This document summarizes a discussion on using Linked Open Data (LOD) for museums. It discusses: 1) The American Art Collaborative (AAC), a consortium of US museums working to implement LOD within their collections to provide open access and interconnect data. 2) The benefits of LOD include telling fuller stories, augmenting collection information by connecting to other institutions, and making data more usable for developers. 3) Challenges include mastering ontologies, data inconsistencies, maintaining accuracy of tools, and understanding implications of different data models. 4) The AAC is developing best practices guides, apps, and open source tools from their experience implementing an LOD initiative over

1. Back to the Future: Museums and the Web
Is Linked Open Data the way forward?
Professional Forum
Eleanor Fink, American Art Collaborative, USA, Shane Richey, Crystal
Bridges Museum of American Art, USA, Jeremy Tubbs, Indianapolis
Museum of Art, USA, Rebecca Menendez, Autry Museum of the
American West, USA, Cathryn Goodwin, Princeton University, USA
.
1997-2016: Los Angeles, CA.

2. Tear Down Data Silos
Provide Open Access

4. Linked Open Data
•A method of publishing structured data so
that it can be interconnected and become
more useful.
•Uses a mark up language called RDF. When
combined with a domain ontology the
relationship between subject, predicate, and
object can be tagged explicitly.
•As a result when you are searching using LOD
you don’t get the “noise” or unrelated
information you get with online searching.

5. A Google search for “winslow homer theft”
retrieves documents that users must read to
extract relevant information
information

6. Linked Data
facts:
<subject> <predicate> <object>
using W3C standards (RDF)
links between facts from different databases
like links between Web pages
Pedro Szekely and Craig KnoblockUniversity of Southern California

9. AAC
Consortium of US museums who have come
together to learn about and implement LOD
within their respective museums. AAC is
developing its LOD under a federated model
whereby each AAC member assumes
responsibility for updating and maintaining
its own data.

11. The American Art Collaborative Partners
Amon Carter Museum of American Art, Archives of
American Art, Autry Museum of the American West,
Colby College Museum of Art, Crystal Bridges
Museum of American Art, Dallas Museum of Art,
Thomas Gilcrease Institute of American History and
Art, Indianapolis Museum of Art, National Museum of
Wildlife Art, National Portrait Gallery, Princeton
University Art Museum, Smithsonian American Art
Museum, Walters Art Gallery, and Yale Center for
British Art
Eleanor E. Fink.

12. American Art Collaborative Advisors
Martin Doerr, Research Director at the Information Systems
Laboratory and head of the Centre for Cultural Informatics
of the Institute of Computer Science, FORTH
Tim Finin, Professor of Computer Science and Electrical
Engineering at the University of Maryland, Baltimore
County
Craig Knoblock, Director of Data Integration, Information
Sciences Institute, University of Southern California
Robert Sanderson, Information Scientist at Stanford
University Libraries
Thorny Staples, Director of the Office of Research
Information Services at the Smithsonian Institution

13. Andrew W. Mellon Foundation
Planning Grant
American Art Collaborative Linked Open
Data Initiative (AAC)
Education
Mission Statement
Commitment
Road Map

14. Road Map over next 18 Months
IMLS Leadership Grant
Andrew W. Mellon Foundation Grant
• Convert data to LOD using the CIDOC CRM
• Link to the Getty Vocabularies as well as
contribute missing names to enhance the
vocabularies
• Implement an API and reader compliant with
the International Image Interoperability
Framework (IIIF) that will allow researchers to
compare and contrast AAC LOD

15. • Develop several open source tools
including a link curation tool and IIIF/CRM
translator
• Develop browse demonstration
• Open access
• Publish best practices and lessons learned

16. Panel Discussion Why LOD
•Everyone wants more meaningful content
•Find new ways to share our information and keep
audiences engaged
•To augment our collection information by connecting to
other museums and institutions
•To better support research and improve access
•Achieve seamless access across museums using LOD
•Make our collections more discoverable
•To tell fuller stories about our objects
•Complementarity: e.g. archives about documents
connecting to works of art
•Explore cross domain connections
•LOD makes our data more useable to developers

17. Why Collaborative
•Learning together
•More comprehensive education
•Building on diverse skills
•Easier to engage and convince management
•Grant Funding to train and produce
•Create best practices

18. Challenges and Uncertainties
•Licensing
•Mastering the CRM
•Data inconsistencies
•Implications of federated or hybrid models versus
aggregation models
•Learning curve and skill set
•Accuracy of tools
•Working with high end technology experts that
are not art specialists
•As new datasets come online, how to connect
•URI identities and connecting to other resources like
Getty Vocabularies that have their own URI

19. •How to know what to connect to
•How to make sure you are connecting to the right
entity (person, place, etc.)
•How to maintain the data
•How to obtain metrics on impact of converting your
data to LOD and see how visitors are using your data
•Exploring what openness means
•How to get dpedia to link back
•How do you encourage people to link to your LOD
Challenges and Uncertainties
•How to retain the authority voice about the object

20. Creative Commons license
E.G.
•Freeing content globally without
restriction CC0
•Only with Attribution CCBY
•Attribution and non commercial CCBY NC

21. Why Federated Versus Aggregation Model
•Better sustainability model: each responsible
for updating and maintaining like websites
•Vision of Tim Berners Lee (distributed data
residing in a LOD Cloud)
•Richness of data
•Make data openly available for aggregators;
include access nodes to other projects

24. ISI’s Karma data integration tool
http://isi.edu/integration/karma

25. Best Practices Guidelines
Eleanor and Diane
W3C G AAC DfC Advisors ISI Emmanuelle SStead

26. Introduction and Background
includes what AAC identifies as the Initiative’s value added. E.g.
leveraging expertise; being able to address tough technical questions
Challenges
including how to access data using a federated model
Data preparation and mapping
Including licensing, considerations in choosing an ontology, submitting
data, CRM mapping, proofing, how accurate was Karma
Linking and reconciliation issues
Discrepancies in handling of dates, dimensions, etc; multiple URIs
Creating apps (lessons learned browse demo)
IIIF implementation
Hosting
Skills and timeframes
Conclusions and recommendations
Including issues the cultural heritage community will need to address
and consider

27. American Art Collaborative LOD

29. “Data that is in motion is much more
preserveable than data on files”
Cory Doctorow
THANK YOU
americanartcollaborative.org/