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In order to effectively and quickly answer queries in environments with distributed RDF/OWL, we present a query optimization algorithm to identify the potentially relevant Semantic Web data sources using structural query features and a term index. This algorithm is based on the observation that the join selectivity of a pair of query triple patterns is often higher than the overall selectivity of these two patterns treated independently. Given a rule goal tree that expresses the reformulation of a conjunctive query, our algorithm uses a bottom-up approach to estimate the selectivity of each node. It then prioritizes loading of selective nodes and uses the information from these sources to further constrain other nodes. Finally, we use an OWL reasoner to answer queries over the selected sources and their corresponding ontologies. We have evaluated our system using both a synthetic data set and a subset of the real-world Billion Triple Challenge data.

Using Reformulation Trees to Optimize Queries over Distributed Heterogeneous Sources