Post on 18-Dec-2014
description
Multi-points of view semantic enrichment of folksonomies"
1 P 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
1. Context and mo-va-ons
2
• Online communi7es of interest
• "Enterprise 2.0" & organiza7ons
⇒ Cross-‐fer7lizing Web 2.0 and Seman7c Web
Context of the thesis
3
• Tools for techno/science monitoring
• Experts seeking
• Industrial partners:
• Academic partners:
Context of the thesis
4
5
From social tagging to folksonomies
Tags freely associated to resources …
… collected and shared on the web
6
… resul7ng in
FOLKSONOMIES
A mass of users for a mass of resources
Limita-ons of folksonomies
7
Spelling varia-ons of tags:
newyork = new_york = nyc
Limita-ons of folksonomies
8
Ambiguity of tags
… or in Texas, USA ?
… in France ?
paris
Lack of seman-c links between tags
Limita-ons of folksonomies
9
10
How to turn folksonomies ...
?... into
topic structures (thesaurus) ?
pollution
Soil pollutions
has narrower
pollutant Energy
related related
11
… without overloading users
… and by collecting all user's expertise into the process
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
2. State of the art and posi-oning
13
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
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
4.2 Ontology Development Methodology Based
on Imagenotions
Based on the ontology maturing process, we can developa solution for these problems. In terms of the previously in-troduced ontology maturing process we concentrate on thefirst three steps from the emergence of ideas up to formaliza-tion. What distinguishes our methodology from the usualontology development methodologies is the strong emphasison collaborative ontology development in the consolidation-in-communities phase. This is motivated by the success ofcollaborative tagging in Web 2.0 projects.
The basis of our ontology formalism is a concept we callimagenotions. An imagenotion graphically represents a se-mantic notion through an image, or a set of images. Inaddition, similarly to many existing ontology formalism, itis also possible to associate tags with an imagenotion indifferent languages (such as English or German). For eachlanguage, one of these tags is selected as the main label ofthe imagenotion. The other tags are termed synonyms.
Instead of tags, images are annotated with imagenotions.It is easy to see the advantage: all the shortcomings of tag-ging approaches are solved using imagenotions—it is easyto find images using search terms in any language. In otherwords, we provide semantic search instead of full-text search.
In the terminology of classical ontologies, imagenotionsare usually instances, but they may also correspond to con-cepts or relations. There are two major advantages of usingimagenotions over the classical ontology constructs:
1. The distinctions between concepts, instances and re-lations are hard to understand for most users. In ourmind, notions play the role of an instance, a concept ora relation, depending on the actual context. This factis acknowledged by many ontology formalisms that al-low metamodeling. Using imagenotions, users do notneed to understand this somewhat artificial separationof notions.
2. Because imagenotions are associated with images, theyare meaningful internationally as an image has thesame meaning in different languages.
The goal of our methodology is to guide the process ofcreating an ontology of imagenotions. The main steps ofthis methodology is based on the ontology maturing processmodel:
1. Emergence of Ideas. In this step, new imagenotions arecreated. Already this step can become collaborative,as users can jointly collect the tags describing imageno-tions, and select the most representative images for animagenotion. Collaborative editing is especially use-ful in a multi-lingual environment where it cannot beexpected that any individual user speaks all requiredlanguages.
2. Consolidation in Communities. Because it is so easy tocreate new imagenotions, it cannot be avoided that forthe same semantic notion initially many imagenotionsare created (synonyms, also in different languages) orthat an imagenotion represents more than one seman-tic notion (homonyms). In this step, these problemsshould be solved by merging synonymous imageno-tions, and by splitting imagenotions representing morethan one notion.
3. Formalization. In this step, taxonomical (“is-a”) andad-hoc relations are specified among imagenotions.
After step 2, the quality of image search already increasessignificantly, as the problems with synonyms and homonymsdo not appear anymore. Moreover, it is easy to see that allannotated images automatically benefit from the maturingimagenotions. E.g. adding a new tag to an imagenotionsautomatically allows users to find all images annotated withthat imagenotion using the new tag. In addition, the out-come of step 3 also allows requests for related images basedon the current context.
