SDM-RDFizer

Abstract

In recent years, the amount of data has increased exponentially, and knowledge graphs have gained attention as data structures to integrate data and knowledge harvested from myriad data sources. However, data complexity issues like large volume, high-duplicate rate, and heterogeneity usually characterize these data sources, being required data management tools able to address the negative impact of these issues on the knowledge graph creation process. In this paper, we propose the SDM-RDFizer, an interpreter of the RDF Mapping Language (RML), to transform raw data in various formats into an RDF knowledge graph. SDM-RDFizer implements novel algorithms to execute the logical operators between mappings in RML, allowing thus to scale up to complex scenarios where data is not only broad but has a high-duplication rate. We empirically evaluate the SDM-RDFizer performance against diverse testbeds with diverse configurations of data volume, duplicates, and heterogeneity. The observed results indicate that SDM-RDFizer is two orders of magnitude faster than state of the art, thus, meaning that SDM-RDFizer an interoperable and scalable solution for knowledge graph creation. SDM-RDFizer is publicly available as a resource through a Github repository and a DOI.

Knowledge4COVID-19: A semantic-based approach for constructing a COVID-19 related knowledge graph from various sources and analyzing treatments’ toxicities

“…The shared data sources are mapped to the Knowledge4COVID-19 unified schema. SDM-RDFizer [25] transforms these shared data into an RDF graph by executing the RML mapping rules. SDM-RDFizer implements optimized data structures that are exploited during the execution of RML mapping rules to speed up the KG creation process [25] .…”

“…SDM-RDFizer [25] transforms these shared data into an RDF graph by executing the RML mapping rules. SDM-RDFizer implements optimized data structures that are exploited during the execution of RML mapping rules to speed up the KG creation process [25] . The Knowledge4COVID-19 KG is published following the Linked Data principles.…”

“…The research community has actively contributed to the problem of automatic knowledge graph creation. As a result, declarative specification of a KG creation process [25] , [41] , [42] , techniques for semantic data integration [43] , [44] , and virtualization of KGs [45] , [46] enable to merge data silos and provide an integrated view of data and metadata. Existing KG construction methods vary from crowdsourced (e.g., Wikidata [47] ), extraction from existing knowledge bases (e.g., DBpedia [48] and YAGO [49] ), and automatic generation (e.g., KnowledgeVault [50] and AI-KG [51] ).…”

The FAIR Data Point: Interfaces and Tooling

“…FAIR-in-a-box installs the GraphDB metadata repository (Figure 1B), the FDP Server (Figure 1C), and the FDP Web Client (Figure 1D). Beyond the metadata that is served by the default FDP, FAIR-in-a-box has an additional pipeline-based on YARRRML (Figure 1.A1; [13]) and RDF Mapping Language (RML) (Figure 1.A2; [14])/SDM-RDFizer [15] -that supports the creation and import of FAIR data into a separate database in GraphDB. FAIR-in-a-box has two further components, one that updates the metadata layers automatically when the underlying data changes (Figure 1F), and a second that provides a pre-configured solution, based on Hitch+Varnish [16], to enable a Secure Sockets Layer (SSL) proxy (Figure 1E) to the FDP Client, thus simplifying provision of HTTPS access to the FDP.…”

RML based ontology development approach in internet of things for healthcare domain

“…Dimou, Sande, Colpaert et al (2014) introduced the RML mapping language, a standardized language built on top of the W3CS R2RML specification for mapping relational databases to RDF. Iglesias et al (2020) proposed SDM-RDFizer, an RDF Mapping Language (RML) interpreter that converts raw data in various formats into an RDF information graph. Gyrard, Serrano and Atemezing (2015) presented the semantic web methodologies, best practices and recommendations beyond the IERC Cluster Semantic Interoperability (IERC AC4).…”