This thesis, set at the crossroads of Social Web and Semantic Web, is an attempt to bridge Social tagging-based systems with structured representations such as thesauri or ontologies (in the informatics sense). Folksonomies resulting from the use of social tagging systems suffer from a lack of precision that hinders their potentials to retrieve or exchange information. This thesis proposes supporting the use of folksonomies with formal languages and ontologies from the Semantic Web. Automatic processing of tags allows bootstraping the process by using a combination of a custom method analyzing tags' labels and adapted methods analyzing the structure of folksonomies. The contributions of users are described thanks to our model SRTag, which allows supporting diverging points of view, and captured thanks to our user friendly interface allowing the users to structure tags while searching the folksonomy. Conflicts between individual points of view are detected, solved, and then exploited to help a referent user maintain a global and coherent structuring of the folksonomy, which is in return used to garanty the coherence while enriching individual contributions with the others' contributions. The result of our method allows enhancing the navigation within tag-based knowledge systems, but can also serve as a basis for building thesauri fed by a truly bottom up process.

1. Multi-points of
view semantic
enrichment of
folksonomies"
1P h . D T h e s i s d e f e n s e – O c t o b e r 2 5 t h 2 0 1 0
Freddy Limpens
Edelweiss, INRIA Sophia Antipolis
Edelweiss
Picasso
129ieth
birthday
Supervisors
Fabien Gandon, Edelweiss, INRIA Sophia Antipolis
Michel Buffa, Kewi/I3S, UNSA/CNRS

2. 1. Context
and
mo-va-ons
2

3. • Online
communi7es
of
interest
• "Enterprise
2.0"
&
organiza7ons
⇒ Cross-­‐fer7lizing
Web
2.0
and
Seman7c
Web
Context
of
the
thesis
3

4. • Tools
for
techno/science
monitoring
• Experts
seeking
• Industrial
partners:
• Academic
partners:
Context
of
the
thesis
4

5. 5
From
social
tagging
to
folksonomies
Tags
freely
associated
to
resources
…
…
collected
and
shared
on
the
web

6. 6
…
resul7ng
in
FOLKSONOMIES
A
mass
of
users
for
a
mass
of
resources

7. Limita-ons
of
folksonomies
7
Spelling
varia-ons
of
tags:
newyork
=
new_york
=
nyc

8. Limita-ons
of
folksonomies
8
Ambiguity
of
tags
…
or
in
Texas,
USA
?
…
in
France
?
paris

9. Lack
of
seman-c
links
between
tags
Limita-ons
of
folksonomies
9

10. 10
How
to
turn
folksonomies
...
?
...
into
topic
structures
(thesaurus)
?
pollution
Soil pollutions
has narrower
pollutant Energy
related related

11. 11
…
without
overloading
users
… and by collecting
all user's expertise
into the process

12. Outline
of
the
presenta-on
12
1. Context
and
mo7va7ons
2. State
of
the
art
and
posi7oning
3. Tagging
&
folksonomy
enrichment
models
4. Folksonomy
enrichment
life-­‐cycle

13. 2.
State
of
the
art
and
posi-oning
13

14. 14
State
of
the
art
Automa-c
extrac-on
of
tag
seman-cs:
• Similarity
based
on
co-­‐occurrence
paZerns
(Specia
&
MoZa
2007;
CatuZo
2008)
• Associa7on
rule
mining
(Mika
2005;
Hotho
et
al.
2006)
pollution
Soil pollutions
has narrower
pollutant Energy
related related

