Bio2RDF

Bio2RDF is a large linked-data knowledge graph for the life sciences. It takes dozens of separate biological and biomedical databases, each with its own schema and quirks, and converts them into a single shared format called RDF, which expresses every fact as a subject-predicate-object statement known as a triple. The combined graph reaches roughly 11 billion triples across 35 datasets, among them DrugBank for drugs, PubMed for the biomedical literature, and MeSH for medical subject headings. Because everything shares one format and a consistent scheme of identifiers, you can follow links across sources that were never designed to talk to each other, from a drug to its targets to the papers that mention them.

You query it with SPARQL, the standard query language for this kind of graph, through the project's SPARQL endpoints, and you can also download the RDF dumps to load into your own triplestore for heavier or offline work. For RAG, the usual pattern is to run structured SPARQL queries to pull precise facts and relationships, then turn those results into short natural-language passages you feed to the model, rather than embedding the raw triples directly.

This makes Bio2RDF best for questions that need connected, factual precision: entity lookups, and relationships between drugs, genes, diseases, and proteins, grounded in named biomedical identifiers. It complements free-text sources like PubMed abstracts rather than replacing them; the graph gives you the structure, the literature gives you the prose.

Two caveats. The licence varies by the underlying source dataset, so there is no single blanket permission covering the whole graph: check the terms of each dataset you rely on before shipping a commercial product. And some Bio2RDF releases lag the current versions of their source databases, so confirm you are working from a recent enough build for anything where freshness matters.