Optimizing RDF Chain Queries using Genetic Algorithms

In an Electronic Commerce environment, Semantic Web technologies are promising enablers for large-scale knowledge-based systems, as they facilitate machine-interpretability of data through effective data representation. Fast query engines are required for efficient real-time querying of large amounts of data, usually represented using the Resource Description Framework (RDF). An RDF model is a collection of RDF facts declared as a collection of triples, each of which consists of a subject, a predicate, and an object. These triples can be visualized using an RDF graph, which is a node and directed-arc diagram, in which each triple is represented as a node-arc-node link. RDF sources can be queried using SPARQL. The execution time of a query depends on the order in which parts of the query paths are executed. The query optimization challenge addressed here is to determine the right join order, hereby optimizing the overall response time.