In many data analysis applications, there is a need to explain why a surprising or interesting result was produced by a query. Previous approaches to explaining results have directly or indirectly used data provenance (input tuples contributing to the result(s) of interest), which is limited by the fact that relevant information for explaining an answer may not be fully contained in the provenance. We propose a new approach for explaining query results by augmenting provenance with information from other related tables in the database. We develop a suite of optimization techniques, and demonstrate experimentally using real datasets and through a user study that our approach produces meaningful results by efficiently navigating the large search space of possible explanations.