NISO Webinar: Library Linked Data: From Vision to Reality

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About the Webinar
The library and cultural institution communities have generally accepted the vision of moving to a Linked Data environment that will align and integrate their resources with those of the greater Semantic Web. But moving from vision to implementation is not easy or well-understood. A number of institutions have begun the needed infrastructure and tools development with pilot projects to provide structured data in support of discovery and navigation services for their collections and resources.

Join NISO for this webinar where speakers will highlight actual Linked Data projects within their institutions—from envisioning the model to implementation and lessons learned—and present their thoughts on how linked data benefits research, scholarly communications, and publishing.

Speakers:
Jon Voss - Strategic Partnerships Director, We Are What We Do
LODLAM + Historypin: A Collaborative Global Community

Matt Miller - Front End Developer, NYPL Labs at the New York Public Library
The Linked Jazz Project: Revealing the Relationships of the Jazz Community

Cory Lampert - Head, Digital Collections , UNLV University Libraries
Silvia Southwick - Digital Collections Metadata Librarian, UNLV University Libraries
Linked Data Demystified: The UNLV Linked Data Project

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  • Introduce SelfNYPL RoleLinked Jazz Role as a developer, a practical look at how we used linked data in our project and why
  • Guiding principle of the project is to develop practical and applicable ways to use linked data.Not some far off ideal, there are practical applications of it right now.
  • The General layout of a project, research questions and primary documents
  • Data remediation still required.
  • NISO Webinar: Library Linked Data: From Vision to Reality