15. 15
State
of
the
art
Involving
users
in
tags
structuring:
• Simple
syntax
to
structure
tags
(Huyn-­‐Kim
Bang
et
al.
2008)
• Crowdsourcing
strategy
to
validate
tag-­‐
concepts
mapping
(Lin
et
al.
2010)
• Integrate
ontology
maturing
into
Social
Bookmarking
tool
(Braun
et
al.
2007)
pollution
Soil pollutions
has narrower
pollutant Energy
related related
a relation, depending on the actual context. This fact
is acknowledged by many ontology formalisms that al-
low metamodeling. Using imagenotions, users do not
need to understand this somewhat artificial separation
of notions.
2. Because imagenotions are associated with images, they
are meaningful internationally as an image has the
same meaning in different languages.
The goal of our methodology is to guide the process of
creating an ontology of imagenotions. The main steps of
this methodology is based on the ontology maturing process
model:
1. Emergence of Ideas. In this step, new imagenotions are
created. Already this step can become collaborative,
as users can jointly collect the tags describing imageno-
tions, and select the most representative images for an
imagenotion. Collaborative editing is especially use-
ful in a multi-lingual environment where it cannot be
expected that any individual user speaks all required
languages.
2. Consolidation in Communities. Because it is so easy to
create new imagenotions, it cannot be avoided that for
the same semantic notion initially many imagenotions
are created (synonyms, also in different languages) or
that an imagenotion represents more than one seman-
tic notion (homonyms). In this step, these problems
should be solved by merging synonymous imageno-
tions, and by splitting imagenotions representing more
than one notion.
We now demonstrate some functionality of the tool in
terms of the steps of our development methodology.
4.3.1 Step 1: Emergence of Ideas
Figure 2 shows an example for the emergence of ideas.
Let us assume that a content owner has new images about
elephants. The imagenotion “elephant” was so far not avail-
able. Therefore, she creates a new imagenotion, adds an
image or part of an image that shows elephants and starts
describing the new imagenotion with more details. She uses
English as spoken language. As synonyms, she enters “ele-
phantidae” and “tusker”. Instead of tagging the new images
that show elephants with these words, she can use the new
imagenotion—she just pulls this imagenotion over the new
images via drag and drop.
Figure 2: Editing an imagenotion with the No-
tionEditor tool

16. 16
State
of
the
art
Tags
and
Seman-c
Web
models
• SCOT
for
tags
and
tagging
(Kim
et
al.
2007):

17. 17
State
of
the
art
Tags
and
Seman-c
Web
models
• SCOT
for
tags
and
tagging
(Kim
et
al.
2007):
• MOAT
(Passant
&
Laublet,
2008)
:
Raising
ambiguity
by
linking
tags
to
concepts
from
Linked
Data

18. 18
Posi-oning
Computed
Tag
similarity
Tag-­‐Concept
mapping
Users'
contrib.
Sem-­‐Web
formalism
Mul7-­‐points
of
view
Angeletou
et
al.
(2008)
✓
✓
✓
Huynh-­‐Kim
Bang
et
al.
(2008)
✓
✓
Passant
&
Laublet
(2008)
✓
✓
✓
Lin
&
Davis
(2010)
✓
✓
✓
✓
Braun
et
al.
(2007)
✓
✓
Our
approach
✓
✓
✓
✓

19. 3. Tagging
&
folksonomy
enrichment
models
19

20. 20
Tagging
model
Tagging
=
linking
a
resource
with
a
sign
What
is
a
tagging
?
"nature"!
picture
shows
"nature"
(1)
(2)
(3)
place
located
l:england
edi7ng
makes
me
:
)

21. 21
Tagging
model
NiceTag
(Monnin
et
al,
2010):
Tagging
as
named
graphs*
nt:TaggedResource
rdfs:Resource
nt:isRelatedTo
nt:TagAc7on(named
graph)
sioc:UserAccount
sioc:has_creator
sioc:Container
sioc:has_container
xsd:Date
dc:date
*Carrol
et
al.
(2005)

22. 22
Tagging
model
No
constraints
on
the
model
of
the
sign
used
to
tag
nt:TaggedResource
rdfs:Resource
nt:isRelatedTo
nt:TagAc7on(named
graph)
nt:TaggedResource
hZp:geonames.org/2990440
nt:isRelatedTo
scot:Tag
:)
skos:Concept
nt:isRelatedTo
nt:isRelatedTo
nt:isRelatedTo
nt:isRelatedTo
moat:Tag
moat:hasMeaning

23. 23
Tagging
model
Typing
the
rela,on
to
reflect
on
pragma-cs
of
use
of
tags
nt:TaggedResource
rdfs:Resource
nt:isRelatedTo
nt:TagAc7on(named
graph)

24. 24
Tagging
model
Typing
the
named
graphs
for
addi-onal
dimensions
of
tagging
nt:TaggedResource
rdfs:Resource
nt:isRelatedTo
nt:TagAc7on(named
graph)

