Provenance data discovery through Semantic Web resources

Abstract

SummaryProviding historical information to deal with knowledge loss about a scientific experiment has been the focus of some several researches. However, computational support for large‐scale scientific experiments is still incipient and is considered one of e‐science's greatest challenges. In this vein, providing provenance information to a scientist is part of these challenges. Provenance information helps to assure the reliability and reproducibility of experiments. Therefore, this work has as its main objective to present a new ontology—Open Provenance Model Ontology‐e (OPMO‐e)—which is part of an architecture, named SciProvMiner. Open Provenance Model Ontology‐e is a new ontology that encompasses both prospective and retrospective provenance. SciProvMiner captures the provenance and implements all inference and completeness rules defined by Open Provenance Model to provide provenance information beyond those already established. We hypothesize that the capture and management of provenance data will provide scientists with implicit strategical information about the experiment through OPMO‐e. Case studies were carried out to evaluate OPMO‐e use considering SciProvMiner. The obtained results revealed that the use of the proposed ontology and SciProvMiner can enhance scientist's knowledge about an experiment.