# PAV ontology: Provenance, Authoring and Versioning

arXiv: https://arxiv.org/abs/1304.7224 · HTML: https://mlsanity.com/paper/1304.7224/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Apr 2013.

## Built on by (most cited first, 3 of 3)

- [PKG API: A Tool for Personal Knowledge Graph Management](https://mlsanity.com/paper/2402.07540/) (2024)
- [F(AI)2R: Who Did What, and Who Checked? Verifiable AI Provenance as an Executable Skill](https://mlsanity.com/paper/2607.25637/) (2026)
- [EBOCA: Evidences for BiOmedical Concepts Association Ontology](https://mlsanity.com/paper/2208.01093/) (2022)

## Abstract (arXiv)

Provenance is a critical ingredient for establishing trust of published scientific content. This is true whether we are considering a data set, a computational workflow, a peer-reviewed publication or a simple scientific claim with supportive evidence. Existing vocabularies such as DC Terms and the W3C PROV-O are domain-independent and general-purpose and they allow and encourage for extensions to cover more specific needs. We identify the specific need for identifying or distinguishing between the various roles assumed by agents manipulating digital artifacts, such as author, contributor and curator. We present the Provenance, Authoring and Versioning ontology (PAV): a lightweight ontology for capturing just enough descriptions essential for tracking the provenance, authoring and versioning of web resources. We argue that such descriptions are essential for digital scientific content. PAV distinguishes between contributors, authors and curators of content and creators of representations in addition to the provenance of originating resources that have been accessed, transformed and consumed. We explore five projects (and communities) that have adopted PAV illustrating their usage through concrete examples. Moreover, we present mappings that show how PAV extends the PROV-O ontology to support broader interoperability. The authors strived to keep PAV lightweight and compact by including only those terms that have demonstrated to be pragmatically useful in existing applications, and by recommending terms from existing ontologies when plausible. We analyze and compare PAV with related approaches, namely Provenance Vocabulary, DC Terms and BIBFRAME. We identify similarities and analyze their differences with PAV, outlining strengths and weaknesses of our proposed model. We specify SKOS mappings that align PAV with DC Terms.