Abstract: We consider the setting of a Semantic Web database, containing both explicit data encoded in RDF triples, and implicit data, im- plied by the RDF semantics. Based on a query workload, we ad- dress the problem of selecting a set of views to be materialized in the database, minimizing a combination of query processing, view storage, and view maintenance costs. Starting from an existing rela- tional view selection method, we devise new algorithms for recom- mending view sets, and show that they scale significantly beyond the existing relational ones when adapted to the RDF context. To account for implicit triples in query answers, we propose a novel RDF query reformulation algorithm and an innovative way of in- corporating it into view selection in order to avoid a combinatorial explosion in the complexity of the selection process. The interest of our techniques is demonstrated through a set of experiments.