In recent years, there has been an explosion of publicly available RDF and OWL web pages. Some of these pages are static text files, while others are dynamically generated from large knowledge bases. Typically, these pages are small, heterogeneous and prone to change frequently. Although centralized knowledge bases and/or triple stores can be used to collect and query large volumes of heterogeneous Semantic Web data, such systems will rapidly become stale when the sources are highly dynamic. An approach based on information integration can address this problem, but poses new questions with respect to how to effectively index the data sources. We propose to adapt a query reformulation algorithm and combine it with an information retrieval inspired index in order to select all sources relevant to a query. We treat each RDF document as a bag of URIs and literals and build an inverted index. Our system first reformulates the user's query into a set of subgoals and then translates the...