2022 LD4 Conference on Linked Data

The complexity of MARC, combined with the multitude of cataloging practices, makes indexing and querying MARC data challenging. As libraries move to linked data structures, the same linked data approaches can also be employed for analyzing MARC data in ways that are more efficient than is typical with other methods.

RDF triplestores cannot index MARC directly but a literal conversion to RDF is a swift and direct way to retain MARC data values. By indexing MARC data in a graph-based index we can accommodate the sheer number of fields, subfields, indicators and their respective combinations that are possible across a set of MARC records. This transformation to RDF is lossless while other common linked data ontologies used by libraries, such as BIBFRAME, have a measurable degree of loss when transforming to RDF. An RDF view of the data could also be useful for improving quality by ensuring consistency across related records as well as facilitate conversion to other ontologies.

This talk will demonstrate a literal transformation of MARC/XML into RDF from a test set of MARCXML records and demonstrate example SPARQL queries of the MARC data.