A Storage-Independent Model for SPARQL-to-SQL Translation Algorithms

Article Preview

Abstract:

This paper proposes a storage-independent model for SPARQL-to-SQL translation algorithm based on relational view. During the development of Web ontology researches, the study for translation from SPARQL to SQL still remain issue. Previous researches focus on efficient and complete translation from SPARQL queries into equivalent SQL queries. However, these translation algorithms depend on specific structure of storages. When we modify the structure of storage, the translation algorithm also should be modified suitable changed structure of storage. This motivates us to study the issue of a model for independent use upon storage structures of an algorithm, which can then be guaranteed independency between translation algorithms and storages by generating relational view, and it improves application and usability of a translation algorithm. In addition, this paper show experiment result about accuracy and no data loss rate of query results on several storages.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

796-800

Citation:

Online since:

May 2015

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2015 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

* - Corresponding Author

[1] T. Berners-Lee, J. Hendler and O. Lassila, The Semantic Web, Scientific American, Vol. 284, No. 5, pp.34-43, (2001).

DOI: 10.1038/scientificamerican0501-34

Google Scholar

[2] The RDF Query Language (RQL), ICS-FORTH, (2008).

Google Scholar

[3] RDQL, http: /www. w3. org/Submission/2004/SUBM-RDQL-20040109.

Google Scholar

[4] SPARQL Query Language for RDF, http: /www. w3. org/TR/2006/WD-rdf-sparql-query-20061004.

DOI: 10.5220/0006091904500455

Google Scholar

[5] Jena Semantic Web Framework. http: /jena. sourceforge. net.

Google Scholar

[6] J. Broekstra, A. Kampman, and F. v. Harmelen. Sesame: A Generic Architecture for Storing and Querying RDF and RDF Schema. Lecture Notes in Computer Science Vol. 2342, pp.54-68 (2002).

DOI: 10.1007/3-540-48005-6_7

Google Scholar

[7] OWLJessKB. http: /edge. cs. drexel. edu/assemblies/software/owljesskb.

Google Scholar

[8] Z. Pan and J. Heflin, DLDB: Extending relational databases to support Semantic Web queries, In Workshop on Practical and Scalable Semantic Web Systems, 2nd International Semantic Web Conference (ISWC2003), Springer-Verlag, Lecture Notes in Computer Science, Vol. 2870, pp.109-113, (2003).

DOI: 10.21236/ada451847

Google Scholar

[9] A. Chebotko, S. Lu, and F. Fotouhi, Semantics Preserving SPARQL-to-SQL Translation, Data Knowl. Eng., Vol. 68, No. 10, pp.973-1000, (2009).

DOI: 10.1016/j.datak.2009.04.001

Google Scholar

[10] sparql2sql - a query engine for SPARQL over Jena triple stores, http: /jena. sourceforge. net.

Google Scholar

[11] S. Harris, SPARQL query processing with conventional relational database systems, Springer-Verlag, Lecture Notes in Computer Science, Vol. 3807, pp.235-244, (2005).

DOI: 10.1007/11581116_25

Google Scholar

[12] J. Son, J. D. Kim, D. K. Baik, Performance Evaluation of Storage-Independent Model for SPARQL-to-SQL Translation Algorithms, NTMS, pp.1-4, (2011).

DOI: 10.1109/ntms.2011.5720594

Google Scholar

[13] Y. Theoharis, V. Christophides, and G. Karvounarakis. Benchmarking database representations of RDF/S stores. Lecture Notes in Computer Science, vol. 3729, pp.685-701, (2005).

DOI: 10.1007/11574620_49

Google Scholar

[14] Y. Guo, Z. Pan, and J. Heflin, LUBM: A Benchmark for OWL Knowledge Base Systems, Journal of Web Semantics, Vol. 3, No. 2, pp.158-182, (2005).

DOI: 10.1016/j.websem.2005.06.005

Google Scholar