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CROWD SOURCINGART HISTORY,[object Object],Research and Applications of ,[object Object],Social Tagging in Museums,[object Object],Robert Stein, Indianapolis Museum of Art,[object Object],Robert Stein,[object Object],Chief Information Officer,[object Object],Indianapolis Museum of Art,[object Object],rstein@imamuseum.org,[object Object],@rjstein,[object Object]
MUSEUMS HAVE,[object Object],A PROBLEM,[object Object]
COLLECTIONS ,[object Object],ARE TOO BIG TO ,[object Object],BROWSE,[object Object],(even for small collections),[object Object],Flickr Credit ~andrewhowson,[object Object]
Crowd sourcing art history
54,000 objects in collection,[object Object],2,242 objects on display (4%),[object Object],26,268 objectimages (48%),[object Object]
Unless you know what to look for…,[object Object],Descriptive meta-data to support browsing is limited,[object Object],Browsing is hard,[object Object],Flickr Credit ~andercismo,[object Object]
Search-only interfaces inhibit discovery,[object Object]
From: J. P. xxxxxx@xxxxxx.com,[object Object],Date: Sat, 26 Aug 2006 11:24:43 -0700,[object Object],To: timeline@metmuseum.org,[object Object],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?,[object Object]
Crowd sourcing art history
Crowd sourcing art history
What “J. P.” knows:,[object Object],painting,[object Object],Renaissance,[object Object],standing,[object Object],man,[object Object],very well dressed,[object Object],library,[object Object],hourglass,[object Object],table,[object Object],rich colors,[object Object],What a Met curator knows:,[object Object],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),[object Object],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],[object Object]
How can visitors,[object Object],take part in powering ,[object Object],their own experience?,[object Object],source ~ mindcaster-ezzolicious,[object Object]
Visitors As Data,[object Object],Visitors Havethe BrainPower We,[object Object],Want,[object Object],Credit: Benedict Campbell,[object Object]
Unfortunately, they aren’t,[object Object],clones who will do ,[object Object],our bidding,[object Object],source ~donsolo,[object Object]
MUSEUM,[object Object],IMPACT,[object Object],VISITOR ,[object Object],ENGAGEMENT,[object Object],Can we create a virtuous circle with visitors that clearly expresses the value and impact of their participation?,[object Object],source ~m-louis,[object Object]
Steve.Museum,[object Object],Exploring Applications of Social Tagging for Museums,[object Object],Founded in 2005,[object Object],2006 Institute for Museum and Library Services (IMLS) National Leadership Research Grant,[object Object],2008 IMLS NLG Steve In Action,[object Object],2008 IMLS NLG Research Grant   T3: Text, Tags, Trust,[object Object],Open Source software supporting tagging in museums,[object Object]
Steve.Museum,[object Object],Exploring Applications of Social Tagging for Museums,[object Object],Founded in 2005,[object Object],2006 Institute for Museum and Library Services (IMLS) National Leadership Research Grant,[object Object],2008 IMLS NLG Steve In Action,[object Object],2008 IMLS NLG Research Grant   T3: Text, Tags, Trust,[object Object],Open Source software supporting tagging in museums,[object Object],http://tagger.steve.museum,[object Object]
www.steve.museum            steve@steve.museum,[object Object],Why study social tagging?Every participant had a different answer,[object Object],Can tagging help users find art more easily?,[object Object],Can tagging change the way users look at and engage with art?,[object Object],Can tagging help museums understand what visitors see and understand?,[object Object]
Uncomfortable,[object Object]
John Singleton Copley, Portrait of Paul Revere,[object Object]
Tag: Jack Black,[object Object]
Winslow Homer, The Gulf Stream,[object Object],Tag: dolphins, leisure,[object Object]
Winslow Homer, The Gulf Stream,[object Object],Tag: dolphins, leisure,[object Object],Accuracy?,[object Object]
2006-08 Research ResultsDownload the 10MB file at http://www.steve.museum,[object Object]
Some stats from the research ,[object Object],11 		Participating Museums,[object Object],1,784 	Works of Art in the Research ,[object Object],93,380 	Tags collected* ,[object Object],2,275 	Users who tagged*,[object Object],*Derived from the sum of statistics from single and multi-institutional deployments,[object Object]
A Few Highlights,[object Object],Museum professionalsfound most tags useful,[object Object],88% of tags were useful,[object Object],If you found this work using this term would you be surprised?,[object Object]
A Few Highlights,[object Object],Tags are different than museum documentation,[object Object],86% of all tags not found in label copy,[object Object],62% of distinct tags not in AAT,[object Object],85% of distinct tags not in ULAN,[object Object]
A Few Highlights,[object Object],Tags are almost always useful when they are assigned two or more times,[object Object]
A Few Highlights,[object Object],Institutional Affiliation Matters,[object Object],Users invited to tag by a single institution were 4 times as productive,[object Object],Multi-Institution Tagger: 22 tags / user,[object Object],Single-Institution Tagger: 82 tags / user,[object Object]
