This Python project implements a generic translator from RDF to NGSI-LD.

The work takes inspiration from rdflib plugins that store RDF data in backends like Neo4j (https://github.com/neo4j-labs/rdflib-neo4j).

In this sense, this project provides an rdflib plugin where an NGSI-LD Context Broker works as the storage backend for RDF data. Additionally, the translator supports the ingestion of streams of RDF data via Kafka.

The following set of rules are applied to translate the RDF data model (triples) into the NGSI-LD data model (property graph):

• Subject: Maps to an NGSI-LD Entity. The URI of the subject in the RDF triple is the URI of the NGSI-LD Entity.

  ⚠️ This approach does not follow the convention recommended by ETSI CIM, which goes "urn:ngsi-ld:<entity-type>:<identifier>". The reason for doing this is to facilitate interoperability between RDF and NGSI-LD.

• Predicate:

  • a or rdf:type predicate maps to the NGSI-LD Entity Type. For example: the RDF triple <http://example.org/people/Bob> a foaf:Person translates into an NGSI-LD Entity of foaf:Person type, and URI http://example.org/people/Bob.

  • RDF Datatype property maps to an NGSI-LD Property. A special treatment is required when the literal of the predicate uses xsd:datetime. In this case the resulting NGSI-LD Property must follow the special format:

        "myProperty": {
            "type": "Property", "value": {
                "@type": "DateTime",
                "@value": "2018-12-04T12:00:00Z"
            }
        }

  • RDF Object property maps to an NGSI-LD Relationship. The target of the Relationship is the URI of the object in the RDF triple.

• Namespaces: There is no need to create specific @context for translating to NGSI-LD. The resulting NGSI-LD Entity can just used expanded the URIs. This approach is easier to maintain as avoids maintaining @context files.

  Optionally, If the ingested RDF data includes a definition of namespaces with prefixes, then this information could be used to generate the @context for the translated NGSI-LD Entity. The resulting @context can be send along the NGSI-LD payload or stored elsewhere and reference via Link header. The selected approach will depend on the use case andthe developer's implementation.

The translator could be configured to expect batches of RDF data, instead of streaming events. In batching mode, the translator can analyze all RDF triples for the same subject, bundle the datatype and object properties, and produce a complete NGSI-LD Entity with a set of Properties and Relationships. This approach can improve performance as less NGSI-LD requests are sent to create the Entities in the Context Broker.

This work was partially supported by the following projects:

• Horizon Europe aerOS: Autonomous, scalablE, tRustworthy, intelligent European meta Operating System for the IoT edge-cloud continuum. Grant agreement 101069732
• SNS Horizon Europe ROBUST-6G: Smart, Automated, and Reliable Security Service Platform for 6G. Grant agreement 101139068
• UNICO 5G I+D 6G-DATADRIVEN: Redes de próxima generación (B5G y 6G) impulsadas por datos para la fabricación sostenible y la respuesta a emergencias. Ministerio de Asuntos Económicos y Transformación Digital. European Union NextGenerationEU.
• UNICO 5G I+D 6G-CHRONOS: Arquitectura asistida por IA para 5G-6G con red determinista para comunicaciones industriales. Ministerio de Asuntos Económicos y Transformación Digital. European Union NextGenerationEU.