helping online communities to semantically enrich folksonomies
DESCRIPTION
Helping online communities to semantically enrich folksonomies. Freddy Limpens , Fabien Gandon Edelweiss, INRIA Sophia Antipolis { freddy.limpens , fabien.gandon}@inria.fr Michel Buffa Kewi , I3S, Univerité Nice-Sophia Antipolis. Edelweiss. ISICIL, mai 2010. How to turn - PowerPoint PPT PresentationTRANSCRIPT
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Helping online communities to semantically enrich folksonomies
ISICIL, mai 2010
Freddy Limpens, Fabien Gandon Edelweiss, INRIA Sophia Antipolis
{freddy.limpens, fabien.gandon}@inria.fr
Michel BuffaKewi, I3S, Univerité Nice-Sophia Antipolis
Edelweiss
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How to turn folksonomies ...
...into comprehensible topic structures ?
?pollution
Soil pollutions
has narrower
pollutant Energy
related related
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… without overloading users
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… and by collectingall user's expertiseinto the process
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Our approach Integrate usage-analysis for a tailored solution
Supporting diverging points of view
Automatic processings +
Human expertise through user-friendly interfaces
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Concrete scenario
Expertsproduce docs
+ tag
Archivistscentralize + tag
Public audienceread + tag
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1. Modeling statements about tags
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Supporting diverging points of view
car pollutionskos:related
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car pollution
John Paul
Supporting diverging points of view
agrees disagrees
skos:related
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Supporting diverging points of view
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car pollutionskos:related
John Paul
hasApproved hasRejected
tagSemanticStatement (named graph)
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Supporting diverging points of view
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2. folksonomy enrichment Life cycle
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Dataset
Delicious TheseNet CADIC
WhatBookmarks of users of tag
"ademe"
Keywords for Ademe's PhD
projectsArchivists
indexing lexicon
# tags 1015 6583 1439
# posts 1013 1425 4675
# (restricted tagging) 3015 10160 25515
# users 812 1425 1
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ADDING TAGS
Automatic processing
User-centricstructuring
Detect conflicts
Globalstructuring
Flat folksonomy
Structured folksonomy
Folksonomy enrichment
life-cycle
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pollution
pollutantpollution
pollutionpollutionpollutionpollution Soil pollutions
1. String-based metrics
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Evaluation of 30 edit distances
Combining the best metrics
Needs complement !
1. String-based metrics
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1. String-based metrics
Evaluated against Ademe expert's point of view
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Related55,10%
Broader/narrower32%
CloseMatch12,82%
Result on full dataset
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Result on full dataset
Node size ↔ InDegree
◉ tags (delicious + thesenet)
◉ mot-clés svic
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Result on full dataset
Node size ↔ InDegree
◉ tags (delicious + thesenet)
◉ mot-clés svic
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Fig. Markines et al. (2009)
Association via :
Users
tags
2. Tri-partite structure of folksonomies
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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)
2. Tri-partite structure of folksonomiesTag-based association :
=> gives "related" relations
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Example result on CADIC dataset:
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2. Tri-partite structure of folksonomies
User-based association (Mika) :
environnementagriculture
U1
U2
U3
U4
U6
=> gives "subsumption" relations
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Example result on Delicious dataset:
Arrows mean "has broader"thickness ≈ weight
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TheseNet dataset:
Arrows mean "has broader"thickness ≈ weight
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ADDING TAGS
Automatic processing
User-centricstructuring
Detect conflicts
Globalstructuring
Flat folksonomy
Structured folksonomy
Folksonomy enrichment
life-cycle
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Embedding structuring tasks within everyday activity (searching e.g)
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Embedding structuring tasks within everyday activity (searching e.g)
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Capturing user's point of view
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Experimentation ADEME
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ADDING TAGS
Automatic processing
User-centricstructuring
Detect conflicts
Globalstructuring
Flat folksonomy
Structured folksonomy
Folksonomy enrichment
life-cycle
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Conflict detection
environment pollution
narrower
broader
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Conflict detection
environment pollution
narrower
broader
Using rules e.g:
IF num(narrower)/num(broader) ≥ cTHEN narrower winsELSE 'more generic' wins
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Conflict detection
environment pollution
narrower
broader
related
related
broader narrower
more generic more generic
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Ademe experimentation
Total number of relations 125
Contradictory 43
Consensual 14
Only rejected (to be deleted ?) 2
Only proposed by computer (no user review) 52
Debated (approved AND rejected at least once) 14
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Example result on Ademe's experimentation subset
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Example result on Ademe's experimentation subset
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ADDING TAGS
Automatic processing
User-centricstructuring
Detect conflicts
Globalstructuring
Flat folksonomy
Structured folksonomy
Folksonomy enrichment
life-cycle
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environment pollutionrelated
ReferentUser
Global structuring by Referent
hasApproved
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ADDING TAGS
Automatic processing
User-centricstructuring
Detect conflicts
Globalstructuring
Flat folksonomy
Structured folksonomy
Folksonomy enrichment
life-cycle
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environment
pollutants
pollution
environment
pollutants
pollution
narrowernarrower
Paul
environment
pollution
narrower
John
environment
pollution
related
Referent pollutantsnarrower
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Take away message (conclusion)
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Help communities
structure their tags
What we do :
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Our contributions:
Usages analysis
Automatic processing of tags
Tag structuring embedded in every-day tools
Supporting multi-points of view
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Future work
• Interfaces :• to capture user-centric contributions • Global administrations (Referent User)• Tag searching
• Real-scale experimentation• Mapping between knowledge representation
(Gemet thesaurus – Tags – CADIC e.g.)
• Moving to other socio-structural models
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Thank you [email protected]
http://www-sop.inria.fr/members/Freddy.Limpens/
http://isicil.inria.fr
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What is a tag ?
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Tagging model
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Tagging model
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Hypotheses
Tag-link is thematic
Tags are concept-candidate
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Supporting diverging points of view
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An eco-system of agents
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Tags are nice to organize your ownresources ...
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... but also to get involved in the organization of
shared resources