Knowledge graphs (KGs) are huge collections of primarily encyclopedic facts. They are widely used in entity recognition, structured search, question answering, and other important tasks. Rule mining is commonly applied to discover patterns in KGs. However, unlike in traditional association rule mining, KGs provide a setting with a high degree of \emph{incompleteness}, which may result in the wrong estimation of the quality of mined rules, leading to erroneous beliefs such as all artists have won an award, or hockey players do not have children. In this paper we propose to use (in-)completeness meta-information to better assess the quality of rules learned from incomplete KGs. We introduce completeness-aware scoring functions for relational association rules. Moreover, we show how one can obtain (in-)completeness meta-data by learning rules about numerical patterns of KG edge counts. Experimental evaluation both on real and synthetic datasets shows that the proposed rule ranking approaches have remarkably higher accuracy than the state-of-the-art methods in uncovering missing facts.