PROV has been adopted by a number of workflow systems for encoding the traces of workflow executions. Exploiting these prove-nance traces is hampered by two main impediments. Firstly, workflow systems extend PROV differently to cater for system-specific constructs. The difference between the adopted PROV extensions yields heterogene-ity in the generated provenance traces. This heterogeneity diminishes the value of such traces, e.g. when combining and querying provenance traces of different workflow systems. Secondly, the provenance recorded by workflow systems tends to be large, and as such difficult to browse and understand by a human user. In this paper 4 , we propose SHARP, a Linked Data approach for harmonizing cross-workflow provenance. The harmonization is performed by chasing tuple-generating and equality-generating dependencies defined for workflow provenance. This results in a provenance graph that can be summarized using domain-specific vocabularies. We experimentally evaluate SHARP i) on publicly available provenance documents and ii) using a real-world omic experiment involving workflow traces generated by the Taverna and Galaxy systems.