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FaceTag - IASummit 2007


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The (quick and dirty) slides from the Las Vegas 2007 IA Summit.

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FaceTag - IASummit 2007

  1. 1. FaceTag Integrating Bottom-up and Top-down Classification in a Social Tagging System IA Summit 2007 22-26 March, Las Vegas
  2. 2. The Long Tail Our culture and economy is increasingly shifting away from a focus on a relatively small number of &quot;hits&quot; at the head of the demand curve and toward a huge number of niches in the tail <ul><li>Costs of production fall </li></ul><ul><li>Costs of storage and distribution fall </li></ul><ul><li>We need better filters and aggregators </li></ul>
  3. 3. The UGC Era <ul><li>Among adults, 35% of internet users have created content and posted it online </li></ul><ul><li>User generated content can be anything produced by the user: text, audio, video, photos, categories or ranks, clips </li></ul><ul><li>(PEW, December 2005) </li></ul>
  4. 4. Information Overload <ul><li>601 exabytes in 2010 </li></ul><ul><li>70 percent of the world's digital data will be created by individuals </li></ul><ul><li>(IDC 2007) </li></ul><ul><li>185 exabytes (billion gigabytes) of storage needed last year </li></ul><ul><ul><li>12 stacks of books that each reach from the Earth to the Sun </li></ul></ul><ul><ul><li>3 million times the information in all the books ever written </li></ul></ul><ul><ul><li>more than 2 billion of the most capacious iPods </li></ul></ul>
  5. 5. How can we navigate and find this information?
  6. 6. An Emerging Approach to Distributed Classification <ul><li>Collaborative tagging systems to organize, browse and share personal collections of resources through the introduction of simple metadata </li></ul><ul><li>Folksonomy = user-generated classification, emerging through bottom-up consensus </li></ul><ul><li>The basic idea is simply to make people share items annotated with keywords </li></ul>
  7. 7. Homepage
  8. 8. Collaborative Tagging Examples <ul><li>An incomplete list of tagging systems </li></ul><ul><li>They are web-based collaborative systems for: </li></ul><ul><ul><li>building a shared database of items </li></ul></ul><ul><ul><li>a flat metadata vocabulary </li></ul></ul><ul><ul><li>metadata driven queries </li></ul></ul><ul><ul><li>to monitor change in areas of interest </li></ul></ul><ul><ul><li>discover emergencies or trends </li></ul></ul>
  9. 9. Have tagging systems solved the issue?
  10. 10. Homepage
  11. 11. Tagcloud
  12. 12. Food Tag in
  13. 13. Down the Long Tail TAG CLOUDS HITS LONG TAIL ?
  14. 14. More Tags for the Same Thing
  15. 15. How many apples? VS VS VS
  16. 16. Folksonomies ’ Properties <ul><li>Pros </li></ul><ul><ul><li>Trade-off between simplicity and precision </li></ul></ul><ul><ul><li>Matching users’ real needs and language </li></ul></ul><ul><ul><li>They are inclusive (nothing is left out) </li></ul></ul><ul><ul><li>Discovery of information and Serendipity </li></ul></ul><ul><ul><li>A forced move (the environment makes the difference) </li></ul></ul><ul><ul><li>Better than nothing (when traditional classification is not viable) </li></ul></ul><ul><li>Cons </li></ul><ul><ul><li>Language related issues </li></ul></ul><ul><ul><li>User Experience issues </li></ul></ul>
  17. 17. Language Issues <ul><li>As a result of intrinsic language variability, tagging systems are also implicitly plagued by: </li></ul><ul><ul><li>Polysemy (window: the hole or the pane of glass) </li></ul></ul><ul><ul><li>Homonymy (apple, jaguar) </li></ul></ul><ul><ul><li>Plurals (blog/blogs, folksonomy/folksonomies) </li></ul></ul><ul><ul><li>Mistyped tags (folsonomy) </li></ul></ul><ul><ul><li>Synonymy (tags, tagging, folksonomy) </li></ul></ul><ul><ul><li>Ego-oriented tags (toread, funny, interesting etc..) </li></ul></ul><ul><ul><li>Basic level variations (dog/beagle) </li></ul></ul><ul><li>These problems can dramatically reduce the effectiveness of the application and the benefits brought on by tagging systems. </li></ul>
