1. 1 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

2. 2 Motivation Scenario Classic MusicBossa Nova Jazz Girl from Ipanema Chill out Chopin

3. 3 Motivation Scenario

4. 4 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

5. 5 Problem Definition Semantic Web suffers from knowledge bottleneck Folksonomies can help How? Voluntary annotators Educated towards shareable annotation How? Through a collabulary

6. 6 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)

7. 7 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

8. 8 Collabulary Learning

9. 9 Folksonomy to trivial ontology Res 8 Res 7 Res 5 User 4 User 2 User 1 User 3 stuff_to_chill makes_me_happy Res 3 Res 2 Res 1 awesome_artists User Resource Tag

10. 10 Matching Concepts

11. 11 Additional tag assignments Res 5 User 1stuff_to_chill Res 1 alternative

12. 12 Expert conceptualization Res 5 User 1stuff_to_chill Res 1 alternative Expert Res5Res6Res7Res8Res1Res4 Rockabilly Emo

13. 13 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

14. 14 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

15. 15 Frequent Itemsets for Learning Ontologies from Folksonomies

16. 16 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

17. 17 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

18. 18 Recommender Systems for Ontology Evaluation User 1 Res 1 User 1 := (res1:=1) T 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

19. 19 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.fm35327081982130899 Musicmoz-555982-

20. 20 Experiments and results Folksonomy Enrichment Edit distance to handle duplications electro hip hop chillout old skool dance anything else but death depeche mode alternative heavy metal experimental rock electronica house

21. 21 Frequent Itemsets for Learning Ontologies from Folksonomies

22. 22 Frequent Itemsets for Learning Ontologies from Folksonomies

23. 23 Recommender Systems for Ontology Evaluation Top-10 best recommendations / Allbut1 protocol Neighborhood size 20 Recall:=Number of hits / Number test users Recall

24. 24 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 ?

25. 25 Thanks for your attention!

26. 26 Frequent Itemsets for Learning Ontologies from Folksonomies