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This paper revisits the classical problem of multi-query optimization in the context of RDF/SPARQL. We show that the techniques developed for relational and semi-structured data/query languages are hard, if not impossible, to be extended to account for RDF data model and graph query patterns expressed in SPARQL. In light of the NP-hardness of the multi-query optimization for SPARQL, we propose heuristic algorithms that partition the input batch of queries into groups such that each group of queries can be optimized together. An essential component of the optimization incorporates an efficient algorithm to discover the common sub-structures of multiple SPARQL queries and an effective cost model to compare candidate execution plans. Since our optimization techniques do not make any assumption about the underlying SPARQL query engine, they have the advantage of being portable across different RDF stores. The extensive experimental studies, performed on three popular RDF stores, show that the proposed techniques are effective, efficient and scalable.