gStore: answering SPARQL queries via subgraph matching

  • Authors:

  • Lei Zou;Jinghui Mo;Lei Chen;M. Tamer Özsu;Dongyan Zhao

  • Affiliations:

  • Peking University, China;Peking University, China;Hong Kong University of Science and Technology, China;University of Waterloo, Canada;Peking University, China and Key Laboratory of Computational Linguistics (PKU), Ministry of Education, China

  • Venue:
  • Proceedings of the VLDB Endowment
  • Year:
  • 2011

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Abstract

Due to the increasing use of RDF data, efficient processing of SPARQL queries over RDF datasets has become an important issue. However, existing solutions suffer from two limitations: 1) they cannot answer SPARQL queries with wildcards in a scalable manner; and 2) they cannot handle frequent updates in RDF repositories efficiently. Thus, most of them have to reprocess the dataset from scratch. In this paper, we propose a graph-based approach to store and query RDF data. Rather than mapping RDF triples into a relational database as most existing methods do, we store RDF data as a large graph. A SPARQL query is then converted into a corresponding subgraph matching query. In order to speed up query processing, we develop a novel index, together with some effective pruning rules and efficient search algorithms. Our method can answer exact SPARQL queries and queries with wildcards in a uniform manner. We also propose an effective maintenance algorithm to handle online updates over RDF repositories. Extensive experiments confirm the efficiency and effectiveness of our solution.