With the rapid growth of data science and machine learning, interactive notebooks have gained widespread adoption among scientists across all disciplines to publish their computational experiments containing code, text, and results. As it is easy to modify and re-run the computations in a notebook, it is important to know how the provenance of results changed in different executions over the course of time, thus enabling trust and reproducibility. In this paper, we present ProvBook, an extension of Jupyter Notebook to capture and view the provenance over the course of time. It also allows the user to share a notebook along with its provenance in RDF and also convert it back to a notebook. We use the REPRODUCE-ME ontology extended from PROV-O and P-Plan to describe the provenance of a notebook. This helps the scientists to compare their previous results with the current ones, check whether the experiments produce the results as expected and query the sequence of executions using SPARQL. The notebook data in RDF can be used in combination with the experiments that used them and help to get a track of the complete path of the scientific experiments.