dependencies

53. EXAMPLES OF FONCTIONNALITIES
examples of challenges and implementations (web 3.0 & enterprise 3.0)

55. assited structuring of folksonomies
flat folksonomiesweb 2.0
[Limpens et al.]
pollution
soil pollution
has narrower
pollutant energy
related related
thesaurus
?
SKOS

56. global giant graph
link users, actions, knowledge, resources, groups, etc.
#Freddy
#bk81
hasBookmark
hasTag
#tag27
industry
hasLabel
#Fabien
#bk34
#tag92
industries
hasBookmark
hasLabel
hasTag

57. pollution
pollutantpollution
pollutionpollutionpollutionpollution Soil pollutions
edition distances

58. 0
0,1
0,2
0,3
0,4
0,5
0,6
0,7
0,8
0,9
1
Close match Aire sous f cm_A
Tag1 broader than Tag2 Aire sous f t1bt2_A
Related Aire sous f rel_A
evaluating distancesc.f. [Limpens et al.]

59. determine thesaurus relations
Comparison of the mean value
of the JaroWinkler metric for
each type of semantic relation
Mean value of the difference
s(t1,t2) - s(t2,t1) with s being the
Monge-Elkan QGram metric for
each set of tag pairs.

60. Node size ↔ InDegree
◉ tags (delicious + thesenet)
◉ svic keywords
examples of results

61. tag1 tag2 tag3
tag1 freq (tag1) cooc (tag1, tag2) cooc (tag1, tag3)
tag2 cooc (tag2, tag1) freq (tag2) cooc (tag2, tag3)
tag3 cooc (tag3, tag1) cooc (tag3, tag2) freq (tag3)
contextual distance: co-occurrence vector
cosine distance to detect related tags
 
21
21
21,cos
tagtag
tagtag
tagtag



[Cattuto et al. 2008]

62. example of resultsCADIC, ADEME

63. environment
interest comunity inclusion
detecting narrower tags [Mika et al.]
agriculture
     12 ,,21
tagrhasnarrowetaguseruser tagtag 

64. Arrows mean "has broader"
thickness ≈ weight
examples of resultsdel.icio.us

65. combining metrics
edition distances
Monge-Elkan Soundex, JaroWinkler,
asymmetry Monge-Elkan Qgram
contextual metric
cosinus vector co-occurring tags
social metrics
inclusion of communities of interest
football
sport
+
+

66. 83 027 relations / 9 037 tags
 68 633 related
 11 254 hyponyms
 3 193 spelling variants

67. structuring as a side effect

68. handling conflicts
arbitration rules
IF num(narrower)/num(broader) ≥ c
THEN narrower/broader
ELSE related
purely automatic
conflicting
arbitrated conflict
debated
consensual

69. folksonomy enrichment lifecycle
ADDING TAGS
Automatic
processing
User-centric
structuring
Detect
conflicts
Global
structuring
Flat
folksonomy
Structured
folksonomy
[Limpens et al.]

71. Graphs, graphs, graphs
Fabien
owner
author
Researcher
type
doc.html
author
Semantic web is not antisocial
Adult
Researcher
sub property sub class
semantic web
title
Fabien
Marco Guillaume
Nicolas
Michel
Rémi
social network analysis
 ),(;)( pxrelxpdin 
4)( 
Guillaumedin
owner
Adult
type

72. parentsibling
motherfatherbrothersister
colleague
knows
Gérard
Fabien
Mylène
Michel
Yvonne
<family> (guillaume)=5d(guillaume)=3guillaume
c.f. [Erétéo et al.]

73. eg. typed proximity centrality
select distinct ?y ?to
pathLength($path) as ?length
(1/sum(?length)) as ?centrality
where{
?y s (foaf:knows*/rel:worksWith)::$path ?to
}group by ?y
    
1

 





 
GEx
worksWithknows
c
worksWithknows xkglengthkC ,/*/*

74. e.g.

76. ipernity.com dataset in RDF
61 937 actors & 494 510 relationships
–18 771 family links between 8 047 actors
–136 311 friend links implicating 17 441 actors
–339 428 favorite links for 61 425 actors
etc.
c.f. [Erétéo et al.]

77. some interpretations
validated with managers of ipernity.com
friendOf, favorite, message, comment
small diameter, high density
family as expected: large diameter, low density
favorite: highly centralized around Ipernity animator.
friendOf, family, message, comment: power law of

78. some interpretations
existence of a largest component in all sub networks
"the effectiveness of the social network at doing its job" [Newman 2003]
0
10000
20000
30000
40000
50000
60000
70000
number actors size largest component
knows
favorite
friend
family
message
comment

79. e.g. of results: different
key actors for different
kinds of links
c.f. [Erétéo et al.]

