  We introduce Kensho, a tool for generating mapping rules between two
Knowledge Bases (KBs). To create the mapping rules, Kensho starts with a set of
correspondences and enriches them with additional semantic information
automatically identified from the structure and constraints of the KBs. Our
approach works in two phases. In the first phase, semantic associations between
resources of each KB are captured. In the second phase, mapping rules are
generated by interpreting the correspondences in a way that respects the
discovered semantic associations among elements of each KB. Kensho's mapping
rules are expressed using SPARQL queries and can be used directly to exchange
knowledge from source to target. Kensho is able to automatically rank the
generated mapping rules using a set of heuristics. We present an experimental
evaluation of Kensho and assess our mapping generation and ranking strategies
using more than 50 synthesized and real world settings, chosen to showcase some
of the most important applications of knowledge translation. In addition, we
use three existing benchmarks to demonstrate Kensho's ability to deal with
different mapping scenarios.
