Abstract
Resource Description Framework (RDF) is widely used for representing biomedical data in practical applications. With the increases of RDF-based applications, there is an emerging requirement of novel architectures to provide effective supports for the future RDF data explosion. Inspired by the success of the new designs in National Center for Biotechnology Information dbSNP (The Single Nucleotide Polymorphism Database) for managing the increasing data volumes using JSON (JavaScript Object Notation), in this paper we present an effective mapping tool that allows data migrations from RDF to JSON for supporting future massive data explosions and releases. We firstly introduce a set of mapping rules, which transform an RDF format into the JSON format, and then present the corresponding transformation algorithm. On this basis, we develop an effective and user-friendly tool called RDF2JSON, which enables automating the process of RDF data extractions and the corresponding JSON data generations.
Highlights
With the increasing adoption of semantic web technologies [22,23,24,25] and formalisms in biomedical and biomolecular areas, many popular database applications provide accessible data represented in a Resource Description Framework (RDF) format [10, 13, 27]
In order to test the performance of RDF2JSON, two realworld data sets UniprotKB and BioModels containing RDF data sources are chosen in our experiments
As the RDF is an extension of XML and it is a complete markup language, it uses redundant tags for the content descriptions, which may result in redundant storage
Summary
With the increasing adoption of semantic web technologies [22,23,24,25] and formalisms in biomedical and biomolecular areas, many popular database applications (such as Uniprot [36], Ensembl [9], BioModels [19], etc.) provide accessible data represented in a Resource Description Framework (RDF) format [10, 13, 27]. As the World Wide Web Consortium (W3C) recommended standard, the graph-based RDF model is well suitable for explicitly publishing life science data and linking the diverse data resources [5, 7, 11, 28]. To effectively publish the cross-reference information about diseases and abnormal states extracted from disease ontology and abnormality ontology, Disease Compass [17] linked the causal chains of diseases by using the RDF model.
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