  18. 18. User Experience Issues <ul><ul><li>Low findability quotient and Low scalability </li></ul></ul><ul><ul><li>High semantic density (very few well-known topics dominating the scene) </li></ul></ul><ul><ul><li>An alphabetical criterion limits the ability to explore the tag cloud </li></ul></ul><ul><ul><li>A flat tag cloud cannot visually support semantic relationships </li></ul></ul><ul><li>Tag clouds are visual interfaces for information retrieval that provide a global contextual view of tags assigned to resources in the system </li></ul><ul><li>Flat tag clouds not sufficient to provide a semantic and multidimensional browsing experience </li></ul>
  19. 19. Navigating Large Domains <ul><li>Information seekers in large domains of objects express the desire of having to deal with meaningful groupings of related items, in order to quickly understand relationships and decide how to proceed [Marti Hearst 2006]. </li></ul><ul><li>How to generate and navigate such groups from a flat set of objects is anyway a different matter. </li></ul><ul><li>Taxonomies, Clustering and Faceted Classification have been proposed in the past as useful techniques </li></ul>
  20. 20. Our Toolbox!
  21. 21. Taxonomies <ul><li>Taxonomies = coherent and complete system of meaningful labels which systematically organize a domain </li></ul><ul><li>Organically crafted before starting to catalogue trying to guess user needs and content types </li></ul><ul><li>Authoritative centralized view </li></ul><ul><li>High precision avoiding ambiguity, hierarchical structure to give context </li></ul><ul><li>Main drawbacks: </li></ul><ul><ul><li>An a priori and monolithic hierarchical organization do not have the ability to match the vocabulary and the varied ways of thinking of different users. </li></ul></ul><ul><ul><li>Expensive to build and maintain by professional indexers </li></ul></ul>
  22. 22. Clusters <ul><li>Clustering = the act of grouping items according to some measure of similarity </li></ul><ul><li>It reduces the semantic density and improve the visual consistency of tag clouds </li></ul><ul><li>But it generate messy groups , conflate many different dimensions and does not allow refinement and follow-up queries </li></ul><ul><li>Users prefer clear hierarchies with categories at uniform levels of granularity over the messy, unpredictable and unlabeled groupings typical of clustering techniques </li></ul>
  23. 23. Facets <ul><li>Facets = orthogonal descriptors (categories) within a metadata system </li></ul><ul><li>Each facet has a name and addresses a different conceptual dimension or feature type relevant to the collection </li></ul><ul><li>Each object is classified combining labels from different facets. </li></ul><ul><li>Facets can be : </li></ul><ul><ul><li>Flat or hierarchical </li></ul></ul><ul><ul><li>Assigned single or multiple values </li></ul></ul>It is hierarchial Facet name 393 resources here
  24. 24. Facets <ul><ul><li>add structure and context to tags </li></ul></ul><ul><ul><li>navigate along several dimensions simultaneously </li></ul></ul><ul><ul><li>seamlessly integrate browsing and searching </li></ul></ul><ul><ul><li>refine and broaden filtering criteria </li></ul></ul><ul><li>Benefits </li></ul><ul><li>Hierarchical faceted metadata can be used to </li></ul><ul><ul><li>Better support for exploration , discovery and iterative query refinement. </li></ul></ul><ul><ul><li>Easier to understand the meaning of tags </li></ul></ul><ul><ul><li>Large tag clouds more browsable </li></ul></ul><ul><ul><li>Reduction of the mental work (favoring recognition over recall) </li></ul></ul><ul><li>Usability studies show how this approach is preferred over single hierarchies and clusters </li></ul>
  25. 25. What do we need next?
