Abstract
With the popularity of knowledge graphs growing rapidly, large amounts of RDF graphs have been released, which raises the need for addressing the challenge of distributed subgraph matching queries. In this paper, we propose an efficient distributed method to answer subgraph matching queries on big RDF graphs using MapReduce. In our method, query graphs are decomposed into a set of stars that utilize the semantic and structural information embedded RDF graphs as heuristics. Two optimization techniques are proposed to further improve the efficiency of our algorithms. One algorithm, called RDF property filtering, filters out invalid input data to reduce intermediate results; the other is to improve the query performance by postponing the Cartesian product operations. The extensive experiments on both synthetic and real-world datasets show that our method outperforms the close competitors S2X and SHARD by an order of magnitude on average.
Highlights
IntroductionMore than one decade ago, the Semantic Web was proposed by Berners-Lee et al [3], which has become a series of W3C standards in order to realize the machine
More than one decade ago, the Semantic Web was proposed by Berners-Lee et al [3], which has become a series of W3C standards1 in order to realize the machineThe Resource Description Framework, a graph-based data model, is commonly used to represent and organize resources in knowledge graphs because of its flexibility
Our main contributions include: (1) we propose an efficient and scalable distributed algorithm based on star decomposition, called StarMR, for answering subgraph matching queries on Resource Description Framework (RDF) graphs; (2) two optimization strategies of StarMR are devised, one of which employing the properties in RDF graphs to filter out invalid input data in MapReduce iterations, the other postponing part of Cartesian product operations to the final step of MapReduce to reduce a part of unpromising Cartesian product operations; and (3) extensive experiments on both synthetic and real-world RDF graphs have been conducted to verify the efficiency and scalability of our method
Summary
More than one decade ago, the Semantic Web was proposed by Berners-Lee et al [3], which has become a series of W3C standards in order to realize the machine. Our main contributions include: (1) we propose an efficient and scalable distributed algorithm based on star decomposition, called StarMR, for answering subgraph matching queries on RDF graphs; (2) two optimization strategies of StarMR are devised, one of which employing the properties in RDF graphs to filter out invalid input data in MapReduce iterations, the other postponing part of Cartesian product operations to the final step of MapReduce to reduce a part of unpromising Cartesian product operations; and (3) extensive experiments on both synthetic and real-world RDF graphs have been conducted to verify the efficiency and scalability of our method.
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