Abstract. As the number of published scientific papers continuously increases, the need to assess paper impact becomes more valuable than ever. In this work, we focus on citation-based measures that try to estimate the popularity (current impact) of an article. State-of-the-art methods in this category calculate estimates of popularity based on paper citation data. However, with respect to recent publications, only limited data of this type are available, rendering these measures prone to inaccuracies. In this work, we present ArtSim, an approach that exploits paper similarity, calculated using scholarly knowledge graphs, to better estimate paper popularity for recently published papers. Our approach is designed to be applied on top of existing popularity measures, to improve their accuracy. We apply ArtSim on top of four well-known popularity measures and demonstrate through experiments its potential in improving their popularity estimates.