As more data (especially scientific data) is digitized and put on the Web, it is desirable to make provenance metadata easy to access, reuse, integrate and reason over. Ontologies can be used to encode expectations and agreements concerning provenance metadata representation and computation. This paper analyzes a selection of popular Semantic Web provenance ontologies such as the Open Provenance Model (OPM), Dublin Core (DC) and the Proof Markup Language (PML). Selected initial findings are reported in this paper: (i) concept coverage analysis – we analyze the coverage, similarities and differences among primitive concepts from different provenance ontologies, based on identified themes; and (ii) concept modeling analysis – we analyze how Semantic Web language features were used to support computational provenance semantics. We expect the outcome of this work to provide guidance for understanding, aligning and evolving existing provenance ontologies.