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Folksonomy-Based Collabulary Learning. Leandro Balby Marinho, Krisztian Buza, Lars Schmidt-Thieme {marinho,buza,[email protected] 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

{marinho,buza,[email protected]

Information Systems and Machine Learning Lab (ISMLL)

University of Hildesheim, Germany

Motivation scenario

Chill out

Classic Music



Bossa Nova

Girl from Ipanema

Motivation Scenario

Motivation scenario1
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 ?