Exposing Provenance Metadata Using Different RDF Models

TL;DR

This paper evaluates different RDF models for exposing provenance metadata in life sciences, analyzing redundancy, efficiency, and query performance across multiple RDF stores to improve scientific data interoperability.

Contribution

It compares the suitability of N-ary, Singleton Property, and Nanopublication RDF models for large, redundant life science provenance data, considering performance and storage efficiency.

Findings

Query performance varies with RDF store and model.

Provenance redundancy is significant in life sciences data.

Model choice impacts querying efficiency.

Abstract

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.