Abstract We introduce kLog, a novel language for kernelbased learning on expressive logical and relational representations. kLog allows users to specify logical and relational learning problems declaratively. It builds on simple but powerful concepts: learning from interpretations, entity/relationship data modeling, and logic programming. Access by the kernel to the rich representation is mediated by a technique we call graphicalization: the relational representation is first transformed into a graph — in particular, a grounded entity/relationship diagram. Subsequently, a choice of graph kernel defines the feature space. The kLog framework can be applied to tackle the same range of tasks that has made statistical relational learning so popular, including classification, regression, multitask learning, and collective classification. An empirical evaluation shows that kLog can be either more accurate, or much faster at the same level of accuracy, than Tilde and Alchemy. kLog is GPLv3 licensed and is available at http://klog.dinfo.unifi.it along with tutorials.