25. 25
Tagging
model
Example
of
a
tagging
in
delicious
hZp://www.windenergy.com
nt:ManualTagAc7on
nt:isAbout
scot:Tag
#wind-­‐energy
<nt:TaggedResource
rdf:about="http://www.windenergy.com"
cos:graph="http://mysocialsi.te/tagaction#7182904">
<nt:isAbout
rdf:resource="http://mysocialsi.te/tag#wind-­‐energy"
/>
</nt:TaggedResource>
freddy
sioc:has_creator
using
RDF
source
declara-on
delicious.com
sioc:has_container
<nt:ManualTagAction
rdf:about="http://mysocialsi.te/tagaction#7182904">
<sioc:has_creator
rdf:resource="http://mysocialsi.te/user#freddy"
</nt:ManualTagAction>

26. 26
Folksonomy
enrichment
2
complementary
seman7c
enrichment:
hZp://www.windenergy.com
nt:ManualTagAc7on
nt:isAbout
wind-­‐energy
renewable
energy
windenergy
wind
turbine
has
broader
close
match
has
narrower
environment
related
Structuring tags as in a thesaurus (SKOS)

27. 27
Folksonomy
enrichment
2
complementary
seman7c
enrichment:
wind-­‐energy
renewable
energy
windenergy
wind
turbine
has
broader
close
match
has
narrower
environment
related
Structuring tags as in a thesaurus (SKOS)

28. 28
Folksonomy
enrichment
2
complementary
seman7c
enrichment:
wind-­‐energy
renewable
energy
windenergy
wind
turbine
has
broader
close
match
has
narrower
environment
related
Structuring tags as in a thesaurus (SKOS)

29. 29
Tagging
model
Suppor,ng
diverging
points
of
view
car
pollu7on
skos:related
john
agrees
paul
disagrees

30. Suppor-ng
diverging
points
of
view
Reifica-on
of
rela7ons
with
named
graphs
30

31. Suppor-ng
diverging
points
of
view
Extending
SIOC
to
model
different
types
of
agents
31

32. Suppor-ng
diverging
points
of
view
Reifica-on
of
rela7ons
with
named
graphs
car
pollu7on
skos:related
srtag:SingleUser
"john"
srtag:hasApproved
srtag:SingleUser
"paul"
srtag:hasRejected
srtag:TagSeman7cStatement
srtag:TagStructureComputer
"r2d2"
srtag:hasProposed
32

33. 33
Ademe
scenario
Experts
produce
docs
+
tag
Archivists
centralize
+
tag
Public
audience
read
+
tag
Life-­‐cycle
grounded
on
usage
analysis

34. 34
Ademe’s
dataset
Delicious TheseNet Cadic
What
Bookmarks of
users of tag
"ademe"
Keywords for
Ademe's PhD
projects
Archivists
indexing lexicon
# tags 1015 6583 1439
# resources 196 1425 4675
# tagging
(1R - 1T - 1U)
3015 10160 25515
# users 812 1425 1

35. 4. Going
through
the
folksonomy
enrichment
life-­‐cycle
35

36. ADDING TAGS
Automatic
processing
User-centric
structuring
Detect
conflicts
Global
structuring
Flat
folksonomy
Structured
folksonomy
Folksonomy
enrichment
life-­‐cycle
36

37. ADDING TAGS
Automatic
processing
User-centric
structuring
Detect
conflicts
Global
structuring
Flat
folksonomy
Structured
folksonomy
Folksonomy
enrichment
life-­‐cycle
37

38. Automatic processing
1. String-based
2. Co-occurrence patterns
3. User-based associations
Flat
folksonomy
38
3 methods to automatically extract tags semantics

39. 39
1.
String-­‐based
metrics
pollution Soil pollutions
pollutantpollution
=> « pollution » related to « pollutant »
=> « pollution » broader than « soil pollutions »

40. • Benchmark
of
30
different
string-­‐based
similarity
from
SimMetrics*
:
σ
(t1,t2)
∊
[0,
1]
• Reference
data
set
built
with
Ademe
experts
• Which
metric
is
best
for
which
rela0on
at
what
threshold
?
• Informa7on-­‐retrieval
metrics
precision,
recall,
and
F1-­‐measure
40
1.
String-­‐based
metrics
* http://staffwww.dcs.shef.ac.uk/people/S.Chapman/simmetrics.html

