Extending Nanopublications with Knowledge Provenance for Multi-Source Scientific Assertions

Nanopublications are RDF graphs that enable the possibility of sharing machine-readable assertions on the Web while tracking their provenance and publication information. However, the current nanopublication model focuses on the provenance of single-source assertions derived from a specific publication or database. This work proposes extending the nanopublication model to include a fourth component called knowledge provenance. Knowledge provenance captures the context where an assertion is not derived from a single publication but from a body of knowledge that can comprehend supporting and conflicting pieces of evidence that we need to track and refer to. We apply the defined model to the facts generated by the Collaborative Oriented Relation Extraction (CORE) and published 197, 511 assertions in the form of extended nanopublications, allowing the identification, representation, access, and citation of individual gene expression-cancer associations.