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In recent years, the amount of data has increased exponentially, andknowledge graphs have gained attention as data structures to integrate data andknowledge harvested from myriad data sources. However, data complexity issueslike large volume, high-duplicate rate, and heterogeneity usually characterizethese data sources, being required data management tools able to address theimpact negatively of these issues on the knowledge graph creation process. Inthis paper, we propose the SDM-RDFizer, an interpreter of the RDF MappingLanguage (RML), to transform raw data in various formats into an RDF knowledgegraph. SDM-RDFizer implements novel algorithms to execute the logical operatorsbetween mappings in RML, allowing thus to scale up to complex scenarios wheredata is not only broad but has a high-duplication rate. We empirically evaluatethe SDM-RDFizer performance against diverse testbeds with diverseconfigurations of data volume, duplicates, and heterogeneity. The observedresults indicate that SDM-RDFizer is two orders of magnitude faster than stateof the art, thus, meaning that SDM-RDFizer an interoperable and scalablesolution for knowledge graph creation. SDM-RDFizer is publicly available as aresource through a Github repository and a DOI.

SDM-RDFizer: An RML Interpreter for the Efficient Creation of RDF Knowledge Graphs