Changing the perspective: From OWL profiles to User profiles


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This is presentation at the Semantic Web Perspectives Dagstuhl seminar in 2009

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  • Next to semantics we also deal with user data
  • Given a user profile and context data We try to recommend various interesting features
  • 3 more examples of interactive apps taking user profiles and context into account
  • Interested in involving the public Tagging is not an issue as quality is an issue
  • Internal elaborate scheme For the web users this would never work Thought of the miniman essential elements for the annotation of this Interested in quality and trust of this data Interaction challenges / problems Autocompletion Ordering Grouping - as a form of diasambiguation – helping people to find the right term There are multiple vocabularies behind (containing duplicates)
  • One step back If in general we do something with users and semantics Have to overlay the users with semantics - contextualizing (context can be very difficult – place, time activity) Granularity of data - number of types of formats - multiple applcations
  • Interesting related problem Relatioships
  • Changing the perspective: From OWL profiles to User profiles

    1. 1. Changing the perspective: OWL USER profiles Lora Aroyo [email_address] Web & Media Group Faculty of Computer Science VU University Amsterdam, The Netherlands twitter: @laroyo
    2. 2. Acknowledgements <ul><li>Kamil Afsar </li></ul><ul><li>Pieter Bellekens </li></ul><ul><li>Dan Brickley </li></ul><ul><li>Maarten Brinkerink </li></ul><ul><li>Rahul Choudhury </li></ul><ul><li>Annelies van Ees </li></ul><ul><li>Michiel Hildebrandt </li></ul><ul><li>Geert-Jan Houben </li></ul><ul><li>Peter Gorgels </li></ul><ul><li>Riste Gligorov </li></ul><ul><li>Annelies Kaptein </li></ul><ul><li>Johan Oomen </li></ul><ul><li>Jacco van Ossenbruggen </li></ul><ul><li>Guus Schreiber </li></ul><ul><li>Natalia Stash </li></ul><ul><li>Just Vervaart </li></ul><ul><li>Yiwen Wang </li></ul>
    3. 3. use case 1: the semantics of Marilyn Monroe iFanzy personalized program guide
    4. 4.
    5. 5. take home message <ul><li>The challenge is to: </li></ul><ul><ul><li>combine semantics with user context </li></ul></ul><ul><ul><li>combine real world & web user data </li></ul></ul><ul><ul><li>determine user relevance </li></ul></ul><ul><li>We need: </li></ul><ul><ul><li>real applications, long-term usage and continuous development and testing </li></ul></ul><ul><ul><li>with real (Web & other) content </li></ul></ul>
    6. 6. use case 2: who knows most about Johan van Oldenbarneveld? semantic annotation of 700 000 prints of the Rijksmuseum ?
    7. 8. multiple vocabularies
    8. 9. users, semantics and content © danbri
    9. 10. distributed context © danbri
    10. 11. some of the issues <ul><li>Collection of activities/context/attention data </li></ul><ul><li>Derive interests from this data </li></ul><ul><li>Recommender-specific problems, e.g. cold start, over-specialization </li></ul><ul><li>Surface programs of interest in the ‘long tail’ </li></ul><ul><li>Cross-domain recommendations </li></ul><ul><li>Multi-person recommending </li></ul><ul><li>Granular control for users </li></ul>
    11. 12. generate automatically tours
    12. 13. semantic recommendations
    13. 14. semantic recommendations
    14. 15. semantic recommendations
    15. 16. semantic features
    16. 17. semantic relationships
    17. 18. semantic tagging: video
    18. 19. semantic tagging: video
    19. 20. most used tags discovered tags highest scored tags
    20. 22. some of the issues <ul><li>vocabularies are : large, heterogenous and overlap ( semantic interoperability required ) </li></ul><ul><li>interface is challenged for: large coverage & effective presentation of results </li></ul><ul><li>images vs videos : </li></ul><ul><ul><li>videos segmentation </li></ul></ul><ul><ul><li>annotation per segment </li></ul></ul><ul><ul><li>aggregate all the annotations together </li></ul></ul><ul><ul><li>previous segments (context for suggestions and disambiguation) </li></ul></ul>
    21. 23. take home message <ul><li>The challenge is: </li></ul><ul><ul><li>to combine semantics with user context </li></ul></ul><ul><ul><li>to combine physical world & web-world </li></ul></ul><ul><ul><li>to find relevance (rank & select) to the end user </li></ul></ul><ul><li>We need: </li></ul><ul><ul><li>real applications, users & context of use in long-term usage and continuous testing </li></ul></ul><ul><ul><li>with real (Web & other) content and user data </li></ul></ul>
    22. 24. examples: semantic television
    23. 25. Extra slides
    24. 26. breaking walls - building bridges <ul><li>Distributed, Web-based multimedia collections </li></ul><ul><li>Semantic Culture Web </li></ul><ul><ul><li>MultimediaN E-Culture: cultural search engine </li></ul></ul><ul><ul><li>Europeana: the EU Culture portal </li></ul></ul><ul><ul><li>CHIP: personalized museums </li></ul></ul><ul><li>Semantic Television </li></ul><ul><ul><li>iFanzy, NoTube, PrestoPrime </li></ul></ul><ul><ul><li>annotation and access to TV archives </li></ul></ul><ul><ul><li>personalized integration of TV and Web content </li></ul></ul>
    25. 28. examples: semantic search
    26. 29. examples: semantic search
    27. 31. semantics and content
    28. 32. semantic annotation: video