One of the pillars of the scientific method is reproducibility – the ability to replicate the results of a prior study if the same procedures are followed. A lack of reproducibility can lead to wasted resources, false conclusions, and a loss of public trust in science. Ensuring reproducibility is challenging due to the heterogeneity of the methods used in different fields of science. In this article, we present an approach for increasing the reproducibility of research results, by semantically describing and interlinking relevant artifacts such as data, software scripts or simulations in a knowledge graph. In order to ensure the flexibility to adapt the approach to different fields of science, we devise a template model, which allows defining typical descriptions required to increase reproducibility of a certain type of study. We provide a scoring model for gradually assessing the reproducibility of a certain study based on the templates and provide a knowledge graph infrastructure for curating reproducibility descriptions along with semantic research contribution descriptions. We demonstrate the feasibility of our approach with an example in data science.