MEET THE AUTHOR SERIES: Library Linked Data: From Proof of Concept to Action
January 12, 2017 @ 10:00 AM – 11:00 AM EST, U.S.A.
Modeling data in a form that the broader Web understands may raise the visibility of libraries where searches for information are now most likely to start. This is the context for describing OCLC’s contributions to the linked data cloud, which have produced data models and RDF datasets for several of the oldest, largest, and most widely referenced resources published by the library community, including WorldCat, WorldCat Works, VIAF, FAST, and the Dewey Decimal Classification. They are encoded in Schema.org, the vocabulary endorsed by the world’s major search engines for indexes and structured displays.
- The new Web is a growing “cloud” of interconnected resources that identify the people, places, things, and concepts that people want to know about when they approach the Internet with an information need. They also explain why linked data is an appropriate architecture for the description of library resources in the new Web.
- Linked Data is a term which describes an approach to exposing data in a machine-readable form where the data is “de-referenceable” (i.e. URIs are an integral part of the exposed data and external applications can use the URIs to perform various actions such as retrieving data, connecting same/similar/related data from multiple Linked Data stores).
- Linked Data is about communities agreeing on the semantics of their common data, adopting the naming patterns of other communities where their semantics agree and mapping/extending those vocabularies when necessary.
- Linked Data offers the potential for agencies and communities to publish information in a manner that permits far greater utility “in the flow” of the network. Unexpected connections, uses and value may be realized by many parties, including parties with which the hosting/publishing agency might not normally have had contact.
Our featured speaker is Carol Jean Godby, a Senior Research Scientist at OCLC, where she has been responsible for directing projects with a focus on automated content analysis that produce research prototypes, open source software, improvements to national and international standards, and enhancements to OCLC’s data architecture. She has a Ph.D. in Linguistics from The Ohio State University. Since 2010, she has been a member of a research and development team at OCLC whose charter is to develop a next-generation data architecture based on the principles of Linked Data.