Tentris - A Tensor-Based Triple Store

被引：9

作者：

Bigerl, Alexander ^{[1
]}

Conrads, Felix ^{[1
]}

Behning, Charlotte ^{[2
]}

Sherif, Mohamed Ahmed ^{[1
]}

Saleem, Muhammad ^{[3
]}

Ngomo, Axel-Cyrille Ngonga ^{[1
]}

机构：

[1] Paderborn Univ, CS Dept, DICE Grp, Paderborn, Germany

[2] Univ Hosp Bonn, Dept Med Biometry Informat & Epidemiol, Bonn, Germany

[3] Univ Leipzig, CS Dept, Leipzig, Germany

来源：

SEMANTIC WEB - ISWC 2020, PT I | 2020年 / 12506卷

基金：

欧盟地平线“2020”;

关键词：

D O I：

10.1007/978-3-030-62419-4_4

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The number and size of RDF knowledge graphs grows continuously. Efficient storage solutions for these graphs are indispensable for their use in real applications. We present such a storage solution dubbed TENTRIS. Our solution represents RDF knowledge graphs as sparse order-3 tensors using a novel data structure, which we dub hypertrie. It then uses tensor algebra to carry out SPARQL queries by mapping SPARQL operations to Einstein summation. By being able to compute Einstein summations efficiently, TENTRIS outperforms the commercial and open-source RDF storage solutions evaluated in our experiments by at least 1.8 times with respect to the average number of queries it can serve per second on three datasets of up to 1 billion triples. Our code, evaluation setup, results, supplementary material and the datasets are provided at https://tentris.dice-research.org/iswc2020.

引用

页码：56 / 73

页数：18

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