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Preparing the Way : Creating Future Compatible Cataloguing Data in a Transitional Environment

Preparing the Way : Creating Future Compatible Cataloguing Data in a Transitional Environment. Dean Seeman & Lisa Goddard Memorial University of Newfoundland Faster, Smarter, Richer Conference Rome, Italy February 27 th , 2014. Aspects of our Linked Data Future. Decentralization

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Preparing the Way : Creating Future Compatible Cataloguing Data in a Transitional Environment

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  1. Preparing the Way : Creating Future Compatible Cataloguing Data in a Transitional Environment Dean Seeman & Lisa Goddard Memorial University of Newfoundland Faster, Smarter, Richer Conference Rome, ItalyFebruary 27th, 2014

  2. Aspects of our Linked Data Future Decentralization Collaboration Localization Richness Structure

  3. 1. Decentralization Indexes eJournals Research Repositories Digital Archives Data Sets eBooks

  4. Extract, Transform, Load

  5. Disparate data sources and incompatible data structures are among the biggest obstacles for 21st century humanities researchers. (RIN, 2011)

  6. Decentralization Statements not records. Subject -> Predicate -> Object Subject -> Predicate -> Object Subject -> Predicate -> Object Subject -> Predicate -> Object Subject -> Predicate -> Object Subject -> Predicate -> Object

  7. Decentralization Most data stored remotely. #Shakespeare #creator #Macbeth http://viaf.org http://purl.org/dc http://id.loc.gov

  8. 2. Collaboration

  9. Collaboration

  10. Collaboration

  11. Collaboration

  12. Enhancing Shared Records

  13. Ontology Development

  14. 3. Localization “[I]t is their accumulated special collections that increasingly define the uniqueness and character of individual research libraries.“ - ARL, 2009

  15. Expose Entity URIs http://mun.ca/place http://mun.ca/event http://mun.ca/person Annotation http://mun.ca/org http://mun.ca/annotation http://mun.ca/doc123 http://this.ca/book

  16. 4. Richness

  17. Define Relationships subjectOf bornIn http://this.ca/place http://this.ca/event creator http://this.ca/doc http://this.ca/person setIn subjectOf employedBy employs Annotation hostedBy http://this.ca/org http://this.ca/annotation annotates published adaptedFrom http://this.ca/film http://this.ca/book

  18. 5. Structure Decentralization Collaboration Richness Localization Structure

  19. Structured Data

  20. Semantic Structure Beauty, Personal Manners & customs Broader Apparel Clothes Dress Garments Same As Related Fashion Undressing Clothing Narrower Collars Color in clothing Costume Coveralls Darts (Clothing) Dirndls Doll clothes Dresses Footwear Fur garments Garters Aprons Armbands Belt toggles Belts (Clothing) Bodices Breechcloths Burial clothing Buttonholes Buttons Caftans Cloaks Headgear Hosiery Jackets Jumpsuits Kilts Kimonos Knitwear Lapels Latex garments Leggings Neckwear

  21. Machine-Actionable Data

  22. 4 Cataloguer Tasks in Relation to Linked Data

  23. Trust Standard Development?

  24. Decentralization Collaboration Richness Localization Structure

  25. The greatest consumer of our data is going to be the machine. We have to make our data machine understandable.

  26. Automatic Data Normalization MARC Linked Data Formats

  27. Decentralization Collaboration Richness Localization Automatic Data Normalization Structure

  28. “In computer terms, we have a data normalization problem.” Ross Singer

  29. Decentralization Collaboration Richness Localization Automatic Data Normalization Structure Manual Data Creation (Cataloguing) Good Data

  30. What is Good Data? Ochoa, X., & Duval, E. (2009). Automatic Evaluation of Metadata Quality in Digital Repositories, 10(2), 67–91. doi:10.1007/s00799-009-0054-4

  31. A Few Markers of Good Data for Data Normalization Discrete Each element asserts a single thing Semantically Unambiguous Data should be clear in its meaning and minimize multiple interpretations Consistent Predictable values

  32. This kind of data … Helps us in our current environment Helps the process of data normalization Helps the future … even if it isn’t Linked Data

  33. Looking at the future ... ... what can cataloguers practically do to plug into it?

  34. Authorities & Controlled Access Points

  35. Authorities Contain mostly differentiated values Better for machine processing

  36. Authorities

  37. Controlled Access Points (MARC 1xx, 6xx, 7xx) Automatically Normalized / Translated into URIs Tom Stoppard Author of Work Parade’s End http://www.worldcat.org/oclc/827974267 http://viaf.org/viaf/101362857/rdf.xml http://rdvocab.info/roles/authorWork

  38. Controlled Access Points Better to have this compacted in one statement As opposed to spread throughout the record

  39. AUTHORIZED UNAUTHORIZED

  40. But in our Current Cataloguing Environment, It May Be the Best We Can Do

  41. Vocabularies & Differentiated Values

  42. Vocabularies Provide Consistent Values for Normalization

  43. Already Equipped with URIs

  44. Differentiable Values Example: Exercise the option at RDA 2.8.2.3 for place of publication … make the implicit explicit

  45. Local

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