A Novel Approach for Generating SPARQL Queries from RDF Graphs
This addresses the challenge of querying RDF data for users by automating SPARQL query generation, though it appears incremental as it builds on existing methods for ontology transformation and database integration.
The paper tackled the problem of generating SPARQL queries from user keywords to query RDF graphs, resulting in a tool that is comprehensive, effective, and powerful based on evaluation with different test bases.
This work is done as part of a research master's thesis project. The goal is to generate SPARQL queries based on user-supplied keywords to query RDF graphs. To do this, we first transformed the input ontology into an RDF graph that reflects the semantics represented in the ontology. Subsequently, we stored this RDF graph in the Neo4j graphical database to ensure efficient and persistent management of RDF data. At the time of the interrogation, we studied the different possible and desired interpretations of the request originally made by the user. We have also proposed to carry out a sort of transformation between the two query languages SPARQL and Cypher, which is specific to Neo4j. This allows us to implement the architecture of our system over a wide variety of BD-RDFs providing their query languages, without changing any of the other components of the system. Finally, we tested and evaluated our tool using different test bases, and it turned out that our tool is comprehensive, effective, and powerful enough.