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Folksonomy-Based Collabulary Learning. Leandro Balby Marinho, Krisztian Buza, Lars Schmidt-Thieme {marinho,buza,schmidt-thieme} Information Systems and Machine Learning Lab (ISMLL) University of Hildesheim, Germany. Chill out. Classic Music. Jazz. Chopin.

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folksonomy based collabulary learning

Folksonomy-Based Collabulary Learning

Leandro Balby Marinho, Krisztian Buza, Lars Schmidt-Thieme


Information Systems and Machine Learning Lab (ISMLL)

University of Hildesheim, Germany

motivation scenario

Chill out

Classic Music



Bossa Nova

Girl from Ipanema

Motivation Scenario
  • 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
problem definition
Problem Definition
  • Semantic Web suffers from knowledge bottleneck
  • Folksonomies can help
  • How?
    • Voluntary annotators
  • Educated towards shareable annotation
  • How?
    • Through a collabulary
problem definition1
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


problem definition2
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
folksonomy to trivial ontology


Res 1

Res 2

Res 3


User 3


User 2

User 4

Res 5

Res 7

Res 8

User 1

Folksonomy to trivial ontology




additional tag assignments

Res 5

User 1


Res 1


Additional tag assignments
expert conceptualization

Res 5

User 1


Res 1











Expert conceptualization
frequent itemsets for learning ontologies from folksonomies
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
frequent itemsets for learning ontologies from folksonomies1
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
recommender systems for ontology evaluation
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
recommender systems for ontology evaluation1
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 Set

Porzel, R., Malaka, R.: A task-based approach for ontology evaluation. In: Proc. of ECAI 2004, Workshop on Ontology Learning and Population, Valencia, Spain

recommender systems for ontology evaluation2

User 1

Res 1

User := (emo:=53.3, alternative:=26.6, rock:=13.3, root:=6.6)T

Recommender Systems for Ontology Evaluation

User 1 := (res1:=1)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

experiments and results
Experiments and results
  • Datasets
    • (folksonomy)
    • Musicmoz (domain-expert ontology)
  • Only the resources contained in both were considered
experiments and results1




depeche mode


experimental rock

anything else but death

hip hop

heavy metal

old skool dance


Experiments and results
  • Folksonomy Enrichment
    • Edit distance to handle duplications
recommender systems for ontology evaluation3
Recommender Systems for Ontology Evaluation

Top-10 best recommendations / Allbut1 protocol

Neighborhood size 20

Recall:=Number of hits / Number test users


conclusions and future work
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 ?