Ant colony optimization for RDF chain queries for decision support

• Published in: Expert systems with applications Vol. 40; no. 5; pp. 1555 - 1563
• Main Authors: Hogenboom, Alexander, Frasincar, Flavius, Kaymak, Uzay
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier Ltd 01.04.2013; Elsevier
• Subjects: Ant colony optimization; Applied sciences; Artificial intelligence; Chains; Computer science; control theory; systems; Computer systems and distributed systems. User interface; Decision theory. Utility theory; Detection, estimation, filtering, equalization, prediction; Engines; Exact sciences and technology; Gallium; Genetic algorithm; Genetic algorithms; Information, signal and communications theory; Iterative improvement; Mathematical models; Operational research and scientific management; Operational research. Management science; Queries; RDF chain query optimization; Semantics; Signal and communications theory; Signal, noise; Simulated annealing; Software; Speech and sound recognition and synthesis. Linguistics; Telecommunications and information theory; Ant colony optimization; Iterative improvement; RDF chain query optimization; Genetic algorithm; Simulated annealing; Artificial life; Database query; Query; Expert system; Metadata; Partial ordering; Iterative method; Resource description framework; Semantic web; Swarm intelligence; Execution time; Decision support system; Decision making; Regression analysis; Soft computing; Fourier series; Real time; Intertemporal choice; Particle swarm optimization; Experimental result
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2012.08.074

► We propose to optimize RDF chain query join order through ant colony optimization. ► We benchmark against existing two-phase optimization and genetic algorithm methods. ► For up to 15-join queries, our method excels in execution time and solution quality. ► A genetic algorithm executes faster for more joins, yet we excel in solution quality. Semantic Web technologies can be utilized in expert systems for decision support, allowing a user to explore in the decision making process numerous interconnected sources of data, commonly represented by means of the Resource Description Framework (RDF). In order to disclose the ever-growing amount of widely distributed RDF data to demanding users in real-time environments, fast RDF query engines are of paramount importance. A crucial task of such engines is to optimize the order in which partial results of a query are joined. Several soft computing techniques have already been proposed to address this problem, i.e., two-phase optimization (2PO) and a genetic algorithm (GA). We propose an alternative approach – an ant colony optimization (ACO) algorithm, which may be more suitable for a Semantic Web environment. Experimental results with respect to the optimization of RDF chain queries on a large RDF data source demonstrate that our approach outperforms both 2PO and a GA in terms of execution time and solution quality for queries consisting of up to 15 joins. For larger queries, both ACO and a GA may be preferable over 2PO, subject to a trade-off between execution time and solution quality. The GA yields relatively good solutions in a comparably short time frame, whereas ACO needs more time to converge to high-quality solutions.