Is Linked Open Data the way forward?

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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.

Transcript of Is Linked Open Data the way forward?

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Back to the Future: Museums and the Web

Is Linked Open Data the way forward?Professional ForumEleanor 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

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1997-2016: Los Angeles, CA.

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Tear Down Data SilosProvide Open Access

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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.

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A Google search for “winslow homer theft”

retrieves documents that users must read to extract relevant information

information

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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

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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.

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Eleanor E. Fink.

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

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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 CountyCraig Knoblock, Director of Data Integration, Information Sciences Institute, University of Southern CaliforniaRobert Sanderson, Information Scientist at Stanford University Libraries Thorny Staples, Director of the Office of Research Information Services at the Smithsonian Institution

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Andrew W. Mellon Foundation Planning Grant

American Art Collaborative Linked Open Data Initiative (AAC)

EducationMission Statement

CommitmentRoad Map

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Road Map over next 18 MonthsIMLS 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

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• 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

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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

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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

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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

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•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

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Creative Commons license E.G.

•Freeing content globally without restriction CC0•Only with Attribution CCBY•Attribution and non commercial CCBY NC

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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

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

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Best Practices GuidelinesEleanor and Diane

W3C G AAC DfC Advisors ISI Emmanuelle SStead

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Introduction and Background includes what AAC identifies as the Initiative’s value added. E.g. leveraging expertise; being able to address tough technical questionsChallenges including how to access data using a federated modelData preparation and mapping Including licensing, considerations in choosing an ontology, submitting data, CRM mapping, proofing, how accurate was KarmaLinking and reconciliation issuesDiscrepancies in handling of dates, dimensions, etc; multiple URIsCreating apps (lessons learned browse demo)IIIF implementationHostingSkills and timeframesConclusions and recommendationsIncluding issues the cultural heritage community will need to address and consider

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American Art Collaborative LOD

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“Data that is in motion is much more preserveable than data on files”Cory Doctorow

THANK YOU

americanartcollaborative.org/