Bio2RDF: Towards a mashup to build bioinformatics knowledge systems
Journal of Biomedical Informatics
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Building an efficient SPAQRL query over the great variety of copious bio-medical Linked Open Data (LOD) requires users to understand the data schema, and makes it difficult for biologists to handle such data. To address this problem, we challenge to realise a SPARQL query builder that generates a structurally-optimised query by logically analysing the target RDF/OWL data; but still the corresponding unifying logic over RDF/OWL data needs to be implemented. An ontology-based smart SPARQL query builder named Bio-SPARQL is an implementation of the needed logic. Bio-SPARQL generates structurally-optimised queries over an ontologically classified RDF/OWL based bio-medical LOD by logically analysing their semantic graph structure. Bio-SPARQL employs our database named BioLOD having LOD data sets categorised in 744 classes with 7.88 million data items (instances) integrated public various types of omic databases by human curation and provides a set of LOD data files of each class. To aid in writing a query, it provides a graphical user interface that suggests possible data path schema and filters by analysing its corresponding ontological BioLOD data structure. The generated SPARQL query is designed to be performed in a user's local environment with its corresponding downloaded BioLOD data files in order to control the influence on query results due to data updates.