41. 1.
String-­‐based
metrics
41
• MongeElkan_Soundex
to
detect
seman,cally
linked
tags
(close
match
+
hyponym
+
related)
threshold
=
0.8
• JaroWinkler
to
dis7nguish
closeMatch
threshold
=
0.9
• asymmetry
of
MongeElkan_QGram
to
dis7nguish
hyponyms
• σ
(t1,t2)
≠
σ
(t2,t1)
• δ
=
σ
(t1,t2)
-­‐
σ
(t2,t1)
>
0.4
!"#!$
!"%!$
!"&!$
!"'!$
!"(!$
!")!$
!"*!$
!"+!$
,"!!$
$-./01
23456/$$
$7
0386954.3:;<
2=>1
.=6/<
.3$$
$;<
2=>1
.=6/<
.3$$
$7
0386954.3:;<
2=>1
.=6/<
.3?0=0>$$
$7
0386954.3:-./0$$
$;<
2=>1
.=6/<
.3?0=0>$$
$7
0386954.3:@66A56<
.31
B3C>$$
$7
0386954.3:-./01
23456/$$
$;0B3A6D$$
$7
0386954.3:;0B3A6D$$
!"#$%&'(#)#*&+,'-.(/#
0&%'(-1'223#2,(4&*#$5(*#6&(17%'84/#
;6/26E&$ ;6/26E,$
;6/26E#$ ;6/26E%$
!"#!$
!"%!$
!"&!$
!"'!$
!"(!$
!")!$
!"*!$
!"+!$
,"!!$
$-
./012345/678
9:;<
5:1=8
5/$$
$78
9:;<
5:1=8
5/$$
$-
./012345/6>5=.$$
$?11@318
5/<
A/B;$$
$-
./012345/6>5=.<
9/431=$$
$C1D1/E;:19/$$
$-
./012345/67.A/@1F$$
$7.A/@1F$$
$>5=.$$
$>5=.<
9/431=$$
!"#$%&'(#)#*&+,'-.(/#
01&22,(3#+'4,'(56#$7(*#8&(9::%'4;/#
7G=91E&$ 7G=91E,$
7G=91E#$ 7G=91E%$
!"#"$%
"#""%
"#"$%
"#&"%
"#&$%
"#'"%
"#'$%
"#("%
"#($%
"#)"%
%*+,-./012,34..50.62,78,9:35;<%%
%*+,-./012,3=+8,5.>35;<%%
%*+,-./012,3=6;?:72?.@62,A+?+:35;<%%
%*+,-./012,3B0+91C;D?2,9.35;<%%
%*+,-./012,3EF.@02GH+.I9;.,?35;<%%
%*+,-./012,3H+D;,.=;6;02@;?J35;<%%
%*+,-./012,3C;9.=;6;02@;?J35;<%%
%*+,-./012,3/890;5.2,C;D?2,9.35;<%%
%*+,-./012,3K2992@5=;6;02@;?J35;<%%
%*+,-./012,3*2?9:;,-H+.I9;.,?35;<%%
%*+,-./012,3K2@+35;<%%
%*+,-./012,3K2@+7;,10.@35;<%
%*+,-./012,3L.F.,D:?.;,35;<%%
%*+,-./012,3=6;?:72?.@62,35;<%%
%*+,-./012,3MA@26DC;D?2,9.35;<%%
!"#$%&%'"()#*+$%
,)-"."$/"%0"12""$%'"31#4567+$689%:%'"31#4;+$:<67+$689=%'"31#>?41@A1B9:?41BA1@9%
=N@;.D&%
=N@;.D'%
=N@;.D(%
=N@;.D)%

42. Cas
1.
String-­‐based
metrics
1.
String-­‐based
metrics
Heuris-c
in
3
steps
seman-cally
linked
:
MongeElkan-­‐Soundex
σ1
IF
σ1(t1,t2)
>
0.8
closeMatch
:
JaroWinkler
σ2
IF
σ2
(t1,t2)
>
0.9
=>
t1
closeMatch
t2
hyponym
:
MongeElkan-­‐QGram
σ3
ELSE
IF
σ3
(t1,t2)
-­‐
σ3
(t2,t1)
>
0.4
=>
t1
has
narrower
t2
related
otherwise
ELSE
=>
t1
related
t2
42

