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A practical introduction to RDF, Turtle, TriG and SPARQL for authoring and querying knowledge graph data
Want to get hands-on with the hottest trends in data representation and data architecture? Want to learn the building blocks of how organisations across various sectors like IT, Manufacturing, Mass Media, Financial Services, Pharmaceutical and many more, are tearing down data silos to build self-descriptive datasets and drive next-level AI and analytics?
You've landed at the right spot! This course is about the Resource Description Framework or RDF for short, and SPARQL, which are two fundamental layers of the Semantic Web Stack for building knowledge graphs. Knowledge graphs are essentially datasets that are richly described and explicitly linked as networks. RDF is a simple data model for capturing these rich networks, and SPARQL is the query language for interrogating knowledge graphs that are expressed in RDF format.
While we are in the Information Age and technologies like relational databases (e.g. SQL) are old but not obsolete, and surely here to stay for many more years to come, organisations are quickly realising that their datasets need to be weaved together efficiently within the data value stream. This requires us to capture and describe data as networks and building a consolidated picture of our data resources, enabling us to answer key business questions more smartly, intuitively and at scale.
In this course, you'll learn how to work with RDF and SPARQL from a practical perspective. We're going to roll up our sleeves and dive into authoring RDF graphs in the Turtle and TriG formats, which are common human-friendly text formats for writing RDF data. We're going spend a great deal of time working with SPARQL and there will be loads of useful examples and problems we'll go through and solve along the way.
This course is for people who care about the future of data representation, data architecture and data engineering.