A Declarative Formalization of R2RML Using Datalog and Its Efficient Execution

Abstract

R2RMListheW3C-recommendedmappinglanguagefordefiningdeclarative,customizedmappingsfromrelational databases to knowledge graphs, particularly in data integration and schema transformation scenarios. R2RML, like other mapping languages, enables viewing existing relational data in RDF, expressed in a structure and target vocabulary of the mapping author’s choice. Despite its broad adoption and plethora of extensions, the complete semantics of R2RML have not been concretely formalized so far. In this paper, we provide a declarative, computable, and rule-based formalization of R2RML through Datalog. We formally define the syntax of R2RML, provide a translation of its semantics into a Datalog program that can be used to evaluate RDF graphs, and discuss the associated complexity. The Datalog program defines output relations for the correct set of triples and quadruples, given any relational data as input relations. We validate the accuracy of our Datalog-based semantics by executing the R2RML test cases using a prototype implementation based on our approach. Our work lays the groundwork for further investigation into the properties and extensions of R2RML, unlocks the various benefits of Datalog reasoning in RDF generation, and introduces a promising approach for generating RDF graphs using any out-of-the-box Datalog reasoner.