Indexing Linked Bibliographic Data with JSON-LD, BibJSON and Elasticsearch

Linked Data is a powerful tool for sharing bibliographic metadata. By combining the decentralization of the web with the use of globally defined metadata vocabularies, data from many sources can be treated as a single, aggregated graph. Supporting search across these distributed data sources within the same application, however, requires considerable work in vocabulary alignment and data transformation. Aggregate systems must convert data into a unified model which must (almost inevitably) be generic at the expense of the structure and granularity of the original data. This paper presents a novel solution for representing and indexing bibliographic resources that retains the data integrity and extensibility of Linked Data while supporting fast, customizable indexes in an application-friendly data format. The methodology makes use of JSON-LD to represent RDF graphs in JSON suitable for indexing with Elasticsearch. BibJSON is used as a common index format capable of handling a wide range of library resources. Since all three technologies (RDF/JSON-LD, BibJSON and Elasticsearch) share an emphasis on extensibility, it is possible to create an index of bibliographic data that is both generalized and flexible enough to handle Linked Data from multiple sources.