  26. 26. What do we need yet? <ul><li>Middle ground (between the pure democracy of bottom-up tagging and the empirical determinism of top-down controlled vocabularies*) </li></ul><ul><li>Metadata ecology : merge and leverage emerging and traditional tools to improve findability </li></ul><ul><li>Metadata ecology as a fusion not only a coexistence </li></ul>*Alex Wright: blog / archives /000900.html
  27. 27. FaceTag Contributions <ul><li>FaceTag tries to limit the impact of polysemy, homonymy and basic level variation introducing a multidimensional and more semantic paradigm </li></ul><ul><li>Goals : improving usability, findability, browsability, serendipity and scalability of the system. </li></ul><ul><li>FaceTag mixes three contributions to social tagging systems: </li></ul><ul><ul><li>Tag hierarchies </li></ul></ul><ul><ul><li>Facets of Tags </li></ul></ul><ul><ul><li>Tagging and searching mixed </li></ul></ul>
  28. 28. Faceted Analysis
  29. 29. How to choice facets? 2 main roads <ul><li>Freehand made </li></ul><ul><ul><li>Each facet is created at the moment </li></ul></ul><ul><ul><li>The subject is freely deconstructed in several aspects </li></ul></ul><ul><li>Standard based </li></ul><ul><ul><li>Each facet is chosen using the guidelines fixed by Ranganathan or CRG </li></ul></ul><ul><ul><li>The subject is deconstructed following a “general scheme” </li></ul></ul><ul><ul><li>“ General scheme” works as a prototype of every particular faceted scheme </li></ul></ul>
  30. 30. Facets derived from the CRG general scheme Date Time [Country] Space People Agent Purposes, [Markets] (e.g. Industry, Health ...) Patient -- By-product Deliverables Product [Activities] Operation -- Process Themes Material Language Property -- Part Resource Types (e.g. online report, case study, blog...) Type [Documents, Resources] Thing FACETAG CRG
  31. 31. FaceTag facets date of publication (through a calendar) Date public administration health, education > conferences > www2006 Purposes Weinberger e. Reiss Morville People competitive analysis classification > facets web 2.0 > folksonomies information design > navigation design > breadcrumbs Themes predefined values (select box ISO Standard ISO 639-2) Language white paper case study blog > enterprise web Resource Types SAMPLES FACETAG FACET
  32. 32. The Real Application
  36. 36. Final Step UPDATED TAGS RESULT SET
  38. 38. Preliminary User Research
  39. 39. <ul><li>Preliminary facets were derived from the CRG schema </li></ul><ul><li>These facets will be revised through an iterative bottom-up (card sorting) process </li></ul><ul><li>The goal: to elicit the best facets from a wide set of IA bookmarks already online </li></ul>Facets Evaluation
  40. 40. <ul><li>An intuitive, easy to learn and easy to use interface is probably the single most effective way to support and stimulate participation </li></ul><ul><li>The new user interface has been designed through documented heuristics and patterns </li></ul><ul><li>Verified at each iterative step by small usability tests . </li></ul><ul><li>Critical task: the assignment of new bookmarks and the association of tags to relevant facets. </li></ul>User Interface Evaluation
  41. 41. Conclusions
  42. 42. Folksonomies are dead. Long Life to folksonomies <ul><li>Low Benefit & Mainstream Ready? </li></ul><ul><li>28% of online Americans tag content (PEW December 2006) </li></ul><ul><li>Tagging is commoditized, but it has a long way to go </li></ul><ul><li>A few ideas with FaceTag </li></ul>Hierarchies Facets Tag Suggestion Hierarchical Clustering Tagging Decay Advanced Navigation Synonyms Syntax Control User Ratings
  43. 43. <ul><li>Thanks for your time! </li></ul><ul><li> </li></ul><ul><li>FaceTag Crew </li></ul><ul><li>Emanuele Quintarelli ( [email_address] ) </li></ul><ul><li>Andrea Resmini ( </li></ul><ul><li>Luca Rosati ( [email_address] ) </li></ul><ul><li>Luca Mascaro ( </li></ul><ul><li>Diego La Monica ( </li></ul>Questions ?