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  • Explain semantic web ( make the web more intelligent, providing homogenously structured meta data, allow reasoning) Two parted problem: Semantic web needs dataSemantic Web needs Machine processable Meta data; Backbone of the Semantic WebNot a one time effort, Continuos process, Web highly dynamic entityTasks that require human input - Difficult for machines, easy for human “computation”Captchas: distorted images to distinguish between maschinesHigh level Semantic Annotation: Audio / Video / Images / ConceptualizationOntology aligment / evaluation / learning / Building hierrachies / reuse, propertiesSo, If we need human => howto motivate them
  • people play games, a lot, growing sector, getting increasingly popularcollected some numbers, speak for themselvesEspecially intersting for our case: casusal games (no steap learning curve, little hardware requirements, high availability via web) and quiz gamesMass audience:High potential => free labour, news.php provide input, unused cycles
  • Second part of Talk, introduce games*advertisment
  • Small scale evaluation, Same level of consensual answers, 80% of the resulting alignments were correctAt time of evaluation: 190gamerounds, 32 concepts, 16 players, average answers per player 23,56, number of records recorded 882Difficulty: 31.2% considered it difficult, majority would not want to play it againFun: huge majority didn’t consider it fun, majority don’t want to play againLearned => next version, compiled all we learned => SLODTheLink
  • Correct: Image and concept matchedConcept:

Transcript

  • 1. Semantic Games
    Year 2 Review 2011
    Katharina Siorpaes, UIBK
    www.insemtives.eu
    http://blog.insemtives.eu
  • 2. Agenda
    Introduction
    The OntoGame series
    Lessonslearnt
    Gaming API
    www.semanticgames.org
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  • 3. Introduction
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  • 4. Problem
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  • 5. Games
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    > 300million downloads1
    They are fun…
    200 million
    people play casual games2
    >15 million daily active players3
    Casual Games Market Report, 2007
    http://www.casualgamesassociation.org/news.php
    Facebook application statistics, Feb 2011
    5
  • 6. Casual games
    Steep learning curve
    Fast game play: little time effort required
    Simple implementation (simple interface and graphics)
    Low hardware efforts (usually browser or mobile app)
    Low bandwidth requirements
    Mass audience
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  • 7. The ontogameseries
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  • 8. 8
    http://www.ontogame.org
    http://apps.facebook.com/ontogame
  • 9. OntoPronto and OntoTube
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  • 10. OntoGame
    Players paired randomly and anonymously
    Best strategy to get points: truthful answers
    2 player live mode or
    Community matching mode
    Skip
    Limited amount of time
    Cheating:
    Anonymity
    Pre-recorded challenges
    Generic gaming platform
    Derive formal representations of the data
    All data exported as linked data
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  • 11. SpotTheLink: ontologymatching
    Players: 2 (anonymouslypaired)
    Time: 2 minutes
    Type: Online casualgame (browser)
    Task:
    Describingrelationshipsbetween 2 concepts.
    Input ontologies in thisexample: DBPedia and Proton.
    Input: 2 ontologies, SKOS concepts.
    Output: Alignmentsbetween 2 ontologies.
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  • 12. SpotTheLink: ontologymatching
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  • 13. SpotTheLink: ontologymatching
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  • 14. www.insemtives.eu
    SpotTheLink – Data generation and results
    • Align DBpedia concepts with ProtonT concepts using SKOS
    • 15. 77.84% consensual answers, 80% valid alignments
    PROTON
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  • 16. SEAFISH: image annotation
    Players: 2 (anonymouslypaired)
    Time: 2 minutes
    Type: Online casualgame (browser)
    Task: Sortingrelated and unrelatedimages.
    Input: DBPediaimages, Google images.
    Output: Improved image annotations.
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  • 17. SEAFISH
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  • 18. www.insemtives.eu
    SEAFish – Annotating images (1/2)
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  • 19. www.insemtives.eu
    SEAFish– Annotating images (2/2)
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  • 20. SeaFish – Collected Data
    3 Different data sets
    An online survey
    Feedback from Siegen, Trento
    14456 Modeling decisions
    931 Game rounds
    548 Generated annotations
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  • 21. SeaFish evaluation
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    96% of the players understood the game’s goal.
  • 22. Tubelink: videointerlinking
    Players: 2 (anonymouslypaired)
    Time: TBD
    Type: Online casualgame (browser)
    Task: Choosingnodesfrom LOD forvideos.
    Selectingdatasets,
    Selectingnodes.
    Input: YouTubevideos, LOD (DBPedia).
    Output: AnnotationsforYouTubeusing LOD.
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  • 23. Tubelink
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  • 24. Tubelink
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  • 25. Lessonslearnt
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  • 26. Lessonslearnt
    Task selection
    Not too easy nor too difficult, suitable for a broad audience, not too many game rounds (3-5)
    Simple challenges (nottoomanystages, etc.)
    Limitedspace of choice
    Allow fast gameplay
    Game fun: user interface and social factor
    Compromise: usability and appealing design
    It isnot trivial to make an interesting user interface
    Competition and reputation
    Ranking is a good motivator
    Sociability
    Playing against other people ismotivating
    Partner!
