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Todays topic

Todays topic. Social Tagging. By Christoffer Hirsimaa. Stop thinking, start tagging: Tag Semantics arise from Collaborative Verbosity. Christian Körner , Dominik Benz, Andreas Hotho, Markus Strohmaier, Gerd Stumme From WWW2010. Where do Semantics come from?.

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Todays topic

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  1. Todays topic Social Tagging By Christoffer Hirsimaa

  2. Stop thinking, start tagging:TagSemantics arise from Collaborative Verbosity Christian Körner, Dominik Benz, Andreas Hotho, Markus Strohmaier, Gerd Stumme From WWW2010

  3. Where do Semantics come from? • Semantically annotated content is the „fuel“ of the next generation World Wide Web – but where is the petrol station? • Expert-built  expensive • Evidence for emergent semantics in Web2.0 data  Built by the crowd!  Which factors influence emergence of semantics?  Do certain users contribute more than others?

  4. Overview Pragmatics of tagging Emergent Tag Semantics Semantic Implications of Tagging Pragmatics Conclusions

  5. Emergent Tag Semantics • tagging is a simple and intuitive way to organize all kinds of resources • formal model: folksonomyF = (U, T, R, Y) • UsersU, Tags T, ResourcesR • Tag assignmentsY  (UTR) • evidence of emergent semantics • Tag similarity measures canidentify e.g. synonym tags (web2.0, web_two)

  6. Tag Similarity Measures: Tag Context Similarity Tag Context Similarity is a scalable and precise tag similarity measure [Cattuto2008,Markines2009]: Describe each tag as a context vector Each dimension of the vector space correspond to another tag; entry denotes co-occurrence count Compute similar tags by cosine similarity 6 … JAVA design software blog web programming  Will be used as indicator of emergent semantics!

  7. WordNet Hierarchy Mapping Average JCN(t,tsim) over all tags t: „Quality of semantics“ = tag = synset Assessing the Quality of Tag Semantics Folksonomy Tags JCN(t,tsim) = 3.68 TagCont(t,tsim) = 0.74

  8. bev donuts duff alc nalc bart Duff-beer wine beer marge beer barty Tagging motivation • „Describers“… • tag „verbously“ with freely chosen words • vocabulary not necessarily consistent (synonyms, spelling variants, …) • goal: describe content, ease retrieval • Evidence of different ways HOW users tag (Tagging Pragmatics) • Broad distinction by tagging motivation [Strohmaier2009]: • „Categorizers“… • use a small controlled tag vocabulary • goal: „ontology-like“ categorization by tags, for later browsing • tags a replacement for folders

  9. high low Tagging Pragmatics: Measures • How to disinguish between two types of taggers? • Vocabulary size: • Tag / Resource ratio: • Average # tags per post:

  10. Tagging Pragmatics: Measures • Orphan ratio: • R(t): set of resources tagged by user u with tag t high low

  11. Tagging pragmatics: Limitations of measures • Real users: no „perfect“ Categorizers / Describers, but „mixed“ behaviour • Possibly influenced by user interfaces / recommenders • Measures are correlated • But: independent of semantics; measures capture usage patterns

  12. = user Subset of 30% categorizers Influence of Tagging Pragmatics on Emergent Semantics Complete folksonomy • Idea: Can we learn the same (or even better) semantics from the folksonomy induced by a subset of describers / categorizers? Extreme Categorizers Extreme Describers

  13. TagCont(t,tsim)= … JCN(t,tsim)= … CF5 DF20 Experimental setup • Apply pragmatic measures vocab, trr, tpp, orphan to each user • Systematically create „sub-folksonomies“ CFi / DFi by subsequently adding i % of Categorizers / Describers (i = 1,2,…,25,30,…,100) • Compute similar tags based on each subset (TagContext Sim.) • Assess (semantic) quality of similar tags by avg. JCNdistance

  14. Dataset From Social Bookmarking Site Delicious in 2006 Two filtering steps (to make measures more meaningful): Restrict to top 10.000 tags FULL Keep only users with > 100 resources MIN100RES 14

  15. Results – adding Describers (DFi)

  16. Results – adding Categorizers (CFi)

  17. Summary & Conclusions • Introduction of measures of users‘ tagging motivation (Categorizers vs. Describers) • Evidence for causal link between tagging pragmatics (HOW people use tags) and tag semantics (WHAT tags mean) • „Mass matters“ for „wisdom of the crowd“, but composition of crowd makes a difference („Verbosity“ of describers in general better, but with a limitation) • Relevant for tag recommendation and ontology learning algorithms

  18. My thoughts and remarks • Confirmed deleting spammers is useful once again, but how useful? • Try to recursively combine the set of describers / categorizers

  19. Q&A and discussion!

  20. Thank you for your attention!

  21. 21 Extras:

  22. 22 References • [Cattuto2008] Ciro Cattuto, Dominik Benz, Andreas Hotho, Gerd Stumme: Semantic Grounding of Tag Relatedness in Social Bookmarking Systems. In: Proc. 7th Intl. Semantic Web Conference (2008), p. 615-631 • [Markines2009] Benjamin Markines, Ciro Cattuto, Filippo Menczer, Dominik Benz, Andreas Hotho, Gerd Stumme: Evaluating Similarity Measures for Emergent Semantics of Social Tagging. In: Proc. 18th Intl. World Wide Web Conference (2009), p.641-641 • [Strohmaier2009] Markus Strohmaier, Christian Körner, Roman Kern: Why do users tag? Detecting users‘ motivation for tagging in social tagging systems. Technical Report, Knowledge Management Institute – Graz University of Technology (2009)

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