Automated Tag Clustering: Improving Search and Exploration in the TagSpace May 2006

1. Collaborative Tagging Workshop, WWW2006 Automated Tag Clustering:Improving Search and Exploration in the TagSpaceMay 2006 Grigory BegelmanTechnion IsraelInstitute ofTechnologyComputer Science Dptgbeg@cs.technion.ac.il Philipp KellerCitrin Informatik GmbHphred@citrin.ch Frank SmadjaRawSugarfrank@rawsugar.com

2. Collaborative Tagging Workshop, WWW2006 Problem 1:Searching the TagSpace How would You tag this? How would You search For it? Tags: Ikura, Uni, Ebi, Sushi, Nigiri, Japanese food, lunch in Tokyo, Ezobafun-uni, Kitamurashiuni, Murasakiuni, Akazaebi, Tenagaebi, etc.

3. Collaborative Tagging Workshop, WWW2006 Problem 2: Exploring the TagSpace Locations Restaurant Type morphology Not a restaurant!

4. Collaborative Tagging Workshop, WWW2006 Problem 3: Exploring the TagSpace Not usable !

5. Collaborative Tagging Workshop, WWW2006 What is Missing?Tag relations • “Tag Relations improve searchability and exploration.” • Similar tags: • Spelling and morphology: macos<->mac_os<->mac os; tagging <-> tags <->tagged, • Synonyms: macos <-> tiger; films <-> movies; new york <-> nyc; • Related: cooking <-> recipes, software development <-> programming, • Tag groups or subtags: • Location -> san francisco, london, new york, etc. • Food -> sushi, sashimi, pizza, etc. • Programming -> html, java, css, etc. Goal : Discover them by Mining the tag space

6. Collaborative Tagging Workshop, WWW2006 Related Work Tagger’s nightmare!! Top Down Predefined taxonomy Rigid - Not scalable - Expensive

7. Collaborative Tagging Workshop, WWW2006 Flickr – Clusters

8. Collaborative Tagging Workshop, WWW2006 RawSugar – Tag HierarchyGuided Navigation Food groups Origins groups Locations groups

9. Collaborative Tagging Workshop, WWW2006 RawSugar Tag Hierarchy • Key idea: Some users (4%) define tag hierarchies – (food>sushi, european>spanish, …) • We mine this tag space to learn simple tag-relations (ISA relations and RELATED) using probabilities. • At search time: We apply this learned knowledge to group tags from results.

10. Collaborative Tagging Workshop, WWW2006 RawSugar –Guided Search Combining Hierarchy Fragments User 3 User 1 food europe cooking recipes UK Scotland User4 Edinburgh Spain Asian Chinese Italy Thai User 2 User 5 food Southwest vegetarian California Sushi Bay Area San Francisco Texas

11. Collaborative Tagging Workshop, WWW2006 Related work Rashmi Sinha: “Tag Sorting: Another tool in an information architect's toolbox” http://www.rashmisinha.com/archives/05_02/tag-sorting.html Emanuele Quintarelli: “Hierarchical taxonomies from flat tag spaces” http://www.infospaces.it/wordpress/topics/information-architecture/91 Paul Heyman (Stanford): “Tag Hierarchies” http://i.stanford.edu/~heymann/taghierarchy.html Brooks, Montanez, University of San Francisco: “Improved Annotation of the Blogopshere via Autotagging and Hierarchical Clustering ” http://www.cs.usfca.edu/~brooks/papers/brooks-montanez-www06.pdf Siderean fac.etio.us: “Faceted search on delicious tags” http://www.siderean.com/delicious/facetious.jsp Marti Hearst: “Clustering vs. Faceted Search” http://bailando.sims.berkeley.edu/papers/cacm06.pdf And more …

12. 1. Get tag metadata Collaborative Tagging Workshop, WWW2006

13. 2. Build tag relation graph Collaborative Tagging Workshop, WWW2006

14. 3. Compute similarity Collaborative Tagging Workshop, WWW2006

15. 4. Cluster Collaborative Tagging Workshop, WWW2006

16. Results/Problems: Definition of „internet“ Collaborative Tagging Workshop, WWW2006

17. Results/Problems: Ambiguity Collaborative Tagging Workshop, WWW2006

18. Results/Problems: Clustering needs lot of tuning Collaborative Tagging Workshop, WWW2006

19. Possible application: Group popular bookmarks Collaborative Tagging Workshop, WWW2006

20. Collaborative Tagging Workshop, WWW2006

21. Collaborative Tagging Workshop, WWW2006 Some good Clusters found

22. Collaborative Tagging Workshop, WWW2006 Tags that belong to the same clusters -