Abstract. Synthetic data are widely used for evaluation, testing, and experimentation. However, there is a lack of systems, tools and datasets that can be used for benchmarking in the context of evolution. In the case of RDF, generation of synthetic data that change through time must take into account evolving paradigms and characteristics that make sense, rather than arbitrary insertions and deletions of triples. In this paper, we discuss requirements for generation of synthetic evolving datasets by abstracting several characteristics of the process, and present EvoGen, a tool for evolving dataset generation that is based on the widely used Lehigh University Benchmark (LUBM) generator.