Semantic Provenance Graph for Reproducibility of Biomedical Research Studies: Generating and Analyzing Graph Structures from Published Literature.

Objective: To characterize the scientific reproducibility of biomedical research studies by query and analysis of semantic provenance graphs generated from provenance metadata terms extracted from PubMed articles.

Methods: We develop a new semantic provenance graph generation algorithm that uses a provenance ontology developed as part of the Provenance for Clinical and Health Research (ProvCaRe) project. The ProvCaRe project has processed and extracted provenance metadata from more than 1.6 million full text articles from the PubMed database.

Results: The semantic provenance graph generation algorithm is evaluated using provenance terms extracted from 75 selected articles describing sleep medicine research studies. In addition, we use eight provenance queries to evaluate the quality of semantic provenance graphs generated by the new algorithm.

Conclusion: The ProvCaRe project has created a unique resource to characterize the reproducibility of biomedical research studies and the semantic provenance graph generation algorithm enables users to effectively query and analyze the provenance metadata in the ProvCaRe knowledge repository.