A standard model for exposing structured provenance metadata of scientific
assertions on the Semantic Web would increase interoperability,
discoverability, reliability, as well as reproducibility for scientific
discourse and evidence-based knowledge discovery. Several Resource Description
Framework (RDF) models have been proposed to track provenance. However,
provenance metadata may not only be verbose, but also significantly redundant.
Therefore, an appropriate RDF provenance model should be efficient for
publishing, querying, and reasoning over Linked Data. In the present work, we
have collected millions of pairwise relations between chemicals, genes, and
diseases from multiple data sources, and demonstrated the extent of redundancy
of provenance information in the life science domain. We also evaluated the
suitability of several RDF provenance models for this crowdsourced data set,
including the N-ary model, the Singleton Property model, and the
Nanopublication model. We examined query performance against three commonly
used large RDF stores, including Virtuoso, Stardog, and Blazegraph. Our
experiments demonstrate that query performance depends on both RDF store as
well as the RDF provenance model
