$$\textsf {GQA}_{\textsf {RDF}}$$: A Graph-Based Approach Towards Efficient SPARQL Query Answering

Due to the increasing use of RDF data, efficient processing of SPARQL queries over RDF datasets has become an important issue. In graph-based RDF data management solution, SPARQL queries are translated into subgraph patterns and evaluated over RDF graphs via graph matching. However, answering SPARQL queries requires handing RDF reasoning to model implicit triples in RDF data, which is largely overlooked by existing graph-based solutions. In this paper, we investigate to equip graph-based solution with the important RDF reasoning feature for supporting SPARQL query answering. (1) We propose an on-demand saturation strategy, which only selects an RDF fragment that may be potentially affected by the query. (2) We provide a filtering-and-verification framework to efficiently compute the answers of a given query. The framework groups the equivalent entity vertices in the RDF graph to form semantic abstracted graph as index, and further computes the matches according to the multi-grade pruning supported by the index. (3) In addition, we show that the semantic abstracted graph and the graph saturation can be efficiently updated upon the changes to the data graph, enabling the framework to cope with dynamic RDF graphs. (4) Extensive experiments over real-life and synthetic datasets verify the effectiveness and efficiency of our approach.