Approaches for Efficient Query Optimization Using Semantic Web Technologies

Abstract

Query optimization system proposes an answer-driven approach to information access. Most of the query optimization system aims for information retrieval required by natural language queries. Queries are generally asked within a context, and answers are provided within that specific context. RDF is a general proposition language for the Web, joining data from diverse resources. SPARQL, a query language for RDF, can join data from different databanks, as well as papers, inference engines, or anything else that may reveal its expertise as a guided classified chart. Because of lack of proper architectural circulation, the existing SPARQL-to-SQL translation techniques have actually trimmed a lot of restrictions that decrease their toughness, effectiveness, and reliability. These constraints include the generation of ineffective or perhaps incorrect SQL inquiries, lack of official history, and bad applications. This paper recommended a structure which made use of by an ontology-based moderator system to provide the well-defined semantical design, which (i) supplies a distinct SPARQL semantics used to rewrite the question in SQL; (ii) ontology-based expertise is created for rapid accessibility as well as equate question revising SPARQL to SQL for reliable information retrieval in semantic Internet data of big dataset; (iii) hybrid query optimization framework is proposed for query handling technique for the effective access of customized details on the semantic Internet making use of bundled ontology expertise and also inference engine.