Photo Credit ~warzauwynn (Flickr),[object Object],Putting Steveto work,[object Object]
Steve in Action,[object Object],Funded in 2008 by the IMLS,[object Object],A Few Project Goals,[object Object],Make Social Tagging Easy,[object Object],Generalize to all object collections,[object Object],Abstract Data for Tags,[object Object],Develop Innovative NewInterfaces,[object Object],Facilitate Cross-CollectionSearch / Browsing,[object Object]
Current Tagger Stats,[object Object],18 		Participating Institutions,[object Object],65,708 	Objects in the Tagger,[object Object],427,624 	Tags collected ,[object Object],4,159 	Users who tagged,[object Object]
Steve in Action Features,[object Object],Simple Import (CSV, CDWA, Scraping),[object Object],Hosted and Themable Data Collection Platform,[object Object],Powerful API Access,[object Object],Cut-n-Paste Tagging Widgets for Easy Integration,[object Object]
Crowd sourcing art history
Crowd sourcing art history
Drive user-experience with tagging,[object Object],Can tagging be fun?,[object Object]
Crowd sourcing art history
Crowd sourcing art history
Crowd sourcing art history
Crowd sourcing art history
Crowd sourcing art history
Crowd sourcing art history
Crowd sourcing art history
Crowd sourcing art history
LOST IN A CLOUD,[object Object]
Finding a Needle in the Haystack,[object Object]
Crowd sourcing art history
Funded in 2008 by IMLS,[object Object],With the University of Maryland, and collaborative of museum partners,[object Object],Studying the relationships between social tags, scholarly text and resources, and the application of trust networks to improve access to museum collections.,[object Object]
Heirarchy for Tags,[object Object]
Can we create hierarchy automatically?,[object Object],Supporting Semantic Analysis…,[object Object]
TermProcessing Framework,[object Object],TokenStreamProcessor,[object Object],Performs an operation on a token,[object Object],TokenStreamPipeline,[object Object],A sequence of TokenStreamProcessors to apply in order,[object Object],Taggers,[object Object],A special TokenStreamProcessor that adds metadata to a term.,[object Object]
Token Stream,[object Object],May contain one or more terms/tags in sequence,[object Object],The,[object Object],fox,[object Object],jumped,[object Object],over,[object Object],the,[object Object],lazy,[object Object],dog.,[object Object],quick,[object Object],brown,[object Object],INPUT,[object Object],Tokenize,[object Object]
Token Stream,[object Object],Remove capitalization and punctuation,[object Object],the,[object Object],fox,[object Object],jumped,[object Object],over,[object Object],the,[object Object],lazy,[object Object],dog,[object Object],quick,[object Object],brown,[object Object],INPUT,[object Object],Tokenize,[object Object],RemoveCapitals,[object Object],RemovePunctuation,[object Object]
Token Stream,[object Object],Remove Stop Words,[object Object],fox,[object Object],jumped,[object Object],over,[object Object],lazy,[object Object],dog,[object Object],quick,[object Object],brown,[object Object],INPUT,[object Object],Tokenize,[object Object],RemoveCapitals,[object Object],RemovePunctuation,[object Object],RemoveStopWords,[object Object]
Token Stream,[object Object],Tag Part of Speech and Normalize morphology,[object Object],fox,[object Object],jump,[object Object],over,[object Object],lazy,[object Object],dog,[object Object],quick,[object Object],brown,[object Object],INPUT,[object Object],ADJ,[object Object],ADJ,[object Object],N,[object Object],V,[object Object],ADV,[object Object],ADJ,[object Object],N,[object Object],Tokenize,[object Object],RemoveCapitals,[object Object],RemovePunctuation,[object Object],RemoveStopWords,[object Object],PartOfSpeechTagger,[object Object],MergeMorphology,[object Object],TermContexts,[object Object]
Sample Chain,[object Object],import steve.proccessing as sp,[object Object],filter1 = sp.filters.RemoveWhiteSpace(),[object Object],filter2 = sp.filters.LowerCase(),[object Object],filter1.setInput([‘Star Wars’, ‘lightsaber’]),[object Object],filter2.setInput(filter1),[object Object],Filter2.getOutput(),[object Object],Result = [[‘starwars’], [‘lightsaber’]],[object Object]
Raw Tags,[object Object]
Morphy Normalization,[object Object],Naïve Normalization yields 105,547 distinct terms,[object Object],Morphological Normalization yields 70,295 distinct terms,[object Object],33% Reduction in the corpus,[object Object]
Multi-Word Tags,[object Object],Approximately 20% of tags contain more than one word (46% distinct),[object Object],fox,[object Object],jump,[object Object],over,[object Object],lazy,[object Object],dog,[object Object],quick,[object Object],brown,[object Object],ADJ,[object Object],ADJ,[object Object],N,[object Object],V,[object Object],ADV,[object Object],ADJ,[object Object],N,[object Object],?,[object Object],quickbrownfoxjumpoverlazydog,[object Object],[object Object],[object Object]
What About “New England”,[object Object],Idioms / lexicalize phrases are more difficult,[object Object],Heuristic comparison to Wikipedia Titles matched 46% (30% distinct) of multiword tags,[object Object],i.e. “Grapes of Wrath”, “Irish Wolfhound”, “Franco-Prussian War”,[object Object],*Klavans and Golbeck, 2010,[object Object]
Thank You!,[object Object],Questions?,[object Object]

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