Imagenotions are useful to collaboratively build an on-tology supporting manual annotation and semantic search.However, to fulfill the requirement of IMAGINATION forautomatic annotation, a classical formal ontology is neededthat can be exploited by text-mining and object identifica-tion algorithms. This last axiomatization step is not yet di-rectly supported by our methodology, it is subject of futureresearch. Nevertheless, it is easy to see that a conversion ofan imagenotion ontology to a standard ontology formalism(e.g. OWL) is possible. The only missing information iswhether an imagenotion should be modelled as a concept,instance or a relation in the target ontology formalism.
4.3 Tool Support
Currently we implement a web-based tool that allows thecreation of new imagenotions and the editing of available im-agenotions. This tool supports all three steps of our method-ology. It can be easily invoked during semantic search orwhen uploading new images into an image repository: e.g.it is fully integrated into the user’s workflow.
We now demonstrate some functionality of the tool interms of the steps of our development methodology.
4.3.1 Step 1: Emergence of IdeasFigure 2 shows an example for the emergence of ideas.
Let us assume that a content owner has new images aboutelephants. The imagenotion “elephant” was so far not avail-able. Therefore, she creates a new imagenotion, adds animage or part of an image that shows elephants and startsdescribing the new imagenotion with more details. She usesEnglish as spoken language. As synonyms, she enters “ele-phantidae” and “tusker”. Instead of tagging the new imagesthat show elephants with these words, she can use the newimagenotion—she just pulls this imagenotion over the newimages via drag and drop.
Figure 2: Editing an imagenotion with the No-tionEditor tool
16
State of the art
Tags and Seman-c Web models
• SCOT for tags and tagging (Kim et al. 2007):
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
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 ✓ ✓ ✓ ✓
3. Tagging & folksonomy enrichment models
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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
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
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
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
Tagging model
Typing the named graphs for addi-onal dimensions of tagging
nt:TaggedResource rdfs:Resource nt:isRelatedTo
nt:TagAc7on(named graph)
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
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
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
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
Tagging model
Suppor,ng diverging points of view
car pollu7on skos:related
john
agrees
paul
disagrees
Suppor-ng diverging points of view
Reifica-on of rela7ons with named graphs
30
Suppor-ng diverging points of view
Extending SIOC to model different types of agents
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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
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Ademe scenario
Experts produce docs
+ tag Archivists
centralize + tag
Public audience read + tag
Life-‐cycle grounded on usage analysis
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
4. Going through the folksonomy enrichment life-‐cycle
35
ADDING TAGS
Automatic processing
User-centric structuring
Detect conflicts
