Library Linked Data in the Cloud: OCLC's Experiments with New Models of Resource Description (Synthesis Lectures on the Semantic Web: Theory and Technology)

This book describes OCLC’s contributions to the transformation of the Internet from a web of documents to a Web of Data. The new Web is a growing ‘cloud’ of interconnected resources that identify the things people want to know about when they approach the Internet with an information need.The linked data architecture has achieved critical mass just as it has become clear that library standards for resource description are nearing obsolescence. Working for the world’s largest library cooperative, OCLC researchers have been active participants in the development of next generation standards for library resource description. By engaging with an international community of library and Web standards experts, they have published some of the most widely used RDF datasets representing library collections and librarianship.This book focuses on the conceptual and technical challenges involved in publishing linked data derived from traditional library metadata. This transformation is a high priority because most searches for information start not in the library, nor even in a Web-accessible library catalog, but elsewhere on the Internet. Modeling data in a form that the broader Web understands will project the value of libraries into the Digital Information Age.The exposition is aimed at librarians, archivists, computer scientists, and other professionals interested in modeling bibliographic descriptions as linked data. It aims to achieve a balanced treatment of theory, technical detail, and practical application.

Carol Jean Godby is a Senior Research Scientist at OCLC, where she has directed projects with a focus on automated content analysis that produce research prototypes, open source software, improvements to national and international standards, and enhancements to OCLCâs products, services, and data architecture. She has a Ph.D. in linguistics from Ohio State University. Her work on mapping library standards for bibliographic description is widely known to librarians and publishers. Since 2012, she has been a leader of a cross-division team at OCLC whose charter is to develop a next-generation data architecture based on the principles of linked data. Shenghui Wang is a Research Scientist at the OCLC EMEA office in Leiden, The Netherlands. Her current research activities include text and data mining as well as Linked Data investigations. She received a Ph.D. in Computer Science from the University of Manchester in 2007. Shenghui has been conducting research in the broad field of Artificial Intelligence with interests in cognitive modeling, knowledge representation and reasoning, natural language semantics, and machine learning. Before joining OCLC Research in 2012, Shenghui was a researcher at the Free University of Amsterdam and Wageningen University, exploring Semantic Web and language technologies to improve the semantic interoperability in the domain of cultural heritage and agrifood research. Jeffrey K. Mixter is a recent graduate of Kent State University, having earned an M.L.I.S. (Masters of Library and Information Science) and an M.S. degree in Information Architecture and Knowledge Management. His master's thesis demonstrated how to convert an existing flat data model into a detailed ontology that is interoperable with search engine aggregating services. As a Research Assistant at OCLC, Jeff worked with Dr. Ed O'Neill in developing the OCLC FAST controlled vocabulary. He is now working as a Software Engineer at OCLC with collaborators from Montana State University on the IMLS-funded project 'Measuring Up: Assessing Accuracy of Reported Use and Impact of Digital Repositories.' Kenning Arlitsch, Dean of Libraries at Montana State, is the principal investigator. Jeff's role in the project is to serve as a data modeling expert, taking the lead in the development of an ontology for modeling items found in institutional repositories and digital collections in a form that can be discovered and indexed by Google and other major search engines.