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Several levels of complexity may legitimately exist in parallel and be utilized based on the availability of data or the goals of an application. This choice can be seen in the pairing of PROV-O ontology where direct object properties have been paired with more complex options involving intermediate nodes that add additional temporal or role information. The related PAV (Provenance, Attribution, and Versioning) ontology offers a simpler set of classes and properties sufficient for many applications requiring only simple attribution. Application software can also often mask a more complex underlying data model, and in many cases it may be preferable in production contexts to separate logging and provenance information from user-facing applications entirely.
Working with non-MARC metadata
While our library catalogs are very likely the largest single sources of metadata, each partner university maintains a large number of digital collections representing a diversity of subject domains, size, and complexity. Several of our use cases involve connecting catalog data with these non-MARC sources, not only to provide a more unified search interface, but to be able to interconnect and cross-references sources that for now remain almost entirely separate. The benefits go both ways, and the addition of sources outside the traditional library domain brings in yet more possibilities for value-added services enhanced by entity recognition and external links. Prime examples of these non-library sources are Stanford's CAP, Harvard's Faculty Finder, and Cornell's VIVO.
Work to date
The ontology team
Annotations and virtual collections
The first two use cases address user tagging and the ability of librarians or others to curate potentially very large collections of library resources through annotations external but linked to the bibliographic metadata. Existing ontologies were identified that support annotations and the assembly and ordering of individual resources into collections.
Usage data
The fifth group of use cases explore including usage data to supplement library discovery interfaces and to inform collection review and additions. Here the team first explored a very granular model for capturing usage information from circulation-related events and other direct user interactions with library resources. On further investigation, however, this data proved not only to be difficult to come by but fraught with concerns about privacy, even when stripped of any directly identifying information. Later discussions have focused on the compilation and use of a simple stack score as a measure potentially more comparable across institutions despite differences in size, discipline, population makeup, and other factors.
References
While by no means exhaustive, the team has found these papers useful.
- The Relationship between BIBFRAME and the OCLC's Linked-Data Model of Bibliographic Description: A Working Paper. Carol Jean Godby, Senior Researc Research Scientist, OCLC Research, September, 2013. PDF