This course is geared towards developers and data professionals who want to become proficient in SPARQL, the query language for semantic data. It covers everything from querying RDF datasets to updating and integrating data using SPARQL. Through practical labs, participants learn to write efficient queries to extract insights from knowledge graphs and connect SPARQL endpoints to applications. By completion, students will be able to work confidently with Linked Data and semantic databases, a valuable skill in data-driven industries.
Curriculum:
Module 1: Understanding RDF Data for SPARQL – Quick overview of RDF graphs and data models to ensure a solid ground for writing SPARQL queries (triples, graphs, datasets).
Module 2: SPARQL Basics – SELECT Queries – SPARQL query structure, basic graph pattern matching, namespaces, and writing SELECT queries to retrieve data from a sample knowledge graph.
Module 3: Filtering and Modifiers – Using FILTER conditions, ordering results, limiting offsets, and pattern modifiers to refine query results.
Module 4: Advanced SPARQL Features – Mastering OPTIONAL patterns, UNION for combining results, BIND and VALUES, as well as ASK queries for boolean checks and DESCRIBE/CONSTRUCT queries to retrieve subgraphs.
Module 5: SPARQL 1.1 Updates and Extensions – Introduction to SPARQL 1.1 UPDATE for inserting/deleting data, handling datasets with multiple named graphs, and an overview of property paths for complex traversals.
Module 6: SPARQL in Practice – Connecting to SPARQL endpoints (e.g., DBpedia or Wikidata) programmatically, using SPARQL libraries (in Python or JavaScript) to integrate query results into applications, and best practices for building applications on top of SPARQL APIs.
