folksonomy-based collabulary learning
DESCRIPTION
Folksonomy-Based Collabulary Learning. Leandro Balby Marinho, Krisztian Buza, Lars Schmidt-Thieme {marinho,buza,schmidt-thieme}@ismll.uni-hildesheim.de Information Systems and Machine Learning Lab (ISMLL) University of Hildesheim, Germany. Chill out. Classic Music. Jazz. Chopin. - PowerPoint PPT PresentationTRANSCRIPT
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Folksonomy-Based Collabulary Learning
Leandro Balby Marinho, Krisztian Buza, Lars Schmidt-Thieme{marinho,buza,schmidt-thieme}@ismll.uni-hildesheim.de
Information Systems and Machine Learning Lab (ISMLL) University of Hildesheim, Germany
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Motivation Scenario
Classic Music
Bossa Nova
Jazz
Girl from Ipanema
Chill out
Chopin
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Motivation Scenario
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Outline
Problem Definition Collabulary Learning
Folksonomy Enrichment Frequent Itemset Mining for Ontology
Learning from Folksonomies Recommender Systems for Ontology
Evaluation Experiments and Results Conclusions and future work
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Problem Definition
Semantic Web suffers from knowledge bottleneck
Folksonomies can help How?
Voluntary annotators Educated towards shareable annotation How?
Through a collabulary
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Problem Definition
“A possible solution to the shortcomings of folksonomies and controlled vocabulary is a collabulary, which can be conceptualized as a compromise between the two: a team of classification experts collaborates with content consumers to create rich, but more systematic content tagging systems.”
Wikipedia article on Folksonomies(http://en.wikipedia.org/wiki/Folksonomy)
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Problem Definition
An ontology with concepts and a knowledge base with f is called a collabulary over and
Problem:Learn a collabulary that best represents
folksonomy and domain-expert vocabulary
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Collabulary LearningUser TagPeterAnnaAnna modernPeterAnnaPeter modernAnnaPeterAnna
ResourceFour Seasons renaissanceHair musicalHairCats musicalHair spiritualCatsFour Seasons classiccats good-to-hearFour Seasons vivaldi
User TagPeterAnnaAnna modernPeterAnnaPeter modernAnnaPeterAnna
Cats... ...
ResourceFour Seasons renaissanceHair musicalHairCats musicalHair spiritualCatsFour Seasons classiccats good-to-hearFour Seasons vivaldi
expert Hair musicalexpert musicalexpert
Folksonomy
Enriched folksonomy:user + expert knowledge
1. Folksonomy enrichment
music
modernclassic
musical ......Collabulary: ontology with user + expert knowledge
music
opera
musical
Domain-Expert Ontology
Folksonomy
classic
2. Ontology Learning
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Folksonomy to trivial ontology
root
stuff_to_chill awesome_artists makes_me_happy
r1, r5 r7, r8 r1, r2, r3
Res 8Res 7
Res 5User 4
User 2
User 1
User 3
stuff_to_chill
makes_me_happy
Res 3Res 2
Res 1
awesome_artists
UserResourceTag
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Matching Concepts
root
stuff_to_chill awesome_artists makes_me_happy
r1, r5 r7, r8 r1, r2, r3
Trivial ontology
(a)
rock
rockabilly
r1, r4
r5, r6 r7, r8
alternative
emo
root
Domain expert ontology
(b)
jazz
garage
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Additional tag assignments
Res 5
User 1 stuff_to_chill
Res 1
alternative
rock
rockabilly
r1, r4
r5, r6 r7, r8
alternative
emo
root
jazz
garage
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Expert conceptualization
Res 5
User 1 stuff_to_chill
Res 1
alternative
rock
rockabilly
r1, r4
r5, r6 r7, r8
alternative
emo
root
jazz
garage
Expert
Res5 Res6 Res7 Res8 Res1 Res4
Rockabilly
Emo
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Frequent Itemsets for Learning Ontologies from Folksonomies
Most of the approaches rely on co-occurrence models In sparse structures positive correlations carry essential
information about the data Project folksonomy to transactional database and use
state of the art frequent itemsets mining algorithms
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Frequent Itemsets for Learning Ontologies from Folksonomies Assumptions for relation extraction from
frequent intemsets High Level Tag
• The more popular a tag is, the more general it is• A tag x is a super-concept of a tag y if there are
frequent itemsets containing both tags such that sup({x})≥sup({y})
Frequency • The higher the support of an itemset, stronger
correlated are the items on it Large Itemset
• Preference is given for items contained in larger itemsets
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Frequent Itemsets for Learning Ontologies from Folksonomies
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Recommender Systems for Ontology Evaluation
Ontologies can facilitate browsing, search and information finding in folksonomies
They should be evaluated in this respect Recommender Systems are programs
for personalized information finding Let the recommender tell which is the
best ontology
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Recommender Systems for Ontology Evaluation
Task Recommend useful resources
Application Ontology-based collaborative filtering
Ontologies A trivial ontology (folksonomy), domain-
expert and collabulary Gold Standard
Test SetPorzel, R., Malaka, R.: A task-based approach for ontology
evaluation. In: Proc. of ECAI 2004, Workshop on Ontology Learning and Population, Valencia, Spain
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Recommender Systems for Ontology Evaluation
User 1
Res 1
User 1 := (res1:=1)T
rock
rockabilly
r1, r4
r5, r6 r7, r8
alternative
emo
root
jazz
garage
User := (emo:=53.3, alternative:=26.6, rock:=13.3, root:=6.6)T
Ziegler, C., Schmidt-Thieme, L., Lausen, G.: Exploiting semantic product descriptions for recommender systems. In: Proc. of the 2nd ACM SIGIR Semantic Web and Information Retrieval Workshop (SWIR 2004), Sheffield, UK
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Experiments and results
Datasets Last.fm (folksonomy) Musicmoz (domain-expert ontology)
Only the resources contained in both were considered
Datasets |U| |T| |R| |Y|Last.fm 3532 7081 982 130899Musicmoz - 555 982 -
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Experiments and results
Folksonomy EnrichmentEdit distance to handle duplications
electro
hip hop
chillout
old skool dance
anything else but death
depeche modealternative
heavy metal
experimental rock
electronica
house
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Frequent Itemsets for Learning Ontologies from Folksonomies
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Frequent Itemsets for Learning Ontologies from Folksonomies
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Recommender Systems for Ontology Evaluation
Top-10 best recommendations / Allbut1 protocolNeighborhood size 20
Recall:=Number of hits / Number test users
Rec
all
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Conclusions and Future work
Conclusions Folksonomies can alleviate knowledge bottleneck Users need to be educated towards more shareble
vocabulary though Collabularies can help
Our Contributions Definition of the collabulary learning problem An approach for enriching folksonomies with domain expert
knowledge A new algorithm for learning ontologies from folksonomies A new benchmark for task-based ontology evaluation
Future Work Non-taxonomic relations ? Different enrichment strategies ? Optimized structure for the task with constraints ?
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Thanks for your attention!
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Frequent Itemsets for Learning Ontologies from Folksonomies