Approaches for Efficient Query Optimization Using Semantic Web Technologies
- 32 Downloads
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.
KeywordsQuery optimization system RDF SPARQL Hybrid Ontology Query processing
- 1.Akahani J, Hiramatsu K, Satoh T (2003) Approximate query reformulation for multiple ontologies in the semantic web. NTT Tech Rev 1(2):83–87Google Scholar
- 2.Benslimane SM, Merazi A, Malki M, Bensaber DA (2008) Ontology mapping for querying heterogeneous information sources. INFOCOMP (J Comput Sci)Google Scholar
- 3.Imprialou M, Stoilos G, Grau BC (2012) Benchmarking ontology-based query rewriting systems. In: Twenty-sixth AAAI conference artificial intelligence, pp 779–785Google Scholar
- 4.Bikakis N, Gioldasis N, Tsinaraki C, Christodoulakis S (2009) Querying XML data with SPARQL. In: Proceedings of the 20th international conference on database and expert systems applications, pp 372–381Google Scholar
- 5.Cyganiak R (2005) A relational algebra for SPARQL. Technical Report http://www.hpl.hp.com/techreports/2005/HPL-2005-170.html, HP Laboratories Bristol; Calvanese D, De Giacomo G, Lenzerini M, Vardi MY (2000) What is query rewriting?
- 7.Khattak M, Batool R, Pervez Z, Khan AM, Lee S (2013) Ontology evolution and challenges. J Inf Sci Eng 29(5):851–871Google Scholar
- 10.Bouquet P, Giunchiglia F, van Harmelen F, Serafini L, Stuckenschmidt H (2003) C-OWL: contextualizing ontologies. The SemanticWeb—ISWC 2003:164–179Google Scholar
- 12.Chebotko A, Lu S, Jamil HM, Fotouhi F (2007) Semantics preserving SPARQL-to-SQL query translation for optional graph patterns. Technical report, Wayne State University, Department of Computer ScienceGoogle Scholar
- 13.Gruber BT (1993) What is an ontology ?, pp 1–11Google Scholar