PhD defense : Multi-points of view semantic enrichment of folksonomies

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


8

Ambiguity of tags

… or in Texas, USA ?

… in France ?

paris

Lack of seman-c links between tags


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

19

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

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: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


24

Tagging model

Typing the named graphs for addi-onal dimensions of tagging

nt:TaggedResource rdfs:Resource nt:isRelatedTo


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



wind-‐energy

renewable energy

windenergy

wind turbine

has broader

close match

has narrower

environment

related


28



wind-‐energy

renewable energy

windenergy

wind turbine

has broader

close match

has narrower

environment

related


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


Extending SIOC to model different types of agents

31


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

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



Detect conflicts

Global structuring

Flat folksonomy



37


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


* http://staffwww.dcs.shef.ac.uk/people/S.Chapman/simmetrics.html


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

;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?.@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'&/$


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


46

€

σ(energy,sustainability) = cos( v energy, v sustainability )

47


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



Detect conflicts

Global structuring

Flat folksonomy



51

compu7ng server

!"#$%&'"()&$!"#$*"&&'+)&$!"#$#,)--.*/$0"&."*1$!"#$&)-"1)($

,'+)&)($%2$/),!.3'&/$

52

?

Computed rela0ons are not always accurate

ADDING TAGS



Detect conflicts

Global structuring

Flat folksonomy



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


Exemple: Rejec7ng a rela7on

57


John


energy

skos:related


Exemple: Proposing another

rela7on

energie

energy

skos:closeMatch


srtag:hasProposed

58


John


energy

skos:related



rela7on

energie

energy

skos:closeMatch


srtag:hasProposed

59


John


energy

skos:related



rela7on

energie

energy

skos:closeMatch


srtag:hasProposed

ADDING TAGS



Detect conflicts

Global structuring

Flat folksonomy



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


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


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

Example conflict resolu7on Conflic7ng Conflict solver choice debatable rejected 67

ADDING TAGS



Detect conflicts

Global structuring

Flat folksonomy



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



Detect conflicts

Global structuring

Flat folksonomy



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)

78

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

79

Future work

80

Thank you !

[email protected]

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

81

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

PhD defense : Multi-points of view semantic enrichment of folksonomies

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