An Effective Discrete Artificial Bee Colony Based SPARQL Query Path Optimization by Reordering Triples

Abstract

Semantic Web has emerged to make web content machine-readable, and with the rapid increase in the number of web pages, its importance has increased. Resource description framework (RDF) is a special data graph format where Semantic Web data are stored and it can be queried by SPARQL query language. The challenge is to find the optimal query order that results in the shortest period of time. In this paper, the discrete Artificial Bee Colony (dABC(SPARQL)) algorithm is proposed, based on a novel heuristic approach, namely reordering SPARQL queries. The processing time of queries with different shapes and sizes is minimized using the dABC(SPARQL) algorithm. The performance of the proposed method is evaluated on chain, star, cyclic, and chain-star queries of different sizes from the Lehigh University Benchmark (LUBM) dataset. The results obtained by the proposed method are compared with those of ARQ (a SPARQL processor for Jena) query engine, the Ant System, the Elitist Ant System, and MAX-MIN Ant System algorithms. The experiments demonstrate that the proposed method significantly reduces the processing time, and in most queries, the reduction rate is higher compared with other optimization methods.