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Communication Dans Un Congrès Année : 2016

Inferray: fast in-memory RDF inference

Julien Subercaze
Christophe Gravier
Jules Chevalier
Frédérique Laforest

Résumé

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 per- formance 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 in- ference; 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 competi- tors on RDFS-Plus, and up to several orders of magnitude for transitivity closure.
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Dates et versions

hal-01384368 , version 1 (19-10-2016)

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  • HAL Id : hal-01384368 , version 1

Citer

Julien Subercaze, Christophe Gravier, Jules Chevalier, Frédérique Laforest. Inferray: fast in-memory RDF inference: [This paper has been published in the VLDB 2016 conference]. BDA 2016, Nov 2016, Poitiers, France. ⟨hal-01384368⟩
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