Invited conference on ISICIL at SIIE 2012

1. ISICIL
semantic and social intraweb for
corporate intelligence and watch
ANR project CONTINT 2009-2011
Fabien Gandon, http://fabien.info
Leader Wimmics research team (INRIA, CNRS, Univ. Nice)
W3C AC Rep. for INRIA

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

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

4. semantic intrawebs & social intrawebs

5. ISICIL
reconcile latest viral applications of the web
with formal models and business processes
 new tools to support business intelligence
and technological watch
 interfaces of web 2.0 app. for interaction
(blog, wikis, social bookmarking, feeds, etc.)
 semantic web formalisms and processing
 social epistemology as theoretical framework

6.  INRIA - Wimmics team (leader)
 CNRS - I3S – KEWI
 Telecom ParisTech
 UTT - Tech-CICO
 ADEME
 Orange Labs R&D
consortium

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

8. extracts of the requirement analysis and specifications
MERGING METHODOLOGIES

9. usage analysis an specification
 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

10. ex. study of transformations: existing  target

11. shared online referential
ARIS portal for ISICIL: http://aris2.utt.fr:9090/businesspublisher/

12. 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 Extraire, filtrer
incontournable et ce que font les Consultation d’experts
autres ingénieurs
Prendre en compte demande Workflow, Outils de collaboration communiquer
Moteur de recherche, équation de rechercher
Préparer requêtes
recherche
Recueillir résultats Abonnement, push… Extraire, annoter
Vérifier pertinence des résultats Analyses, outils de filtrage filtrer
Messagerie électronique, chat, envoyer
Informer l'ingénieur
vidéo-conférence
S'approprier les résultats et les Annoter, organiser
requêtes
Equation de recherche, profil, tags
Devenir le destinataire des diffuser
alertes
Diffusion par profil

13. business intelligence market analysis

14. proposing functionalities & prototypes
Frequent functionalities and Prioritization of functionalities
dependencies

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

17. a tag
a data attached to an object
geometry

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

20. a crowd of users creating massive
categorizations

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

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

23. [Limpens et al ]
folksonomies → ontologies contributions…
… [Mika, 2005] hierarchies / community inclusion.
… [Heymann et al., 2006] hierarchies / centrality in graph Tag-Ressource
… [Schmitz, 2006] hierarchies / conditional probabilies & co-occurrence
… [Cattuto et al., 2008] [Markines et al., 2009] different metrics
… [Specia et al., 2007] [Begelman et al., 2006] clustering de tags
variations around metrics & space (tag-resource-user).

24. [Limpens et al ]
folksonomies + ontologies contributions…
... [Gruber, 2005] [Tanasescu et al., 2007] tagging tags
… [Specia et al., 2007][Cattuto et al., 2008][Giannakidou et al., 2008]
[Ronzano et al., 2008] [Tesconi et al., 2008] automated structuring
using external linguistic resources.
... [Good et al., 2007] manual disambiguation referencing a vocabulary
… [Passant et al., 2007] manual disambiguation referencing a thesaurus
… [Huynh-Kim Bang et al. , 2008] structured tagging “Paris<France”

25. [Limpens et al ]
ontologies → folksonomies contributions…
… [Gruber, 2005] [Newman et al., 2005] ontology of the tagging act
… [Breslin et al., 2005] SIOC resources shared on social web sites
… [Kim et al., 2007] SCOT representing tags and their cloud
… [Passant et al., 2008] MOAT, associating a meaning to a tag.

26. SoA… you are here
Computed Tag Tag-Concept Sem-Web Multi-points
Users' contrib.
similarity mapping formalism of view
Angeletou et al.
✓ ✓ ✓
(2008)
Huynh-Kim Bang et
✓ ✓
al. (2008)
Passant &
✓ ✓ ✓
Laublet(2008)
Lin & Davis (2010) ✓ ✓ ✓ ✓
Braun et al. (2007) ✓ ✓
Limpens et al.
(2010)
✓ ✓ ✓ ✓

27. edition distances
pollution
pollution Soil pollutions
pollution pollutant

28. 1
0,9
0,8
0,7
0,6
0,5
0,4
0,3
0,2
0,1
0
evaluating distances
c.f. [Limpens et al.]
Close match Aire sous f cm_A
Tag1 broader than Tag2 Aire sous f t1bt2_A
Related Aire sous f rel_A

29. 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.
determine thesaurus relations

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

31. contextual distance: co-occurrence vector
cosine distance to detect related tags
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)

cos tag1 , tag 2  
tag1  tag 2
tag1  tag 2
[Cattuto et al. 2008]

32. example of results
CADIC, ADEME

33. interest comunity inclusion
detecting narrower tags [Mika et al.]
user  user  tag , hasnarrower, tag 
tag1 tag2 2 1
agriculture environment

34. examples of results
del.icio.us
Arrows mean "has broader"
thickness ≈ weight

35. 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

36. 83 027 relations / 9 037 tags
 68 633 related
 11 254 hyponyms
 3 193 spelling variants

37. structuring as a side effect

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

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

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

42. Chine: 1 600 millions
Inde: 1 200 millions
acebook
800 millions

43. Graphs, graphs, graphs
Fabien Michel
Marco Guillaume Rémi d ( p )  x ; rel( x , p )

in

din (Guillaume)  4
Nicolas
social network analysis
Researcher
owner
type author owner Adult
Fabien doc.html
type sub property sub class
title
Adult author Researcher
Semantic web is not antisocial
semantic web

