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A presentation at the ESF-COST symposium on the Networked Humanities - July 2010.

A presentation at the ESF-COST symposium on the Networked Humanities - July 2010.

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  • 1. CROWD SOURCINGART HISTORY
    Research and Applications of
    Social Tagging in Museums
    Robert Stein, Indianapolis Museum of Art
    Robert Stein
    Chief Information Officer
    Indianapolis Museum of Art
    rstein@imamuseum.org
    @rjstein
  • 2. MUSEUMS HAVE
    A PROBLEM
  • 3. COLLECTIONS
    ARE TOO BIG TO
    BROWSE
    (even for small collections)
    Flickr Credit ~andrewhowson
  • 4.
  • 5. 54,000 objects in collection
    2,242 objects on display (4%)
    26,268 objectimages (48%)
  • 6. Unless you know what to look for…
    Descriptive meta-data to support browsing is limited
    Browsing is hard
    Flickr Credit ~andercismo
  • 7. Search-only interfaces inhibit discovery
  • 8. From: J. P. xxxxxx@xxxxxx.com
    Date: Sat, 26 Aug 2006 11:24:43 -0700
    To: timeline@metmuseum.org
    Subject: Looking for a paintingPlease help:I have been looking on and off for years for this painting. The painting is of a very well dressed renaissance man standing in a room (a library) in front of him on a table is a large hour glass. The painting has very rich colors. I have talked to a lot of people and they have said they have seen this painting but can't remember its name or the name of the artist.Could you please use your resources to find this painting?
  • 9.
  • 10.
  • 11. What “J. P.” knows:
    painting
    Renaissance
    standing
    man
    very well dressed
    library
    hourglass
    table
    rich colors
    What a Met curator knows:
    Portrait of a Man, ca. 1520–25Morettoda Brescia (Alessandro Bonvicino) (Italian, Brescian, born about 1498, died 1554)Oil on canvas; 34 1/4 x 32 in. (87 x 81.3 cm)Rogers Fund, 1928 (28.79)
    Provenance: Maffei, Brescia (by 1760, as "Ritrattod'uomo con carta in mano, edOrologio, diCallistoda Lodi"); by descent to contessa Beatrice ErizzoMaffeiFenaroli Avogadro, Palazzo Fenaroli, Brescia (by 1853–at least 1857, as by Moretto); her daughter, contessa Maria LiviaFenaroli Avogadro, later marchesaFassati, Brescia (in 1862); her son, marcheseIppolitoFassati, Milan (by 1878–at least 1912); [EliaVolpi, Florence, by 1915–16; sold to Knoedler]; [Knoedler, New York, 1916–28; sold to MMA]
  • 12. How can visitors
    take part in powering
    their own experience?
    source ~ mindcaster-ezzolicious
  • 13. Visitors As Data
    Visitors Havethe BrainPower We
    Want
    Credit: Benedict Campbell
  • 14. Unfortunately, they aren’t
    clones who will do
    our bidding
    source ~donsolo
  • 15. MUSEUM
    IMPACT
    VISITOR
    ENGAGEMENT
    Can we create a virtuous circle with visitors that clearly expresses the value and impact of their participation?
    source ~m-louis
  • 16. Steve.Museum
    Exploring Applications of Social Tagging for Museums
    Founded in 2005
    2006 Institute for Museum and Library Services (IMLS) National Leadership Research Grant
    2008 IMLS NLG Steve In Action
    2008 IMLS NLG Research Grant T3: Text, Tags, Trust
    Open Source software supporting tagging in museums
  • 17. Steve.Museum
    Exploring Applications of Social Tagging for Museums
    Founded in 2005
    2006 Institute for Museum and Library Services (IMLS) National Leadership Research Grant
    2008 IMLS NLG Steve In Action
    2008 IMLS NLG Research Grant T3: Text, Tags, Trust
    Open Source software supporting tagging in museums
    http://tagger.steve.museum
  • 18. www.steve.museum steve@steve.museum
    Why study social tagging?Every participant had a different answer
    Can tagging help users find art more easily?
    Can tagging change the way users look at and engage with art?
    Can tagging help museums understand what visitors see and understand?
  • 19. Uncomfortable
  • 20. John Singleton Copley, Portrait of Paul Revere
  • 21. Tag: Jack Black
  • 22. Winslow Homer, The Gulf Stream
    Tag: dolphins, leisure
  • 23. Winslow Homer, The Gulf Stream
    Tag: dolphins, leisure
    Accuracy?
  • 24. 2006-08 Research ResultsDownload the 10MB file at http://www.steve.museum
  • 25. Some stats from the research
    11 Participating Museums
    1,784 Works of Art in the Research
    93,380 Tags collected*
    2,275 Users who tagged*
    *Derived from the sum of statistics from single and multi-institutional deployments
  • 26. A Few Highlights
    Museum professionalsfound most tags useful
    88% of tags were useful
    If you found this work using this term would you be surprised?
