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

Tracking #: 2144-3357

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Zeynep Banu Ozger
Nurgul Yuzbasioglu Uslu

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Axel Polleres

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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. SPARQL is the standard query language for Semantic Web data and the Resource Description Framework (RDF) is the widely used data graph format for queries. The challenge is finding the optimal query order that processes in a short period of time. In this paper, the discrete Artificial Bee Colony (dABC SPARQL) algorithm was proposed, based a novel heuristic approach for re-ordering SPARQL queries. The processing times of queries with different shapes and sizes were minimised using the dABC SPARQL algorithm. The performance of the proposed method was evaluated on chain, star, cyclic, and chain-star queries of different sizes from the Lehigh University Benchmark (LUBM) dataset. The results were compared to ARQ query engine, the Ant System, the Elitist Ant System, and MAX-MIN Ant System Algorithms. The experiments demonstrated that the proposed method significantly reduced the processing time, and in most queries the reduction rate was higher than that of other methods.
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