44. [Ereteo et al ]
semantic social network analysis
contributions…
… [Goldbeck et al 2003] propagating trust
… [Finin et al 2005] power law of degrees & community struct
… [Paolillo et al 2006] classical SNA on FOAF from LiveJournal
… [Goldbeck et Rothstein 2008] merging FOAF profiles
… [Anyanwu et al 2007] [Kochut et al 2007] [Corby et al 2004]
[Corby 2008] [Baget et al, 2007] paths in SPARQL
… [Ereteo et al 2009] type-parameterized SNA and SemTagP
… [Rowe et al. 2011] User Behaviour in Online Communities

45. Directed Weighted Labelled Parametrized
Network size
networks networks network operators
106 nodes
Graph Theory ✔ ✔ ✔ 107 edges
[Brandes 2009] ✔ ✔ ✔ 104 nodes
[Paolillo & Wright ~ 104 nodes
2006] ✔ ✔ ~ 105 edges
~ 104 nodes
[San Martin &
Gutierrez 2009] ✔ ✔ ~ 104 - 105
edges
104 nodes
SEMSNA ✔ … ✔ ✔ ~ 105 edges
[Erétéo et al.]

46. Fabien
Gérard Mylène
knows
colleague d(guillaume)=5
(guillaume)=3
<family>guillaume
Michel
sibling parent Yvonne
sister brother father mother
c.f. [Erétéo et al.]

47. eg. typed proximity centrality
1
 
C c
 knows / worksWith
* k     length g knows* / worksWith k , x 
 xEG 
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

48. CORESE/ KGRAM [Corby et al.]

49. e.g.

51. 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.]

52. 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

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

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

55. PERFORMANCES & LIMITS
time projections
Knows 0.71 s 494 510
Comprel (G) 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
D ( y)
rel ,1
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 Knows Path length <= 2: 14m 50.69s 100 000
to calculate Path length <= 2: 2h 56m 34.13s 1 000 000
Cbrel  (b) Path length <= 2: 7h 19m 15.18s 2 000 000
Favorite Path length <= 2: 5h 33m 18.43s 2 000 000
Friend Path length <= 2: 1m 12.18 s 1 000 000
Path length <= 2: 2m 7.98 s 2 000 000
Family Path length <= 2 : 27.23 s 1 000 000
Path length <= 2 : 2m 9.73 s 3 681 626
Path length <= 3 : 1m 10.71 s 1 000 000
Path length <= 4 : 1m 9.06 s 1 000 000

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

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

58. example of SemSNA
4
Gérard Mylène
2
Degree
colleague
Yvonne
Guillaume
supervisor
Michel Fabien
colleague Ivan
Philippe Peter

59. 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)

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

61. groups & reasons

62. hierarchical algorithms
output dendrograms of larger and larger
communities from top to bottom.
• agglomerative algorithms [Donetti &
Munoz 2004] [Zhou & Lipowsky 2004]
[Xu et al 2007] [Newman 2004]
• divisive algorithms [Girvan & Newman
2002] [Radicchi et al 2004]
[Eretéo et al., 2011]

63. heuristic based algorithms
• similarity with electrical networks [Wu 2004]
• random walk [Dongen 2000] [Pons et al 2005]
• label propagation [Raghavan et al 2007]
[Eretéo et al., 2011]

64. detecting AND labeling communities
?
?

65. propagating tags
to discover communities of interest

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

67. experimented algorithm
1. Algorithm SemTagP(RDFGraph network, Type relation)
2. DO
3. old_network = network
4. FOREACH user in network.users
5. user.tag = mostUsedNeighborTag(user, relationType)
6. END FOREACH
7. WHILE modularity(network) > modularity(old_network)
8. RETURN old_network
inject semantics here
[Eretéo et al., 2011]

68. semantic tag propagation
exploit folksonomy for label assignment
wiki mobile
b e
mobile inria
a d f
c g
sweetwiki mobile
[Eretéo et al., 2011]

69. semantic tag propagation
apply social pressure of RAK/LP
wiki mobile
b e
mobile inria
a d f
c g
sweetwiki mobile
[Eretéo et al., 2011]

70. semantic tag propagation wiki
take thesaurus into account in propagating
skos:narrower
sweetwiki mediawiki
wiki mobile
b e
mobile inria
a d f
c g
sweetwiki mobile
[Eretéo et al., 2011]

71. semantic tag propagation wiki
take thesaurus into account in propagating
skos:narrower
sweetwiki mediawiki
wiki mobile
b e
wiki inria
a d f
c g
sweetwiki mobile
[Eretéo et al., 2011]

72. semantic tag propagation
etc. leading to 2 communities
wiki mobile
b e
wiki mobile
a d f
c g
wiki mobile
[Eretéo et al., 2011]

73. 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

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

75. results
1. pollution
2. sustainable
development
3. energy
4. chemistry
5. air pollution
6. metals
7. biomass
8. wastes
75

76. controled abstraction and merge
lead

77. overview of the architecture
PLATFORM AND PROTOTYPES

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

80. towards rich webmarks

84. navigating in the expert network

85. activity flow and notification

86. export to Gephi for visualization & analyze

87. web-scraping: archiving and integrating

88. Fresnel lenses to adapt results

89. create dynamic reports in the wiki

90. going mobile

91. to know more
deployment & test campaign (4… 20… +) .
deliverables and publications
http://isicil.inria.fr
open source code on INRIA forge
https://gforge.inria.fr/projects/isicil/
models
http://ns.inria.fr/

92. doggy-bag
of the talk

93. social web 2.0
epistemology semantic web
theoretical framework
extensible models
process and interaction
services and interfaces

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

95. What is WWW2012?
21st International World Wide Web Conference
a “A rated” scientific conference
~12% acceptance & 1000-1500 participants
Lyon- France from 16t to 20th April 2012
RESEARCHERS
USERS INDUSTRIALS
www2012.org @www2012Lyon