Global structuring
Flat folksonomy
Structured folksonomy
Folksonomy enrichment life-‐cycle
36
ADDING TAGS
Automatic processing
User-centric structuring
Detect conflicts
Global structuring
Flat folksonomy
Structured folksonomy
Folksonomy enrichment life-‐cycle
37
Automatic processing
1. String-based
2. Co-occurrence patterns
3. User-based associations
Flat folksonomy
38
3 methods to automatically extract tags semantics
39
1. String-‐based metrics
pollution Soil pollutions
pollutant pollution
=> « pollution » related to « pollutant »
=> « pollution » broader than « soil pollutions »
• 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
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/$$
$70386954.3:;<2=>1
.=6/<.3$$
$;<2=>1.=6/<.3$$
$70386954.3:;<2=>1
.=6/<.3?0=0>
$$
$70386954.3:-./0$$
$;<2=>1.=6/<.3?0=0
>$$
$70386954.3:@66A56<.31
B3C>$$
$70386954.3:-./01
23456/$$
$;0B3A6D$$
$70386954.3:;0B3A6D$$
!"#$%
&'(#)#*&
+,'-
.(/#
0&%'(-1'223#2,(4&*#$5(*#6&(17%'84/#
;6/26E&$ ;6/26E,$
;6/26E#$ ;6/26E%$
!"#!$
!"%!$
!"&!$
!"'!$
!"(!$
!")!$
!"*!$
!"+!$
,"!!$
$-./012345/6789:;<5:1=85/$$
$789:;<5:1=85/$$
$-./012345/6>5=.$$
$?11@3185/<A/B;$$
$-./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%$
!"#"$%
"#""%
"#"$%
"#&"%
"#&$%
"#'"%
"#'$%
"#("%
"#($%
"#)"%
%*+,-./012,34..50.62,78,9:35;<%%
%*+,-./012,3=+8,5.>35;<%%
%*+,-./012,3=6;?:72?.@6
2,A+?+:35;<%%
%*+,-./012,3B0+91C;D?2,9.35;<%%
%*+,-./012,3EF.@02GH+.I9;.,?35;<%%
%*+,-./012,3H+D;,.=;6
;02@;?J35;<%%
%*+,-./012,3C;9.=;6
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%*+,-./012,3/890;5.2,C;D?2,9.35;<%%
%*+,-./012,3K2992@5=;6
;02@;?J35;<%%
%*+,-./012,3*2?9:;,-H+.I9;.,?35;<%%
%*+,-./012,3K2@+35;<%%
%*+,-./012,3K2@+7;,10.@35;<%
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%*+,-./012,3=6;?:72?.@6
2,35;<%%
%*+,-./012,3MA@26DC;D?2,9.35;<%%
!"#$%&%'"()#*+$%
,)-"."$/"%0"12""$%'"31#4567+$689%:%'"31#4;+$:<67+$689=%'"31#>?41@A1B9:?41BA1@9%
=N@;.D&%
=N@;.D'%
=N@;.D(%
=N@;.D)%
Cas
1. String-‐based metrics 1. String-‐based metrics
Heuris-c in 3 steps
seman-cally linked : MongeElkan-‐Soundex σ1 IF σ1(t1,t2) > 0.8
closeMatch : JaroWinkler σ2 IF σ2 (t1,t2) > 0.9 => t1 closeMatch t2 hyponym : MongeElkan-‐QGram σ3 ELSE IF σ3 (t1,t2) -‐ σ3 (t2,t1) > 0.4 => t1 has narrower t2 related otherwise ELSE => t1 related t2
42
Cas
1. String-‐based metrics 1. String-‐based metrics
Performances
!"
!#$"
!#%"
!#&"
!#'"
!#("
!#)"
!#*"
+,-../01"234/305" 67,8079" 4-.35-:"
!"#$%&%'()*)"#$+,,)
;4-</+/80"6-=4/+><" ?-<3.."6-=4/+><"
43
1. String-based metrics results
!"#$%&'"()&$!"#$*"&&'+)&$!"#$#,)--.*/$0"&."*1$!"#$&)-"1)($
,'+)&)($%2$/),!.3'&/$
1. String-‐based metrics
44 results on full dataset
tags from experts
tags from archivsts
close match related
broader
45
2. Co-‐occurrence pacerns
Example of folksonomy
c c
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"
€
v ecology
€
v energy
€
v wind turbine
€
v sustainability
€
v housing
2. Co-‐occurrence pacerns
46
€
σ(energy,sustainability) = cos( v energy, v sustainability )
47
2. Co-‐occurrence pacerns
Cadic dataset
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
3. User-‐based associa-on
THESENET dataset
49
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
ADDING TAGS
Automatic processing
User-centric structuring
Detect conflicts
Global structuring
Flat folksonomy
Structured folksonomy
Folksonomy enrichment life-‐cycle
51
compu7ng server
!"#$%&'"()&$!"#$*"&&'+)&$!"#$#,)--.*/$0"&."*1$!"#$&)-"1)($
,'+)&)($%2$/),!.3'&/$
52
?