43. Cas
1.
String-­‐based
metrics
1.
String-­‐based
metrics
Performances
!"
!#$"
!#%"
!#&"
!#'"
!#("
!#)"
!#*"
+,-../01"234/305" 67,8079" 4-.35-:"
!"#$%&%'()*)"#$+,,)
;4-</+/80"6-=4/+><" ?-<3.."6-=4/+><"
43

44. 1. String-based
metrics results
!"#$%&'"()&$
!"#$*"&&'+)&$
!"#$#,)--.*/$0"&."*1$
!"#$&)-"1)($
1.
String-­‐based
metrics
44results on full dataset
tags
from
experts
tags
from
archivsts
close
match
related
broader

45. 45
2.
Co-­‐occurrence
pacerns
Example
of
folksonomy
cc

46. ecology energy wind turbine sustainability housing
ecology 0 1 1 3 1
energy 1 0 2 4 3
wind turbine 1 2 0 1 1
sustainability 3 4 1 0 4
housing 1 3 1 4 0
IF σ > 0.85 => "energy" related "sustainability"
€

vecology
€

venergy

vwind turbine

vsustainability
€

vhousing
2.
Co-­‐occurrence
pacerns
46
σ(energy,sustainability) = cos(

venergy,

vsustainability )

47. 47
2.
Co-­‐occurrence
pacerns
Cadic dataset

48. renewable
energy
wind-­‐energy
Alex
Delphine
Claire
Monique
Anne
⇒
Hyponym
rela7ons
(broader/narrower):
«
renewable
energy
»
broader
than
«
wind-­‐energy
»
3.
User-­‐based
associa-on
48

49. 3.
User-­‐based
associa-on
THESENET
dataset
49

50. Global
results
of
automa-c
processings
Total
with
3
automa7c
methods:
83027
rela-ons
for
9037
tags
– 68633
related
– 11254
hyponym
– 3193
spelling
variants
50

51. ADDING TAGS
Automatic
processing
User-centric
structuring
Detect
conflicts
Global
structuring
Flat
folksonomy
Structured
folksonomy
Folksonomy
enrichment
life-­‐cycle
51
compu7ng
server

52. !"#$%&'"()&$
!"#$*"&&'+)&$
!"#$#,)--.*/$0"&."*1$
!"#$&)-"1)($
52
?
Computed
rela0ons
are
not
always
accurate

53. ADDING TAGS
Automatic
processing
User-centric
structuring
Detect
conflicts
Global
structuring
Flat
folksonomy
Structured
folksonomy
Folksonomy
enrichment
life-­‐cycle
53
Firefox
extension
SRTAgEditor

54. 54
Capturing
users's
contribu-ons
Embedding
structuring
tasks
within
everyday
ac0vity
(searching
e.g)

55. 55
Capturing
users's
contribu-ons

56. 56
Capturing
user's
point
of
view
John
srtag:hasRejected
energie
france
skos:broader
srtag:TagSeman7cStatement
Exemple:
Rejec7ng
a
rela7on

57. 57
Capturing
user's
point
of
view
John
srtag:hasRejected
energie
energy
skos:related
srtag:TagSeman7cStatement
Exemple:
Proposing
another
rela7on
energie
energy
skos:closeMatch
srtag:TagSeman7cStatement
srtag:hasProposed

58. 58
Capturing
user's
point
of
view
John
srtag:hasRejected
energie
energy
skos:related
srtag:TagSeman7cStatement
Exemple:
Proposing
another
rela7on
energie
energy
skos:closeMatch
srtag:TagSeman7cStatement
srtag:hasProposed

59. 59
Capturing
user's
point
of
view
John
srtag:hasRejected
energie
energy
skos:related
srtag:TagSeman7cStatement
Exemple:
Proposing
another
rela7on
energie
energy
skos:closeMatch
srtag:TagSeman7cStatement
srtag:hasProposed

60. ADDING TAGS
Automatic
processing
User-centric
structuring
Detect
conflicts
Global
structuring
Flat
folksonomy
Structured
folksonomy
Folksonomy
enrichment
life-­‐cycle
60