80. PERFORMANCES & LIMITS
Knows 0.71 s 494 510
Favorite 0.64 s 339 428
Friend 0.31 s 136 311
Family 0.03 s 18 771
Message 1.98 s 795 949
Comment 9.67 s 2 874 170
Knows 20.59 s 989 020
Favorite 18.73 s 678 856
Friend 1.31 s 272 622
Family 0.42 s 37 542
Message 16.03 s 1 591 898
Comment 28.98 s 5 748 340
Shortest paths used
to calculate
Knows Path length <= 2: 14m 50.69s
Path length <= 2: 2h 56m 34.13s
Path length <= 2: 7h 19m 15.18s
100 000
1 000 000
2 000 000
Favorite Path length <= 2: 5h 33m 18.43s 2 000 000
Friend Path length <= 2: 1m 12.18 s
Path length <= 2: 2m 7.98 s
1 000 000
2 000 000
Family Path length <= 2 : 27.23 s
Path length <= 2 : 2m 9.73 s
Path length <= 3 : 1m 10.71 s
Path length <= 4 : 1m 9.06 s
1 000 000
3 681 626
1 000 000
1 000 000
)(GComp rel
)(, yD rel  1
time projections

81. SEMSNA SCHEMA
annotating the networks with analysis results
high centrality

82. SemSNA an ontology of SNA
http://ns.inria.fr/semsna/

83. 4
Philippe
colleague
2
colleague
supervisor
Degree
Guillaume
Gérard
Fabien
Mylène
Michel
Yvonne
IvanPeter
example of SemSNA

84. ADD {
?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
)(do
lengthtype, yin 
[PhD Guillaume Erétéo]

85. conceptual and software framework
for a semantic analysis of social networks using
semantic web frameworks

86. groups&reasons

87. detecting AND labeling communities
?
?

88. propagating tags
to discover communities of interest

89. tags to detect and label communities
extension of algorithm RAK/LP :
from random labels to structured tags
salt, water
pepper, wine
mustard
rugby, foot
foot, movie
hockey sport sport
sport
condiment
condiment
condiment
[Eretéo et al., 2011]

90. applied to Ademe Ph.D. network
 1 853 agents
1 597 academic supervisors
256 ADEME engineers.
 13 982 relationships
10 246 rel:worksWith
3 736 rel:colleagueOf
 6 583 tags
 3 570 skos:narrower
relations between 2 785 tags

91. -1,2
-1,1
-1
-0,9
-0,8
-0,7
-0,6
-0,5
-0,4
-0,3
-0,2
-0,1
0
0,1
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
R AK
TagP
S emTagP
controlled S emTagP (env)
controlled S emTagP (env, energ)
controlled S emTagP (env, energ, model)
MODULARITY COMPARISONS
X axis: propagation iterations, Y axis: modularity

92. results
1. pollution
2. sustainable
development
3. energy
4. chemistry
5. air pollution
6. metals
7. biomass
8. wastes
92

93. controled abstraction and merge
lead

94. PLATFORM AND PROTOTYPES
overview of the architecture

95. a web 3.0 solution
open integration and standard in the front-end

97. towardsrichwebmarks

101. navigating in the expert network

102. activity flow and notification

103. Semantic Web plugin for Gephi

104. web-scraping: archiving and integrating

105. Fresnel lenses to adapt results

106. create dynamic reports in the wiki

108. integrating with new web platforms

109. going mobile

110. to know more
deliverables and publications
http://isicil.inria.fr
open source code on INRIA forge
https://gforge.inria.fr/projects/isicil/
models
http://ns.inria.fr/

111. Xxxxx
xxx
Xxxxx
xxxx
xxxx
xxxxx
xxxx
Xxxxx
x
xxxxx
gave birth to …
« Unveil the semantic imprint from within your Community »
• cooperative society of social semantic web specialists
• industrialization and maintenance of research results
• developing webwarks as linked semantic traces
Xxxxxx
xxxxxx
xxxxxxXxxxxx
xxxxxx
xxxxxx
Xxxxxx
xxxxxx
xxxxxx
contribute
downloaded
interested in
exchange
Xxxxxx
xxxxxx
xxxxxxcontributes
exhange
interested in
contributes
Xxxxxx
xxxxxx
xxxxxx
interested
in
Xxxxxx
xxxxxx
xxxxxx
link & enrich
analyse & assist

112. doggy-bag
of the talk

113. web 1, 2

114. price convert?
person homepage?
more info?
web 1, 2, 3

115. wikipedia editions by users…
0
500000
1000000
1500000
2000000
2500000
3000000
3500000
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50
0
500000
1000000
1500000
2000000
2500000
3000000
3500000
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50
without bots…

116. semantic spreading activation
some other contributions
mappings
linguistic patterns
[Person] is starring in [Movie]
[Person] born in [Place]
(…)
exploratory search
natural language Q&A
publication & links
projection & inferences
ASK {
?res creator ?prov .
?prov hasColleague ?user .
?prov interestedBy ?topic .
?user interestedBy ?topic }

117. informal
formal
usage representation
one web…
data
person document
program
metadata

118. tomorrow, he who controls the metadata,
controls the web.
@fabien_gandon
http://fabien.info