Computed rela0ons are not always accurate
ADDING TAGS
Automatic processing
User-centric structuring
Detect conflicts
Global structuring
Flat folksonomy
Structured folksonomy
Folksonomy enrichment life-‐cycle
53
Firefox extension SRTAgEditor
54
Capturing users's contribu-ons
Embedding structuring tasks within everyday ac0vity (searching e.g)
55
Capturing users's contribu-ons
56
Capturing user's point of view
John
srtag:hasRejected energie
france
skos:broader
srtag:TagSeman7cStatement
Exemple: Rejec7ng a rela7on
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
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
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
ADDING TAGS
Automatic processing
User-centric structuring
Detect conflicts
Global structuring
Flat folksonomy
Structured folksonomy
Folksonomy enrichment life-‐cycle
60
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
Conflict detec-on
related
broader narrower
less constrained less constrained less constrained
close match
related environment pollu7on narrower
broader
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 raisonneebiologie agriculture biologique
changements sociaux changement socialchimie verte chanvre
Climat/changement changement climatiquecollectivite action collectivecollectivite collecte de donneescommande communication entre acteurs
comportements pro-environnementaux
comportements pro-environnemental
compost composantconception ecoconception
conception travail collaboratif vis a vis de la conception
cycle de rankine cycle organique de rankinedeveloppement durable developpement local
accumulateurs li-ion tours d'habitationacteurs du territoire territorialite
agglomeration cooperationagriculture durable agriculture biologiquediversite culturelle diversite microbienne
ecologie ecologyelements finis methode des elements finis
energie politique energetiqueenergie production energieenergie energie renouvelableenergie autonomie energetiqueenergy energies
environmental environment
environnement domaines environnementauxenvironnement grenelle de l'environnementenvironnement competences environnementales
environnement socialisation aux preoccupations environnementales
ester gasteropodesexperimentation electromediation
extraction phytoextractionfinance financementgestion gestion stock
gestion stock gestion des ressources naturellesgouvernance gouvernance forestiere
hybride vehicules electriques et hybrides
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
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
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
!"#
$!"#
%!"#
&!"#
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(!"#
)!"#
*!"#
+!"#
,!"#
$!!"#
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;/9<=-4#0/.>17#?7/?/03.# @??7/>18# A163418# B1C1-418#
65
Several cases of conflic-ng situa-ons
Influence of compound words
?
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%!"#
&!"#
'!"#
(!"#
)!"#
*!"#
+!"#
,!"#
$!!"#
-./0.12345.637#08967#
:.24;./0.123#5.637#08967#
<==#08967#
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energy
renewable energy
80%
46%
66
Example conflict resolu7on Conflic7ng Conflict solver choice debatable rejected 67
ADDING TAGS
Automatic processing
User-centric structuring
Detect conflicts
Global structuring
Flat folksonomy
Structured folksonomy
Folksonomy enrichment life-‐cycle
68
Helping Referent User (Ademe archivists) choose solu0ons to conflicts
Repor-ng
69
70
Global map
Includes all points of view, highlights conflicts + consensuses
Referent choices
71 Choices of the referent user (archivists at Ademe e.g.)
Referent choices
72
ADDING TAGS
Automatic processing
User-centric structuring
Detect conflicts
Global structuring
Flat folksonomy
Structured folksonomy
Folksonomy enrichment life-‐cycle
73
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
Each point of view corresponds to a layer
75
5. Conclusion
76
77
What we do :
Help online communi7es
structure their tags wind-‐energy
renewable energy
sustainability
wind turbine
has broader
related
has narrower
environment
related
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)
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Our contribu-ons:
• 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
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Future work
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Thank you !
freddy.limpens@inria.fr
hZp://www-‐sop.inria.fr/members/Freddy.Limpens/
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
• Limpens, F.; Gandon, F. & Buffa, M. Helping online communi-es to seman-cally enrich folksonomies Proceedings of the WebSci10: Extending the Fron7ers of Society On-‐Line, hZp://webscience.org, 2010
2009
• Limpens, F.; Monnin, A.; Laniado, D. & Gandon, F. NiceTag Ontology: tags as named graphs Interna7onal Workshop in Social Networks Interoperability, ASWC09, 2009
• Limpens, F.; Gandon, F. & Buffa, M. Séman-que des folksonomies : structura-on collabora-ve et assistée Ingénierie des Connaissances, 2009
• Limpens, F.; Gandon, F. & Buffa, M. Collabora-ve seman-c structuring of folksonomies (short ar-cle) IEEE/WIC/ACM Int. Conf. on Web Intelligence, 2009
• Erétéo, G.; Buffa, M.; Gandon, F.; Leitzelman, M. & Limpens, F. Leveraging Social data with Seman-cs W3C Workshop on the Future of Social Networking, Barcelona., 2009
• Henri, F.; Charlier, B. & Limpens, F. Understanding and Suppor-ng the Crea-on of More Effec-ve PLE Int. Conf. on Informa7on Resources Management, Dubai, 2009
2008 • Henri, F.; Charlier, B. & Limpens, F. Understanding PLE as an Essen-al Component of the Learning Process World Conf. on Educa7onal
Mul7media, Hypermedia & Telecommunica7ons, ED-‐Media, Vienna, Austria, 2008 • Limpens, F.; Gandon, F. & Buffa, M. Rapprocher les ontologies et les folksonomies pour la ges-on des connaissances partagées : un Etat
de l'art Proc. 19èmes journées francophones d'Ingénierie des Connaissances, Nancy, 2008
• Limpens, F.; Gandon, F. & Buffa, M. Bridging Ontologies and Folksonomies to Leverage Knowledge Sharing on the Social Web: a Brief Survey Proc. 1st Interna7onal Workshop on Social Sowware Engineering and Applica7ons (SoSEA),
http://www-‐sop.inria.fr/members/Freddy.Limpens/?q=biblio
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Personal publica-ons
ANGELETOU S., SABOU M. & MOTTA E. (2008). Seman7cally Enriching Folksonomies with FLOR. In CISWeb Workshop at European Seman7c Web Conference ESWC.
