Unconference session at the 2013 AAM on OpenGLAM in museums, looking at Linked Open Data and Wikipedia.

1. GLAMbassador: Georgina Goodlander, @bathlander, goodlanderg@si.edu

2. Why be open?
• Global benefit for the public to have access to our collections
• Enable rich research and encourage innovation
• Benefit from increased exposure and promotion of your content
• Being open does not necessarily mean releasing high resolution
images for free
• Being transparent about licensing can help reduce inappropriate use of
your content

3. A To Do List for
your institution!

4. Working with Wikipedia

5. What is Wikipedia?
Wikipedia: The Free Encyclopedia
• Free as in beer AND free as in speech
• Created and refined entirely by volunteers
• Run by the non-profit Wikimedia Foundation
• All edits/versions are recorded indefinitely
• 4 million+ articles, 285 languages
The Five Pillars of Wikipedia
• Wikipedia is an encyclopedia.
• Wikipedia is written from a neutral point of view.
• Wikipedia is free content that anyone can edit, use, modify, and
distribute.
• Editors should interact with each other in a respectful and civil manner.
• Wikipedia does not have firm rules.
(http://en.wikipedia.org/wiki/Wikipedia:Five_pillars)

6. Wikipedia and GLAM
WikiProjects = venues on Wikipedia where editors coordinate
coverage around a particular field or topic
GLAM = Galleries, Libraries, Archives & Museums
The GLAM-Wiki initiative (Galleries, Libraries, Archives, Museums with
Wikipedia) helps cultural institutions share their resources with the
world through high-impact collaboration alongside experienced
Wikipedia editors.
http://en.wikipedia.org/wiki/Wikipedia:GLAM/US

7. Wikipedia and GLAM – Example
The Walters Art Museum, 2012
• Uploaded over 18,000 images to Wikimedia Commons and applied the
same licensing on their website
• Before the upload (March 2012), images from the Walters were used
in 27 Wikipedia articles and viewed 276,843 times. After the upload
(December 2012), images from the Walters were used in 1,357 articles
and viewed 10,016,938 times. Traffic to the museum’s website from
Wikipedia increased 544%

8. Wikipedia Edit-a-thons
• Work with the local Wikipedia community
• Provide resources and expertise to improve Wikipedia articles

9. Wikipedia Edit-a-thons

10. Linked Open Data

11. What is Linked Open Data?
Data published by existing internet protocols that use a URI (Unique
Resource Indicator) as the primary discoverable entity for a resource
(e.g. person, object, web page, etc.)
THE FIVE STARS OF LOD (Tim Berners Lee):
★ make your stuff available on the web (whatever format) under an open license
★★ make it available as structured data (e.g., Excel instead of image scan of a table)
★★★ use non-proprietary formats (e.g., CSV instead of Excel)
★★★★ use URIs to identify things, so that people can point at your stuff
★★★★★ link your data to other data to provide context

12. Current State of (most) Cultural Heritage Data: Info from web pages

13. Step one: Open Data

14. Step two: Linked Open Data

15. Examples
Europeana
• Digitized collections of museums, libraries, archives and galleries
across Europe.
• Open metadata on 20 million texts, images, videos and sounds
• A subset of 2.4 millions objects from 8 direct Europeana providers
encompassing over 200 cultural institutions from 15 countries is
served according to the Linked Data recipes.
• Virtual exhibitions showcase some of the content available.

16. Examples
Pelagios
• Stands for 'Pelagios: Enable Linked Ancient Geodata In Open Systems’
• Aim is to help introduce Linked Open Data into online resources that
refer to places in the Ancient World.
• Allows you to find content related to a specific place

17. What is Linked Open Data good for?
For the American Art Museum, Linked Open Data will:
• Make our collections data more discoverable on the web
• Allow for more sophisticated queries about our collections
• Create connections with other museums
• Create connections with other non-museum resources
• Create connections with our dispersed content on social media
• Help us better adapt to the changing web

18. Getting Started
Our Initial Questions
• Will it take a lot of time and resources to prepare our data?
• How does LOD differ from what a Google search can do?
• Is it foolish to be doing this before standards are in place?
• What if people do inappropriate things with our data?
• Will it be worth the time and effort in the end?
• How do we handle all of the non-public data that we have?
The Project
• Working with the Information Sciences Institute (ISI) and Department
of Computer Science at the University of Southern California.
• Goal: Publish 5-star Linked Open Data of our complete collections
data.
• Project Phases: Prepare the data, Create an ontology, map the data to
RDF, link the data to hub datasets, publish the data.

19. The Process
Preparing the data
• Collections data is stored in TMS
• Publish only the data that is already visible on our website
• Use an existing output report from TMS
• Several fields needed to be interpreted first
Designing the Ontology
• We built our ontology around existing ontologies
• Europeana Data Model v.2, SKOS, Dublin Core, RDA Group 2
Elements, schema.org
Mapping the Data to RDF (Resource Description Framework)
• Used KARMA tool to model the data
(http://www.isi.edu/integration/karma/)

20. The Process
Linking the Data to External Data
• Verify matches before publishing

21. The Process
Linking the Data to External Data (cont…)
• Have already linked artists to:
• DBPedia - 2,194
• New York Times - 70
• Additionally, can link artists to:
• Getty Union List of Artist Names - 2,110 (ULAN is not yet
published as LOD, but will be)
• Rijksmuseum dataset – 551 (links are not yet verified)
• In the works:
• Linking places
• Linking concepts
• Linking to datasets from other museums
• Linking to social media content
Publishing
• Plan to publish complete dataset under a CC0 license

22. Conclusions
• Able to convert data for entire collection
• Using KARMA to model the data and verify links reduced time and
resources
• LOD eliminates the “noise” of a Google search
What’s next?
• Embed LOD on our website
• Improve representation of artists on Wikipedia
• Create an ongoing maintenance plan
• Tag object- and person-related museum content on social sites
• Investigate mapping and linking an artwork’s subject
• Expand the LOD in ways that will enhance research
• Create a tool that allows users to “curate stories”:
• http://prezi.com/htrvh2jrcsio/curating-stories-with-linked-open-
data/
• Encourage others to build applications with our data

23. Example

24. Reminder…

25. Thank you!
PLEASE take a sticker and flyer!
Georgina Goodlander
@bathlander
goodlanderg@si.edu