Semantic Adapter & Federated Query Engine Sample Clauses

Semantic Adapter & Federated Query Engine. The knowledge base creation pipeline presented in deliverables D5.2 and D5.3 integrates a semantic adapter and a federated query processing; it follows the PLATOON reference architecture presented in D2.1/D2.5. This pipeline is agonistic of the query engine used to implement the semantic connector and the federated query processing engine. The semantic adapter converts data sources in different formats, e.g., CSV, JSON or relational databases, into RDF; this conversion is guided by mapping rules specified in a mapping language, e.g., SPARQL-Generate or RDF Mapping Language (RML). These rules define declaratively concepts (i.e., classes and properties) in the PLATOON semantic data models (presented in D2.3) in terms of data collected from the PLATOON data sources. As a result, during the execution of these rules, the semantic adapter populates classes and properties in a unified knowledge base. Then, the pipeline uploads this unified knowledge base into a federation of SPARQL endpoints (e.g., in Virtuoso or Fuseki). The federated query processing engine provides an interface to these endpoints; it enables users to explore the knowledge graph and retrieve the answers to a SPARQL query. Two implementations of the pipeline are available; one resorts to SDM-RDFizer and the other one to SPARQL-Generator. The former, which is used in this deliverable, has a demo component as analysis tool which has been specifically designed to replicate the PLATOON reference platform as close as possible and make use of REST APIs. This enables us to validate the platoon reference architecture. Both pipelines are described in D5.2. Figure 5: Knowledge Graph Creation Pipeline
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