    Knowledge corpora
    Interesting domain, structure and size of the corpora
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  • 27. GAMING Api
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  • 28. OntoGame API
    API that provides several methods that are shared by the OntoGame games, such as:
    Different agreement types, e.g. Selection Agreement
    Input matching (e.g. majority)
    Game modes (double player, single player)
    Player reliability evaluation.
    Player matching, e.g. finding the optimal partner to play.
    Resource( = data needed for games) management.
    Semantic data extraction of stored game data.
    http://insemtives.svn.sourceforge.net/viewvc/insemtives/generic-gaming-toolkit
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  • 29. www.semanticgames.org
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  • 31. 4/13/11
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  • 32. Summary
    Games
    www.ontogame.org
    SPARQL endpoint
    http://ontogame.sti2.at:8080/openrdf-workbench/repositories/onto-game/
    Communityportal
    www.semanticgames.org
    Gamessurvey
    http://www.insemtives.eu/games.php
    Interlinking survey
    http://www.sti-innsbruck.at/results/browse/technical-reports/details/?uid=62
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  • 33. References
    Luis von Ahn. Games With A Purpose. IEEE Computer Magazine, June 2006. pp 96-98.
    Luis von Ahn and Laura Dabbish. Designing games with a purpose. Communications of the ACM, 2008.
    Katharina Siorpaes and Elena Simperl: Human Intelligence in the Process of Semantic Content Creation, World Wide Web Journal (WWW), Volume 13, Issue 1-2, March 2010.
    Katharina Siorpaes and Martin Hepp: Games with a Purpose for the Semantic Web. IEEE Intelligent Systems, Vol. 23, No. 3, pp. 50-60, May/June 2008.
    CasualGame Association White Paper: http://www.casualgamesassociation.org
    List of games: http://www.insemtives.eu/games.php
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  • 34. BACKUP
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  • 35. Tasks and deliverables
    All tools are available on http://sourceforge.net/projects/insemtives/
    Information about tools is provided on http://insemtives.eu/community/tools
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  • 36. Work plan view
    Months
    24
    12
    18
    30
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    6
    0
    Tasks
    SEMANTIC GAMES
    D4.1.1 Requirements and Design of a Generic Gaming Toolkit and API
    D4.1.2 Generic Gaming Toolkit and API implementation
    D4.1.3 Games
    Task 4.1 Genericgaming toolkit
    UIBK
    WEB SERVICE ANNOTATION CHALLENGE
    D4.2.2 Human‐driven annotation tools for Web services
    D4.2.1 Human‐driven annotation tools for Web services
    Task 4.2 Human‐driven Annotation Tools
    SEEKDA/ONTO
    D4.2.4 Human‐driven media annotation tools
    BOOTSTRAPPING TOOL AND L!NKS SEMI AUTOMATIC IMAGE ANNOTATION
    D4.2.3 Human‐driven media annotation tools
    D4.3.2 Bootstrapping tools for image files
    Task 4.3 Bootstrapping Tools
    UNITN
    D4.3.1 Bootstrapping tools for image files
    L!NKS MANUAL IMAGE ANNOTATION AND SEARCH
    D4.4.2 Search and Navigation tools
    Task 4.4 Search and Navigation Tools
    ONTO
    D4.4.1 Search and Navigation tools
  • 37. Guidelines
    Timed response
    Score keeping
    Player skill level
    High score lists
    Randomness
    Random player pairing
    Player testing
    Repetition
    Taboo outputs
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    Luis von Ahn. Games With A Purpose. IEEE Computer Magazine, June 2006. pp 96-98.
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  • 38. How to design your own game
    Specify output
    Identify input
    Choose type of game and define game play
    Based on previous decisions, define game play and adapt underlying game ontology
    Adapt or define export algorithm
    Evaluate output
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  • 39. OntoTube: designflaws
    Consensus findinghard: toomanyoptions to choosefrom.
    Gameplaycomplex: toomany different stages (8 questions).
    Toocomplexinterfaces: playershave to choosefromwidelyvaryingUis in different stages of the samegame.
    Videos toolong: for a fast gameplay, usersshouldnotberequired to watch the completevideo.
    Socialfactor: the interactionwith the partneristoolow – players do notenjoy the gamebecause the emphasis on the socialcomponentismissing.
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  • 40. Challenges
    Identifying suitable tasks in semantic content creation
    Designing games
    Designing a usable, attractive interface
    Identifying suitable knowledge corpora
    Preventing cheating
    Defusing typical pitfalls of conceptual modeling
    Distribution of labor
    Fostering user participation
    Deriving formal representations
    Scalability and performance
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  • 41. Evaluation
    • 2 resource data sets:
    • 42. Game logs
    • 43. Questionaire about the game
    • 44. 16 players, mostly male and computer scientists
    • 45. ~ 300 minutes played game time, 380 game rounds, 882 inputs recorded
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  • 46. Evaluation results
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  • 47. SpotTheLinkResults
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