Sparql query processing in relational databases

  • Authors

    • Ju Ri Kim
    • Zhanfang Zhao
    • Sung Kook Han
    2018-06-08
    https://doi.org/10.14419/ijet.v7i2.33.13860
  • SPARQL, RDF, Triple, Linked Open Data, Schema Mapping.

  • Background/Objectives: The mapping RDB to RDF has become important to populate Linked Data more efficiently. This paper shows how to implement SPARQL endpoint in RDB using a conceptual level mapping approach.

    Methods/Statistical analysis: Many diverse approaches and related languages for mapping RDB to RDF have been proposed. The prominent achievements of mapping RDB to RDF are two standard draft Direct Mapping and R2RML proposed by W3C RDB2RDF Working Group. This paper analyzes these conventional mapping approaches and proposes a new approach based on schema mapping. The paper also presents SPARQL query processing in RDB.

    Findings: There are distinct differences between instance level mapping and conceptual level mapping for RDB2RDF. Data redundancy of instance level mapping causes many inevitable problems during mapping procedure. The conceptual level mapping can provide straightforward and efficient way. The ER model in RDB and RDF model in Linked Data have obvious similarity. The ER model describes entities and relationships, which is the conceptual schema of RDB. RDF model consists of three parts: subject, predicate and object, which is the standard model for data interchange on the Web. The entities in ER model and subjects in RDF model are all the things that can be anything in the real world. Both the relationships in ER model and predicates in RDF model describe the relations between things.

    Since RDB and RDF share the similar modeling approach at the schema level, it is reasonable that mapping approach should be based on RDB schema. This kind of conceptual level mapping also can provide efficient SPARQL query processing in RDB.

    Improvements/Applications: The paper realizes SPARQL query processing in RDB, which is based on conceptual level mapping. The query experiments show that it is a concise and efficient way to populate Linked Data.

     

     

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  • How to Cite

    Ri Kim, J., Zhao, Z., & Kook Han, S. (2018). Sparql query processing in relational databases. International Journal of Engineering & Technology, 7(2.33), 84-88. https://doi.org/10.14419/ijet.v7i2.33.13860