Taking Action: Linked Data for Digital Library Managers

1. Taking Action: Linked Data for Digital Library Managers American Library Association Annual Meeting June 28, 2014 Las Vegas, NV Silvia Southwick and Cory Lampert UNLV Digital Collections

2. Agenda • Motivation • Environment • UNLV Linked Data project • Technologies used for transforming metadata into linked data • Visualizations of linked data (demos) • Next steps and Q & A

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 Open Data Cloud

9. Making the Case for Linked Data Problem: • Rich metadata is being lost when adopting a standard that is designed for interoperability (Dublin Core) • Rationale for adopting linked data is being disseminated, but there is very little practical implementation to serve as reference; no “recipe” or uniform solution • Evolving beyond records takes resources and requires embracing an exciting but uncertain future

10. Example of a metadata record

11. 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

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. Graphic Representation

14. Triples and RDF • Once we have triples we need to: • Assign URIs to each subject • URIs definitely are used for subjects, and might also represent objects. URIs are essential for constructing RDF statements These steps take the human readable graph and make it machine readable!

15. Examples of records Showgirls Dreaming the Skyline Menus

16. title

17. How can I transform textual triples into machine-readable? • We need vocabularies to express our triples • Even better – a data model with these vocabularies • 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)

18. title

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

20. 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 .

21. “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.

22. 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

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

24. Phase 1 • Clean data • Export data

25. Clean / Export Data Technology: CONTENTdm • Increase consistency across collections: • metadata element labels • use of well-known CVs • share local CVs • etc. • Export data as spreadsheet

26. Phase 2 • Import to OpenRefine • Prepare (Reconcile) • Generate triples • Export RDF files

27. OpenRefine • Open source • It is a server – can communicate with other datasets via http • Open Refine and its RDF extension should be installed Screenshots to show some of the functions we have used

28. Import

29. Facets

30. Split multi-value cells

32. Facet view for Graphic Elements after splitting

33. Reconciliation

34. Specifying Reconciliation service

35. Activating Reconciliation

39. Creating the Mapping (Skeleton)

42. Exporting RDF files

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

44. Phase 3 • Import data • Publish • Query

45. Mulgara Triple Store: Import

47. Simple SPARQL Query Select * Where {?s ?p ?o} limit 100

49. Visualization Open Source Tools • OpenLink Virtuoso Pivot Viewer • RelFinder UNLV Linked Data Blog with videos: http://www.library.unlv.edu/linked-data/2014/04/selected-presentations-project.html

50. OpenLink Pivot Viewer • Good for displaying images • Selection of images through SPARQL Queries • Allows refinements using facets • Allows creating dynamic “collections”