Abstract
Modern web wants the data to be in Resource Description Framework (RDF) format, a machine-readable form that is easy to share and reuse data without human intervention. However, most of the information is still available in relational form. The existing conventional methods transform the data from RDB to RDF using instance-level mapping, which has not yielded the expected results because of poor mapping. Hence, in this paper, a novel schema-based RDB-RDF mapping method (relational database to Resource Description Framework) is proposed, which is an improvised version for transforming the relational database into the Resource Description Framework. It provides both data materialization and on-demand mapping. RDB-RDF reduces the data retrieval time for nonprimary key search by using schema-level mapping. The resultant mapped RDF graph presents the relational database in a conceptual schema and maintains the instance triples as data graph. This mechanism is known as data materialization, which suits well for the static dataset. To get the data in a dynamic environment, query translation (on-demand mapping) is best instead of whole data conversion. The proposed approach directly converts the SPARQL query into SQL query using the mapping descriptions available in the proposed system. The mapping description is the key component of this proposed system which is responsible for quick data retrieval and query translation. Join expression introduced in the proposed RDB-RDF mapping method efficiently handles all complex operations with primary and foreign keys. Experimental evaluation is done on the graphics designer database. It is observed from the result that the proposed schema-based RDB-RDF mapping method accomplishes more comprehensible mapping than conventional methods by dissolving structural and operational differences.
Highlights
Most of the data are still stored in the relational databases, but the current semantic web requires data in Resource Description Framework (RDF) form
In the proposed schema-based RDB-RDF mapping, the mapped RDF graph is denoted by the conceptual schema of a relational database, which is the primary resource to generate SPARQL query
An efficient way to populate Linked Open Data (LOD) dataset is by publishing RDB data on the web as an RDF data graph
Summary
Most of the data are still stored in the relational databases, but the current semantic web requires data in RDF form. The SPARQL query engine evaluates a query against the RDF repository in which the materialized RDF data has been loaded These existing methods are not properly handled the relationships (foreign key and primary key) and apply instance level mapping for data materialization. The on-demand mapping approach evaluates the queries against the relational data during run-time in converse to the data materialization method. In this model, the data remains located in the legacy database. The key contributions of the research article are a modification of mapping strategy from instance level to schema level, which improves the data retrieval time, efficient handling of primary foreign keys, during this data conversion process which leads to the improvised outcome.
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