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 Duration 4 hours

Course Outline

Introduction to RDF and SPARQL

  • Foundations of RDF: triples, IRIs, literals, and blank nodes
  • Utilizing Namespaces and QName in queries
  • Overview of SPARQL query forms and their use cases

Setting Up a SPARQL Environment

  • Installing and operating Apache Jena Fuseki or RDF4J Server
  • Loading sample RDF datasets into a triple store
  • Using a SPARQL client or workbench to execute queries

Basic SPARQL SELECT Queries

  • Creating triple patterns and retrieving bindings
  • Applying DISTINCT, LIMIT, and OFFSET
  • Sorting and projecting results using ORDER BY

Filtering and Solution Modifiers

  • Implementing FILTER expressions and built-in functions
  • Employing OPTIONAL for partial matching
  • Combining patterns with UNION and MINUS

Advanced Querying: Aggregation and Subqueries

  • Utilizing GROUP BY, COUNT, SUM, MIN, MAX, and HAVING
  • Nested queries and subselect patterns
  • Working with expressions and bind() to calculate values

Constructing and Transforming RDF

  • Using CONSTRUCT queries to generate new RDF graphs
  • Applying DESCRIBE and ASK query forms and understanding their appropriate use
  • Utilizing SPARQL UPDATE for data modification (INSERT/DELETE)

Working with Graphs and Named Graphs

  • Understanding Quads and the GRAPH keyword
  • Managing and querying named graphs
  • Best practices for organizing dataset graphs

Federated Queries and Remote Endpoints

  • Using SERVICE to query remote SPARQL endpoints
  • Performance considerations and timeouts
  • Strategies for combining local and remote data

Practical Lab: Real-World SPARQL Tasks

  • Querying DBpedia and other public datasets to gain insights
  • Building reusable query templates and views
  • Debugging common query errors and optimizing performance

Summary and Next Steps

Requirements

  • An understanding of the RDF data model and triples
  • Familiarity with basic HTTP and JSON concepts
  • Confidence in reading and writing basic programming or query expressions

Audience

  • Data engineers and integrators
  • Semantic web developers
  • Analysts working with linked data

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