    1. 1. http://www.niso.org/news/events/2013/webinars/linked_data NISO Webinar: Library Linked Data: From Vision to Reality December 11, 2013 Speakers: Jon Voss - Strategic Partnerships Director, We Are What We Do Matt Miller - Front End Developer, NYPL Labs at the New York Public Library Silvia Southwick - Digital Collections Metadata Librarian, UNLV University Libraries Cory Lampert - Head, Digital Collections , UNLV University Libraries
    2. 2. Linked Jazz Revealing the relationships of the jazz community Matt Miller @thisismmiller December 2013
    3. 3. Project Overview • Investigating the application of Linked Open Data to enhance the discovery and visibility of digital cultural heritage materials. • Build new methods of connecting cultural data. • Uncover meaningful connections between documents and data related to the personal and professional lives of musicians who often practice in rich and diverse social networks. Professor Cristina Pattuelli at the Pratt Institute School of Library Information Science is the director of the project which began in 2011.
    4. 4. Linked Data Now! Why? • Bootstrap your project with existing data. • Highlights knowledge you have created and knowledge that is missing. • Facilitates sharing, but also growing your own project.
    5. 5. Bootstrapping – Identifying Research Question How can we discover and analyze the rich and diverse network of relationships between jazz musicians? Primary Sources Oral history interview transcripts of jazz musicians.
    6. 6. Bootstrapping – Identifying Research Question How can we discover and analyze the rich and diverse network of relationships between jazz musicians? Primary Sources Oral history interview transcripts of jazz musicians. We need to know the names (and variants) of jazz musicians in a structured controlled vocabulary.
    7. 7. Bootstrapping – Identifying Charlie Parker Many different LOD datasets contain this information. We need to access, query and link it for only jazz related individuals.
    8. 8. Bootstrapping – Querying
    9. 9. Bootstrapping – Querying • Processing the DBpedia dataset resulted in around 9,000 URIs. – DBpedia is fluid! After each release (currently 3.9) we reprocess the files resulting in the addition of 500-700 URIs. • We now have a name directory, but we want additional forms of personal names. To accomplish this we try mapping to Library of Congress. • Matching DBpedia and LC URIs is not automatic.
    10. 10. Bootstrapping – Mapping • We matched identities based on: • Name • Life Dates • White listed words found in sources (http://www.loc.gov/mads/rdf/v1#Source) • Reconciling authorities is difficult! • Use others work: http://viaf.org/viaf/data/ • But don’t discount your own processes. • Using our relatively simple process we were able to match about 1500 more URIs than VIAF.org. • This is due to a smaller domain (jazz). Our name directory creation and authority matching is documented: https://github.com/thisismattmiller/linkedjazz-name-directory
    11. 11. Bootstrapping – Curating http://linkedjazz.org/public_demo_mapping/
    12. 12. Bootstrapping – Review • Start small, think big. – Specific subject domain. – Large infrastructure not required (triple stores, etc.) • Can get started with extract files and python scripting. • Reuse as much as possible, but try new processes leveraging domain specificity. • Always be curating, use tools to facilitate process but a human hand is often required.
    13. 13. Applying the Data • Use the name directory to locate individuals in the interview transcript. • This project phase involves 50 transcripts. • Because the names are tied to URIs we can infer a relationship triple between two individuals. <foaf:Person> <rel:knowsOf> <foaf:Person>
    14. 14. Applying the Data
    15. 15. Transcript Analyzer
    16. 16. Transcript Analyzer • An interface to curate the transcripts and verify detected names. • Implements off the shelf NLP (NLTK) to attempt to locate additional names not in our directory as well as corporate names and locations. • Global rule system, as we process more transcripts the system is being trained. • Using URIs to represent entities we can quickly see where we are discovering new material. – 50 Transcripts • 1800 person entities tagged. • 250 names tagged without authoritative URI. – Knowledge Creation
    17. 17. New Dataset • We have created a new LOD dataset now of jazz musician’s relationships. • Our next steps are: – Visualize. – Further qualify the rel:knowsOf relationships. – Provide access to the data created.
    18. 18. Visualize http://linkedjazz.org/network/
    19. 19. Qualify Relationships – 52nd St. • Recruit jazz experts and enthusiasts to help categorize relationships based on transcript text. • We use existing vocabularies to build the data set: Foaf, Relationship Vocabulary, Music Ontology • The interface is critical for crowdsourcing tools, we work with user experience experts and conduct user studies to refine our public facing tools
    20. 20. Qualify Relationships – 52nd St. http://linkedjazz.org/52ndStreet/
    21. 21. Provide Access • We provide a SPARQL endpoint. • But also a traditional API: – http://linkedjazz.org/api/ – Can return: • JSON • N-Triples • Gephi graph files (GXEF)
    22. 22. Learn and Grow as a Team • Experience through doing. • Empower graduate students with skills and practical experience working with a LOD project. • Use the project as a vehicle to make intra- and inter-intuitional collaborations. Linked Jazz Team July 2013
    23. 23. Next Steps • Refactor our prototype tools into sustainable open source projects. • Redesign 52nd St. based on user study groups. • Work on emerging collaborations with Jazz Centers.
    24. 24. Thanks! http://www.linkedjazz.org
    25. 25. Linked, Exposed Data: UNLV Linked Data Project NISO Webinar: Library Linked Data: From Vision to Reality December 11, 2013 Silvia B. Southwick Digital Collections Metadata Librarian UNLV Libraries Cory K. Lampert Head, Digital Collections UNLV Libraries
    26. 26. Agenda • • • • • • Motivation Environment UNLV Linked Data project Technologies Transforming metadata into linked data Next steps
    27. 27. How it Started • • • • Conferences and “buzz” Curiousity and professional development Exploration and pilot project Compelling results; sharing impact of what we’ve learned • Assessment • Much more to do...
    28. 28. 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
    29. 29. 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
    30. 30. Making the Case for Linked Data in Academic Library Digital Collections – Problem: Rich metadata is being lost in dumbed down DC records – Issue: Investment and resource allocation (Item-level philosophy) – Goal: Increased: exposure, collaboration, and openness – Outcome: Increased discovery and user-focus
    31. 31. Gaining Buy In Administration • Innovative project, high impact • Pilot, experiment, learn by doing, share results Staff • We already have the metadata; We need to transform them into triples • Managing change
    32. 32. Graphical Representation: One Record
    33. 33. Examples of records
    34. 34. December 12, 1915 title
    35. 35. Implications (Internal) • Cross-unit collaboration is necessary • Staff expertise will evolve • Staff roles will change to accommodate new / parallel workflow • Data clean-up will be an investment • Management of data becomes critical • Discovery issues = user interfaces still need development
    36. 36. Implications (External) • Publish data from our collections in the Linked Data Cloud to improve discoverability and connections with other related data sets on the Web • Sharing data in new ways with new partners may raise new issues • Need to engage with linked data community for technologies, tools, best practices, and to demand library vendor support for LOD.
    37. 37. 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
    38. 38. PROJECT IMPLEMENTATION
    39. 39. Actions Prepare data Export data Import data Clean data Reconcile Generate triples Export RDF Import data Publish Technologies CONTENTdm Open Refine Mulgara / Virtuoso
    40. 40. Prepare / Export Data Technology: CONTENTdm • Increase consistency across collections: – metadata element labels – use of CV, share local CVs – etc. • Export data as spreadsheet Create mapping between metadata elements and EDM model predicates
    41. 41. 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
    42. 42. OpenRefine first screen
    43. 43. Facets
    44. 44. Split multi-value cells
    45. 45. Facet view for Graphic Elements after splitting
    46. 46. Reconciliation
    47. 47. Specifying Reconciliation service
    48. 48. Activating Reconciliation
    49. 49. Creating a Skeleton
    50. 50. Exporting RDF files
    51. 51. Actions Prepare data Export data Import data Clean data Reconcile Generate triples Export RDF Import data Publish Query Technologies CONTENTdm Open Refine Mulgara / Virtuoso
    52. 52. Mulgara Triple Store: Import
    53. 53. A simple SPARQL query Select * where { ?s ?p ?o} limit 100
    54. 54. SPARQL: Querying Data • Using Virtuoso PivotViewer
    55. 55. Query Costume Design Drawings Showgirls
    56. 56. Next steps for the UNLV project • Transform all digital collections into linked data (parallel structure) • Increase linkage with other datasets • Design interfaces to access and display our data and related data from other datasets • Evaluate alternative interfaces from user’s perspective • Produce a cost benefit analysis to inform future plans for the development of digital collections
    57. 57. Thank You! Questions?
    58. 58. NISO Webinar: Library Linked Data: From Vision to Reality Questions? All questions will be posted with presenter answers on the NISO website following the webinar: http://www.niso.org/news/events/2013/webinars/linked_data NISO Webinar • December 11, 2013
    59. 59. THANK YOU Thank you for joining us today. Please take a moment to fill out the brief online survey. We look forward to hearing from you!

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