Large (Linked) Open Data are increasingly shared as RDF graphs today. However, such data does not yet reach its full potential in terms of sharing and reuse. We provide new methods to meaningfully summarize data graphs, with a particular focus on RDF graphs. One class of tools for this task are structural RDF graph summaries, which allow users to grasp the different connections between RDF graph nodes. To this end, we introduce our novel RDFQuotient tool that finds compact yet informative RDF graph summaries that can serve as first-sight visualizations of an RDF graph’s structure. We also consider the problem of automatically identifying the k most interesting aggregate queries that can be evaluated on an RDF graph, given an integer k and a user-specified interestingness function. Aggregate queries are routinely used to learn insights from relational data warehouses, and some prior research has addressed the problem of automatically recommending interesting aggregate queries.