FolksonomyBased Collabulary Learning

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Folksonomy-Based Collabulary Learning

• Leandro Balby Marinho, Krisztian Buza, Lars
  Schmidt-Thieme
• marinho,buza,schmidt-thieme_at_ismll.uni-hildesheim
  .de
• Information Systems and Machine Learning Lab
  (ISMLL)
• University of Hildesheim, Germany

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Motivation Scenario
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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 Learning
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Folksonomy to trivial ontology
User
Resource
Tag
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Matching Concepts
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Additional tag assignments
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Expert conceptualization
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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 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
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Recommender Systems for Ontology Evaluation
User 1 (res11)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

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Experiments and results

• Folksonomy Enrichment
• Edit distance to handle duplications

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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 protocol
• Neighborhood size 20
• RecallNumber of hits / Number test users

Recall
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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