The CALBC RDF Triple Store: retrieval over large literature content

TL;DR

This paper presents the CALBC RDF Triple Store, a large-scale, integrated database of biomedical literature and resources that enables complex querying across multiple bioinformatics datasets for research insights.

Contribution

The paper introduces a comprehensive RDF Triple Store combining annotated biomedical literature with bioinformatics resources, facilitating integrated querying and analysis.

Findings

Contains over 4.5 million triples from literature annotations

Integrates data from GeneAtlas, UniProtKb, and LexEBI

Enables querying of literature and bioinformatics data simultaneously

Abstract

Integration of the scientific literature into a biomedical research infrastructure requires the processing of the literature, identification of the contained named entities (NEs) and concepts, and to represent the content in a standardised way. The CALBC project partners (PPs) have produced a large-scale annotated biomedical corpus with four different semantic groups through the harmonisation of annotations from automatic text mining solutions (Silver Standard Corpus, SSC). The four semantic groups were chemical entities and drugs (CHED), genes and proteins (PRGE), diseases and disorders (DISO) and species (SPE). The content of the SSC has been fully integrated into RDF Triple Store (4,568,678 triples) and has been aligned with content from the GeneAtlas (182,840 triples), UniProtKb (12,552,239 triples for human) and the lexical resource LexEBI (BioLexicon). RDF Triple Store enables querying the scientific literature and bioinformatics resources at the same time for evidence of genetic causes, such as drug targets and disease involvement.