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Unleashing Expressivity

Unleashing Expressivity. Linked Data for Digital Collections Managers. Mountain West Digital Library Hubs Meeting March 17, 2014. Cory Lampert Head, Digital Collections. Agenda. Why? Primer on LD for Digital Library Managers Opening our minds to a post-”record” world

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Unleashing Expressivity

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  1. Unleashing Expressivity Linked Data for Digital Collections Managers Mountain West Digital Library Hubs Meeting March 17, 2014 Cory Lampert Head, Digital Collections

  2. Agenda • Why? Primer on LD for Digital Library Managers • Opening our minds to a post-”record” world • Empowering others through practical exploration • Technologies • Transforming metadata into linked data • Let’s see it! • Next steps

  3. Linked Data Overview • My collections are already visible through Google; so who cares • This is a topic for catalogers • It’s too technical / complicated / boring Actually ... • Linked data is the future of the Web • Data will no longer be in trapped in silos imposed by systems, collections, or records • Exposed open data presents new opportunities for users

  4. What is Linked Data? • Linked Data refers to a set of best practices for publishing and interlinking data on the Web • Data needs to be machine-readable • Linked data (Web of Data) is an expansion of the Web we know (Web of documents)

  5. Current Practice • Data (or metadata) encapsulated in records • Records contained in collections • Very few links are created within and/or across collections • Links have to be manually created • Existing links do not specify the nature of the relationships among records This structure hides potential links within and across collections

  6. What we can do with linked data • Free data from silos • Expose relationships • Powerful, seamless, interlinking of our data • Users interact or query data in new ways • Search results would be more precise • Data can be easily repurposed

  7. Why?Our data needs an upgrade. http://5stardata.info/

  8. The Linked Data Cloud

  9. A Post-”record” world • Despite limitations, we have already invested lots of resources in the metadata! It is valuable. • Triples represent the next evolution in powerful, flexible, standards-based interoperable metadata. • Each metadata field may produce one or several statements • One metadata record can produce many, many, triples

  10. How can we create linked data? • Our metadata records are deconstructed in triples (statements) that are machine-readable • Triples are expressed as: Subject – Predicate - Object For example: This book – has creator – Tom Heath This book – has title – Linked Data: Evolving the…” • Subjects, predicates and most objects should have unique identifiers (URIs) creating data that can be used in Web architecture (HTTP) • These statements are expressed using the Resource Description Framework (RDF) • Linked data can be queried using SPARQL

  11. Example of a metadata record

  12. Expressing metadata as triples • <this thing> <has creator> <Las Vegas News Bureau> • < this thing > <has genre> <Photographic print> • < this thing> <depicts> <Frank Sinatra> • < this thing> <depicts> <Jack Entratter> ------------------------------------------------------------------- • <Frank Sinatra> <has profession> <entertainer> • <Jack Entratter> <has profession> <theatrical producer>

  13. Graphical Representation

  14. Examples of records Showgirls Dreaming the Skyline Menus

  15. title

  16. How can I transform textual triples into machine-readable? • We need a data model • Europeana Data Model gives us a framework to help organize, structure, and define which predicates we are going to use • Adopting an existing model is preferable to creating your own (interoperability)

  17. title

  18. Triples with URIs & EDM model predicates (Local URI)

  19. Machine-readable triple @prefix dc: <http://purl.org/dc/elements/1.1/> . @prefix edm: <http://www.europeana.eu/schemas/edm/> . @prefix foaf: <http://xmlns.com/foaf/0.1/> . <http://digloc7.library.unlv.edu:8890/ProvidedCHO/sho000071> dc:creatorhttp://digcol7.library.unlv.edu:8890/Agent/Las-Vegas-News-Bureau . <http://digloc7.library.unlv.edu:8890/ProvidedCHO/sho000071> foaf:depicts <http://id.loc.gov/authorities/names/n50026395> . <http://digloc7.library.unlv.edu:8890/ProvidedCHO/sho000071> edm:hasTypehttp://id.loc.gov/vocabulary/graphicMaterials/tgm007779 .

  20. “I’m a digital collections manager”… • What is known? – lots of THEORY and lots of TECHNICAL information • What is happening? – a move toward PRACTICE and APPLICATION in libraries by non-programmers • Is there a “recipe” yet?- No. But, our staff CAN do significant work to prepare for linked data and to understand linked data principles, even if it isn’t realistic to run a parallel process.

  21. UNLV Linked Data Project Goals: • Study the feasibility of developing a common process that would allow the conversion of our collection records into linked data preserving their original expressivity and richness • Publish data from our collections in the Linked Data Cloud to improve discoverability and connections with other related data sets on the Web

  22. Actions Technologies Prepare data Export data CONTENTdm Import data Clean data Reconcile Generate triples Export RDF Open Refine Import data Publish Mulgara / Virtuoso

  23. Export Data and Prepare • Increase consistency across collections: • metadata element labels • use of CV, • share local CVs • etc. • Map metadata elements with predicates from data model • Export data as spreadsheet

  24. OpenRefine • Open Source • It is a server – can communicate with other datasets via http • Install Open Refine and its RDF extension Screenshots to cover some functions we have used so far

  25. OpenRefine first screen

  26. Facet

  27. Split multi-value cells

  28. Reconciliation

  29. Specifying Reconcilation Service

  30. Activating Reconcilation

  31. Creating a Skeleton

  32. Exporting RDF Files

  33. Actions Technologies Prepare data Export data CONTENTdm Import data Clean data Reconcile Generate triples Export RDF Open Refine Import data Publish Query Mulgara / Virtuoso

  34. Mulgara Triple Store: Import

  35. Simple SPARQL query Select * Where {?s ?p ?o} limit 100

  36. Yeah, but what does it look like – to humans? • Pivot Viewer and Virtuoso: http://www.microsoft.com/silverlight/pivotviewer/ • RelFinder: www.visualdataweb.org/relfinder.php • Gelphi: Linked Jazz: http://linkedjazz.org/network/

  37. Query an interface

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