61. 61
Conflict
detec-on
environment
pollu7on
Using rules:
IF num(narrower)/num(broader) ≥ c
THEN narrower wins
ELSE related wins
narrower
John
srtag:hasApproved
Anne
srtag:hasApproved
broader
Monique
srtag:hasApproved
Delphine
srtag:hasApproved

62. 62
Conflict
detec-on
related
broader narrower
less constrained less constrained less constrained
close match
relatedenvironment
pollu7on
narrower
broader

63. 63
Experimenta-on
at
ADEME
Par7cipa7on
of
3
members
at
Ademe
+
2
professionals
in
environment
Si je cherche des
informations, je dois
pouvoir utiliser
indifféremment le
Tag1 ou le Tag2
Si je cherche des
informations liées à
Tag1, les informations
liées à Tag2 sont
pertinentes, mais pas
le contraire
Si je cherche des
informations liées à
Tag2, les informations
liées à Tag1 sont
pertinentes, mais pas
le contraire
Si je cherche des
informations sur l'un
des tags, il est
pertinent de suggérer
des informations sur
l'autre tag
(Tag1 et Tag2 sont
équivalents)
(Tag1 est plus général
que Tag2)
(Tag2 est plus général
que Tag1)
(Tag1 et Tag2 liés)
agriculture durable agriculture raisonnee
biologie agriculture biologique
changements sociaux changement social
chimie verte chanvre
Climat/changement changement climatique
collectivite action collective
collectivite collecte de donnees
commande communication entre acteurs
comportements pro-
environnementaux
comportements pro-
environnemental
compost composant
conception ecoconception
conception
travail collaboratif vis a vis de la
conception
cycle de rankine cycle organique de rankine
developpement durable developpement local
accumulateurs li-ion tours d'habitation
acteurs du territoire territorialite
agglomeration cooperation
agriculture durable agriculture biologique
diversite culturelle diversite microbienne
ecologie ecology
elements finis methode des elements finis
energie politique energetique
energie production energie
energie energie renouvelable
energie autonomie energetique
energy energies
Nom Prénom :
Poste :
Profil en quelques mots-clés :
Indiquer par un "X" la relation que vous jugez la plus exacte entre les deux tags.
Choisissez une seule relation pour chaque tag. Les deux premières lignes sont des exemples
fictifs.
Tag1 Tag2
Ces 2 tags ne
sont pas
spécialement
liés

64. Several
cases
of
conflic-ng
situa-ons
Conflic-ng
:
>1
rela7on
per
pair
of
tags
Approved
:
1
rela7on,
only
approved
Debatable
:
1
rela7on,
BOTH
approved
and
rejected
Rejected
:
1
rela7on,
only
rejected
!"#$%&'#()
*+,)
-../"012)
34,)
516787691)
:;,)
<1=1&812)
:+,)
!"#$%&'("&$)*+,&-$'.$/'012/-$+'&3204$
64

65. Several
cases
of
conflic-ng
situa-ons
Distribu-on
over
rela-on
types
:
•
"closeMatch"
tends
to
draw
a
consensus
more
easily
than
others
•
"broader/
narrower"
and
"related"
cause
more
debates/conflicts
!"#
$!"#
%!"#
&!"#
'!"#
(!"#
)!"#
*!"#
+!"#
,!"#
$!!"#
-./01234-5# 67/3817# 9377/:17# 71.3418#
!"#$%&'()&"*+,-$,.$/,-0&/($/1'2'$,32)$)241+,-$(562'$
;/9<=-4#0/.>17#?7/?/03.# @??7/>18# A163418# B1C1-418#
65

66. Several
cases
of
conflic-ng
situa-ons
Influence
of
compound
words
?
!"#
$!"#
%!"#
&!"#
'!"#
(!"#
)!"#
*!"#
+!"#
,!"#
$!!"#
-./0.12345.637#
08967#
:.24;./0.123#
5.637#08967#
<==#08967#
-.2>9;?2@# <006.AB3# CBD8E8D=B# FBGB;EB3#
energy
renewable
energy
80%
46%
66

