Torpedo: Improving the State-of-the-Art RDF Dataset Slicing


Marx, Edgard, Shekarpour, Saeedeh, Soru, Tommaso, Brasoveanu, Adrian M.P., Saleem, Muhammad, Baron, Ciro, Weichselbraun, Albert, Lehmann, Jens, Ngomo, Axel-Cyrille Ngonga and Auer, Sören. (2017). Torpedo: Improving the State-of-the-Art RDF Dataset Slicing. 11th International Conference on Semantic Computing (ICSC 2017), San Diego, California, USA


Over the last years, the amount of data published as Linked Data on the Web has grown enormously. In spite of the high availability of Linked Data, organizations still encounter an accessibility challenge while consuming it. This is mostly due to the mere size of some of the datasets published as Linked Data. The core observation behind this work is that a subset of these datasets suffices to address the needs of most organizations. In this paper, we introduce Torpedo, an approach for efficiently selecting and extracting relevant subsets from RDF datasets. In particular, Torpedo introduces optimization techniques as good ass the support of multi-join graph patterns and SPARQL FILTERs that enable to perform a more granular data selection. We compare the performance of our approach with existing solutions on nine different queries against four datasets. Our results show that our approach is highly scalable and is up to 26\% faster than the current state-of-the-art RDF dataset slicing approach.

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