Inferray: fast in-memory RDF inference

Abstract : The advent of semantic data on the Web requires efficient reasoning systems to infer RDF and OWL data. The linked nature and the huge volume of data entail efficiency and scalability challenges when designing productive inference systems. This paper presents Inferray, an implementation of RDFS, ρDF, and RDFS-Plus inference with improved performance over existing solutions. The main features of Infer-ray are 1) a storage layout based on vertical partitioning that guarantees sequential access and efficient sort-merge join inference ; 2) efficient sorting of pairs of 64-bit integers using ad-hoc optimizations on MSD radix and a custom counting sort; 3) a dedicated temporary storage to perform efficient graph closure computation. Our measurements on synthetic and real-world datasets show improvements over competitors on RDFS-Plus, and up to several orders of magnitude for transitivity closure.
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Contributor : Julien Subercaze <>
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Julien Subercaze, Christophe Gravier, Jules Chevalier, Frederique Laforest. Inferray: fast in-memory RDF inference. VLDB, Sep 2016, New Delhi, India. ⟨hal-01245610⟩



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