Authors : Jason A. Clark, Scott W. H. Young
One of our greatest library resources is people. Most libraries have staff directory information published on the web, yet most of this data is trapped in local silos, PDFs, or unstructured HTML markup.
With this in mind, the library informatics team at Montana State University (MSU) Library set a goal of remaking our people pages by connecting the local staff database to the Linked Open Data (LOD) cloud.
In pursuing linked data integration for library staff profiles, we have realized two primary use cases: improving the search engine optimization (SEO) for people pages and creating network graph visualizations.
In this article, we will focus on the code to build this library graph model as well as the linked data workflows and ontology expressions developed to support it. Existing linked data work has largely centered around machine-actionable data and improvements for bots or intelligent software agents.
Our work demonstrates that connecting your staff directory to the LOD cloud can reveal relationships among people in dynamic ways, thereby raising staff visibility and bringing an increased level of understanding and collaboration potential for one of our primary assets: the people that make the library happen.