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What do MARC, RDF, and OWL have in common?

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It is understood that in the current library ecosystem, catalogers must be willing to adapt to new semantic web environment while keeping in mind the crucial library mission – providing efficient access to information. How could catalogers transform their jobs in order to enable library users to retrieve information more effectively in the age of semantic web?

Researchers have argued that catalogers have the fundamental skills to successfully work with and repurpose the metadata originally created for use in traditional library systems by utilizing various programing languages. In the new environment their jobs will require new tools and new systems but the basic skills of organization of information, knowledge of commonly used access points, and an ever growing knowledge of information technology systems will still be the same. This presentation will stress the role of catalogers in bringing the data silos down, merging, augmenting, and creating interoperable data that can be used not just in library specific systems, but in various other systems. Catalogers’ indispensable knowledge of controlled vocabularies, authority aggregators, metadata creation, metadata reuse, taxonomies, and data stores makes it all possible.

We will demonstrate how catalogers’ knowledge can be leveraged to design an institutional repository and/or a researchers profiling system, create semantic web compliant data, create ontologies, utilize unique identifiers, and (re)use data from legacy systems.

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What do MARC, RDF, and OWL have in common?

  1. 1. Northwestern University Feinberg School of Medicine What do MARC, RDF, and OWL have in common? Violeta Ilik Head, Digital Systems & Collection Services Galter Health Sciences Library Program for Cooperative Cataloging (PCC) Participants Meeting - ALA 2015, San Francisco, June 28, 2015
  2. 2. Catalogers and MARC Violeta Ilik ALA PCC Participants Meeting, June 28, 2015 San Francisco 2 MARC record Feels like home! • You know the rules, • You know all the intricacies
  3. 3. Catalogers and XML, XSLT, namespaces, MarcEdit … Violeta Ilik ALA PCC Participants Meeting, June 28, 2015 San Francisco 3 Feels like home? • Do you know the rules? • Do you know all the intricacies?
  4. 4. MARCXML, Dublin Core XML, BIBFRAME (JSON) 4 Violeta Ilik ALA PCC Participants Meeting, June 28, 2015 San Francisco
  5. 5. Catalogers and repository metadata Should feel like home! • Descriptive, administrative and structural metadata • Use of established controlled vocabularies • Use of institutional databases – campus directory • Use of established standards (date, language) Violeta Ilik ALA PCC Participants Meeting, June 28, 2015 San Francisco 5 Fedora/Sufia Repository record
  6. 6. OWL and RDF ”ontology is an explicit representation of conceptualization" Gruber, 1993 “RDF is a standard model for data interchange on the Web.” w3c.org/RDF/
  7. 7. Catalogers and repository metadata Should feel like home! • Descriptive, administrative and structural metadata • Use of established controlled vocabularies • Use of university databases – campus directory • Use of established standards (date, language) • Using ontologies Violeta Ilik ALA PCC Participants Meeting, June 28, 2015 San Francisco 7 Exis%ng  op%ons  for  resource  type   in  Fedora/Sufia  
  8. 8. Protégé - understanding ontologies According to Crofts, Le Boef, and Artur (2002): •  An ontology is a formal representation which aims to be precise, explicit and unambiguous •  The ontology describes the entities and concepts relevant to a particular domain •  An ontology embodies a particular view of a domain - the same domain may be described in different ways by different ontologies. Violeta Ilik ALA PCC Participants Meeting, June 28, 2015 San Francisco 8
  9. 9. Catalogers and repository metadata Should feel like home! • Descriptive, administrative and structural metadata • Use of established controlled vocabularies • Use of university databases – campus directory • Use of established standards (date, language) • Using ontologies • Creating RDF data Violeta Ilik ALA PCC Participants Meeting, June 28, 2015 San Francisco 9 Fedora/Sufia Repository record
  10. 10. RDF for VIVO: from CSV to RDF via Karma Violeta Ilik ALA PCC Participants Meeting, June 28, 2015 San Francisco 10
  11. 11. N-Triples – VIVO compliant data Violeta Ilik ALA PCC Participants Meeting, June 28, 2015 San Francisco 11
  12. 12. Library ecosystem 12 Violeta Ilik ALA PCC Participants Meeting, June 28, 2015 San Francisco We want to optimize discoverability and dissemination of content and enhance the impact of FSM, NUCATS, and our Northwestern Medicine community.
  13. 13. Other projects led by Digital Systems & Collection Services 13 Symplectic Elements •  Back-end bibliometric aggregator •  Support OA with repository integration •  Facilitates reports and reuse of clean aggregated data from a number of diverse sources Violeta Ilik ALA PCC Participants Meeting, June 28, 2015 San Francisco
  14. 14. Symplectic elements 14 Symplectic Elements data & platform Evaluation and reporting Digital Asset Management System (IR) Tasks (biosketches, etc.) Research Information Systems Connect outputs with grants for ROI Violeta Ilik ALA PCC Participants Meeting, June 28, 2015 San Francisco Holmes, K. 2015. Linked Data Love: research representation, discovery, and assessment. ALCTS/LITA Linked Library Data Interest Group. American Library Association Annual Meeting, San Francisco June 27, 2015
  15. 15. Modeling Symplectic elements’ publications data 15 Violeta Ilik ALA PCC Participants Meeting, June 28, 2015 San Francisco
  16. 16. Projects to which we contribute: VIVO @ Northwestern FSM 16 Violeta Ilik ALA PCC Participants Meeting, June 28, 2015 San Francisco
  17. 17. Projects to which we contribute: VIVO @ Northwestern FSM 17 Violeta Ilik ALA PCC Participants Meeting, June 28, 2015 San Francisco Data modeling using the Karma data integration tool R2RML (W3C standard) models that can be re- applied to same data source (CSV export files from data stores) Ontology development
  18. 18. What do MARC, OWL, and RDF have in common? •  They all need a cataloger, or in other words – one honest trouble maker 18 Violeta Ilik ALA PCC Participants Meeting, June 28, 2015 San Francisco
  19. 19. Northwestern University Feinberg School of Medicine Thank you Violeta Ilik vivoweb.org

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