Executing queries on incomplete, sparse knowledge graphs yields incomplete results, especially when it comes to queries involving traversals. In this paper, we question the applicability of all known architectures for incomplete knowledge bases and propose ORB: a clear departure from existing system designs, relying on Machine Learning-based operators to provide inferred query results. At the same time, ORB addresses peculiarities inherent to knowledge graphs, such as schema evolution, dynamism, scalability, as well as high query complexity via the use of embedding-driven inference. Through ORB, we stress that approximating complex processing tasks is not only desirable but also imperative for knowledge graphs.