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SRSIS OntologyBecause no existing ontology supports the range of entities and relationship that SRSIS will encompass, we will use the Protégé ontology editor to develop a SRSIS ontology framework that reuses appropriate parts of currently available ontologies while introducing extensions and additions where necessary. The framework will be based on and remain compatible with the existing VIVO and emerging research dataset and research resource ontology work. It will be sufficiently expressive to encompass traditional catalog metadata from both Cornell and Harvard, the basic linked data elements described in the Stanford Linked Data Workshop Technology Plan, and the usage and other contextual elements from StackLife. The ontology will capture a series of basic concepts and be structured as modules that draw inspiration from and reuse existing ontology classes and properties where appropriate, such as the Semantic Publishing and Referencing ontologies, and that also support arbitrary system-wide refinement, including local extensions. |
Ontology team activities to date
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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.
- W3C Open Annotation Data Model
- Open Archives Initiative Object Reuse and Exchange
- LD4L Use Case 1.1 - Triples Examples
- LD4L Use Case 1.2 - Triples Examples
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.
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