67. Example
conflict
resolu7on
Conflic7ng
Conflict
solver
choice
debatable
rejected
67

68. ADDING TAGS
Automatic
processing
User-centric
structuring
Detect
conflicts
Global
structuring
Flat
folksonomy
Structured
folksonomy
Folksonomy
enrichment
life-­‐cycle
68

69. Helping
Referent
User
(Ademe
archivists)
choose
solu0ons
to
conflicts
Repor-ng
69

70. 70
Global
map
Includes
all
points
of
view,
highlights
conflicts
+
consensuses

71. Referent
choices
71
Choices
of
the
referent
user
(archivists
at
Ademe
e.g.)

72. Referent
choices
72

73. ADDING TAGS
Automatic
processing
User-centric
structuring
Detect
conflicts
Global
structuring
Flat
folksonomy
Structured
folksonomy
Folksonomy
enrichment
life-­‐cycle
73

74. Enriching
individual
points
of
view
Integra7ng
others'
contribu7ons:
1. Current
user
-­‐>
"Anne"
2. ReferentUser
(e.g.
archivists)
3. ConflictSolver
(sowware
agent)
4. Other
individual
users
5. Automatons
(metrics)
BROADER
NARROWER
RELATED
CLOSE
MATCH
environnement
Search:
preoccupa7on
environnementales
grenelle
de
l
environnement
competences
environnementales
environment
environmental
domaines
environnementaux
Anne
is
looking
for
tag
"environnement"
74

75. Each
point
of
view
corresponds
to
a
layer
75

76. 5. Conclusion
76

77. 77
What
we
do
:
Help
online
communi7es
structure
their
tags
wind-­‐energy
renewable
energy
sustainability
wind
turbine
has
broader
related
has
narrower
environment
related

78.  An
approach
to
bridge
tagging
with
Seman-c
Web:
 NiceTag
for
tagging
 SRTag
for
mul7-­‐points
of
view
structuring
of
tags
 Complete
life-­‐cycle
of
folksonomy
enrichment
 Automa-c
processing
of
tags:
 String-­‐based
heuris-c
 State
of
the
art
methods
integrated
in
Seman7c
Web
compu7ng
environment
(Corese
Sparql
engine)
 User
interface
to
capture
tag
structuring
embedded
in
every-­‐day
tasks
 Implementa-on
within
ISICIL
solu7on
(tagging
server)
78
Our
contribu-ons:

79. • More
user
interfaces
:
• Collabora-ve
aspects
• Visualisa-on
of
large
structured
folksonomy
• Tag
searching
• Other
computa7onal
methods
+
op7miza7on
• ISICIL
:
test
with
final
users
Ademe
and
Orange
labs
• Tes7ng
on
other
types
of
communi7es
(Life2Times)
• Temporal
dimension
• Mul7linguism
• Integra7ng
collabora-ve
ergonomics
in
design
processes
79
Future
work

80. 80
Thank
you
!
freddy.limpens@inria.fr
hZp://www-­‐sop.inria.fr/members/Freddy.Limpens/

81. 2010
• Monnin,
A.;
Limpens,
F.;
Gandon,
F.
&
Laniado,
D.
Speech
acts
meets
tagging:
NiceTag
ontology
AIS
SigPrag
Interna7onal
Pragma7c
Web
Conference,
2010
• Monnin,
A.;
Limpens,
F.;
Gandon,
F.
&
Laniado,
D.
,L'ontologie
NiceTag
:
les
tags
en
tant
que
graphes
nommés,A.
Monnin,
F.
Limpens,
D.
Laniado,
F.
Gandon,
EGC
2010,
Atelier
Web
Social
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F.;
Gandon,
F.
&
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M.
Helping
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communi-es
to
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folksonomies
Proceedings
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Extending
the
Fron7ers
of
Society
On-­‐Line,
hZp://webscience.org,
2010
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F.;
Monnin,
A.;
Laniado,
D.
&
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F.
NiceTag
Ontology:
tags
as
named
graphs
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Workshop
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Social
Networks
Interoperability,
ASWC09,
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&
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M.
Séman-que
des
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collabora-ve
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F.;
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F.
&
Buffa,
M.
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2009
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M.;
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F.;
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M.
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Social
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Crea-on
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More
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PLE
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B.
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Component
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Learning
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2008
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References