Inductive Learning-Based SPARQL Query Optimization

Abstract

In any query optimization, the goal is to find the execution plan which is expected to return the result set without actually executing the query or subparts with optimal cost. The users are not expected to write their queries in such a way so that they can be processed efficiently; rather it is expected from system to construct a query evaluation plan that minimizes the cost of query evaluation. SPARQL is used for querying the ontologies, and thus, we need to implement SPARQL query optimization algorithms in semantic query engines, to ensure that query results are delivered within reasonable time. In this paper, we proposed an approach in which the learning is triggered by user queries. Then, the system uses an inductive learning algorithm to generate semantic rules. This inductive learning algorithm can automatically select useful join paths and properties to construct rules from an ontology with many concepts. The learned semantic rules are effective for optimization of SPARQL query because they match query patterns and reflect data regularities.