Bridging the Scientific Knowledge Gap and Reproducibility: A Survey of Provenance, Assertion and Evidence Ontologies
Chhetri TR, Halchenko YO, Jarecka D, Trivedi P, Ghosh SS, Ray P, Ng L
Identifiers and access
- DOI
- 10.1145/3701716.3715483
- PubMed
- 40855889
- PMC
- PMC12376154
- Open-access copy →
Key findings
A survey of 23 ontologies — 13 for assertions and evidence and 10 for provenance — maps the current landscape of structured knowledge representation for scientific reproducibility and highlights gaps and opportunities for better-supported, machine-actionable scientific claims.
Abstract
Source: pubmed
The rapid growth of scientific publications and evolving experimental paradigms create significant challenges in staying up-to-date with current advances. Assertions are often unstructured and have limited provenance, which hinders reproducibility. Ontologies and knowledge graphs (KGs) offer structured solutions by capturing assertions, evidence, and provenance to support reproducibility. This paper reviews 23 ontologies - 13 focused on assertions and evidence and 10 on provenance - providing an overview of the current landscape while highlighting key challenges and opportunities for improvement.
Topics
- ml-nlp-knowledge
- reproducibility-tooling
Lab authors
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