  • 27. A Few Highlights
    Tags are different than museum documentation
    86% of all tags not found in label copy
    62% of distinct tags not in AAT
    85% of distinct tags not in ULAN
  • 28. A Few Highlights
    Tags are almost always useful when they are assigned two or more times
  • 29. A Few Highlights
    Institutional Affiliation Matters
    Users invited to tag by a single institution were 4 times as productive
    Multi-Institution Tagger: 22 tags / user
    Single-Institution Tagger: 82 tags / user
  • 30. Photo Credit ~warzauwynn (Flickr)
    Putting Steveto work
  • 31. Steve in Action
    Funded in 2008 by the IMLS
    A Few Project Goals
    Make Social Tagging Easy
    Generalize to all object collections
    Abstract Data for Tags
    Develop Innovative NewInterfaces
    Facilitate Cross-CollectionSearch / Browsing
  • 32. Current Tagger Stats
    18 Participating Institutions
    65,708 Objects in the Tagger
    427,624 Tags collected
    4,159 Users who tagged
  • 33. Steve in Action Features
    Simple Import (CSV, CDWA, Scraping)
    Hosted and Themable Data Collection Platform
    Powerful API Access
    Cut-n-Paste Tagging Widgets for Easy Integration
  • 34.
  • 35.
  • 36. Drive user-experience with tagging
    Can tagging be fun?
  • 37.
  • 38.
  • 39.
  • 40.
  • 41.
  • 42.
  • 43.
  • 44.
  • 45. LOST IN A CLOUD
  • 46. Finding a Needle in the Haystack
  • 47.
  • 48. Funded in 2008 by IMLS
    With the University of Maryland, and collaborative of museum partners
    Studying the relationships between social tags, scholarly text and resources, and the application of trust networks to improve access to museum collections.
  • 49. Heirarchy for Tags
  • 50. Can we create hierarchy automatically?
    Supporting Semantic Analysis…
  • 51. TermProcessing Framework
    TokenStreamProcessor
    Performs an operation on a token
    TokenStreamPipeline
    A sequence of TokenStreamProcessors to apply in order
    Taggers
    A special TokenStreamProcessor that adds metadata to a term.
  • 52. Token Stream
    May contain one or more terms/tags in sequence
    The
    fox
    jumped
    over
    the
    lazy
    dog.
    quick
    brown
    INPUT
    Tokenize
  • 53. Token Stream
    Remove capitalization and punctuation
    the
    fox
    jumped
    over
    the
    lazy
    dog
    quick
    brown
    INPUT
    Tokenize
    RemoveCapitals
    RemovePunctuation
  • 54. Token Stream
    Remove Stop Words
    fox
    jumped
    over
    lazy
    dog
    quick
    brown
    INPUT
    Tokenize
    RemoveCapitals
    RemovePunctuation
    RemoveStopWords
  • 55. Token Stream
    Tag Part of Speech and Normalize morphology
    fox
    jump
    over
    lazy
    dog
    quick
    brown
    INPUT
    ADJ
    ADJ
    N
    V
    ADV
    ADJ
    N
    Tokenize
    RemoveCapitals
    RemovePunctuation
    RemoveStopWords
    PartOfSpeechTagger
    MergeMorphology
    TermContexts
  • 56. Sample Chain
    import steve.proccessing as sp
    filter1 = sp.filters.RemoveWhiteSpace()
    filter2 = sp.filters.LowerCase()
    filter1.setInput([‘Star Wars’, ‘lightsaber’])
    filter2.setInput(filter1)
    Filter2.getOutput()
    Result = [[‘starwars’], [‘lightsaber’]]
  • 57. Raw Tags
  • 58. Morphy Normalization
    Naïve Normalization yields 105,547 distinct terms
    Morphological Normalization yields 70,295 distinct terms
    33% Reduction in the corpus
  • 59. Multi-Word Tags
    Approximately 20% of tags contain more than one word (46% distinct)
    fox
    jump
    over
    lazy
    dog
    quick
    brown
    ADJ
    ADJ
    N
    V
    ADV
    ADJ
    N
    ?
    quickbrownfoxjumpoverlazydog
    • Detect noun and verb phrases, proper nouns – split or merge?
  • Lexical Tag Analysis (2 word tags)
    58% NOUN-NOUN
    “lotus flower”
    36% ADJ-NOUN
    “beautiful headdress”
    6% AVD / VERB Combinations
    *Klavans and Golbeck, 2010
  • 60. What About “New England”
    Idioms / lexicalize phrases are more difficult
    Heuristic comparison to Wikipedia Titles matched 46% (30% distinct) of multiword tags
    i.e. “Grapes of Wrath”, “Irish Wolfhound”, “Franco-Prussian War”
    *Klavans and Golbeck, 2010
  • 61. Thank You!
    Questions?