BRAUN S., SCHMIDT A., WALTER A., NAGYPÁL G. & ZACHARIAS V. (2007). Ontology maturing: a collabora7ve web 2.0 approach to ontology engineering. In CKC, volume 273 of CEUR Workshop Proceedings: CEURWS.org.
CATTUTO C., BENZ D., HOTHO A. & STUMME G. (2008). Seman7c grounding of tag relatedness in social bookmarking systems. In Proceedings of the 7th Interna7onal Conference on The Seman7c Web, Berlin, Heidelberg: Springer-‐Verlag.
GANDONF.,BOTTOLIERV.,CORBYO.&DURVILLEP. (2007).Rdf/xml source declara7on, w3c member submission. hZp://www.w3.org/Submission/rdfsource/.
HALPIN H. & PRESUTTI V. (2009). An ontology of resources: Solving the iden7ty crisis in ESWC, volume 5554 of Lecture Notes in Computer Science, p. 521–534: Springer.
HOTHO A., JÄSCHKE R., SCHMITZ C. & STUMME G. (2006). Informa7on retrieval in folksonomies: Search and ranking. In The Seman7c Web: Research and Applica-‐ 7ons, LNCS(4011) , Heidelberg: Springer.
HUYNH-‐KIM BANG B., DANÉ E. & GRANDBASTIEN M. (2008). Merging seman7c and par7cipa7ve approaches for organizing teachers’ documents. In Proceedings of World Conference on Educa7onal Mul7media, Hypermedia & Telecommunica7ons, p. x4959–4966, Vienna France.
KIM H.-‐L., YANG S.-‐K., SONG S.-‐J., BRESLIN J. G. & KIM H.-‐G. (2007). Tag Mediated Society with SCOT Ontology. In Seman7c Web Challenge, ISWC.
LIN H. & DAVIS J. (2010). Computa7onal and crowdsourcing methods for extrac7ng ontological structure from folksonomy. In ESWC (2), volume 6089 of Lecture Notes in Computer Science, p. 472–477: Springer.
MIKA P. (2005). Ontologies are Us: a Unified Model of Social Networks and Seman7cs. In ISWC, volume 3729 of LNCS, p. 522–536: Springer.
MONNIN A., LIMPENS F., GANDON F. & LANIADO D. (2010). Speech acts meet tagging: Nicetag ontology. In I-‐SEMANTICS ’10: Proceedings of the 6th Interna7onal Conference on Seman7c Systems, p. 1–10, New York, NY, USA: ACM.
PASSANT A. & LAUBLET P. (2008). Meaning of a tag: A collabora7ve approach to bridge the gap between tagging and linked data. In Proceedings of the WWW 2008 Workshop Linked Data on the Web (LDOW2008), Beijing, China.
SPECIA L. & MOTTA E. (2007). Integra7ng folksonomies with the seman7c web. In Proc. of the European Seman7c Web Conference (ESWC2007), volume 4519 of LNCS, p. 624–639, Berlin Heidelberg, Germany: Springer-‐Verlag. 82
References