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RDF and SPARQL Essentials

Tish Chungoora

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?

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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 data representation, data architecture and data engineering.

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What's inside

Learning objectives

  • Knowledge graph technologies that are revolutionising the way we store and query data at scale
  • Author rdf data and perform create, read, update and delete (crud) operations using the sparql query language
  • Comfortably speak rdf and sparql and use the jargon in technical conversations with stakeholders
  • Acquire a rock-solid foundation for taking on more advanced training in semantic approaches such as rdfs and owl

Syllabus

Gain an understanding of the context of the course, its scope, audience and learning outcomes.

This is the very first lecture of this course, where we'll go through introductions and mention a few key terms relevant to the topic of knowledge graphs and more specifically RDF and SPARQL.

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In this lesson, we will clarify the intended audience for the course and highlight all the key learning outcomes you will benefit from.

This is about the scope of the course, touching on its coverage and things that are out of scope in this introductory course.

Here, you will find a decision tree diagram that will help you decide whether this course is really what you are after.

Recognise the RDF graph model and have a clear view of RDF triples and their constituent building blocks.

This lesson is all about basic jargon in RDF graphs, including nodes and edges.

Recognising the 'triple' as the fundamental building block of RDF graphs is key. This lesson illustrates what triples are and their constituent parts.

In RDF, there are three types of nodes - IRI nodes, literals and blank nodes. Being able to recognise and work with IRI nodes and literals is key in the beginner's journey into RDF and the Semantic Web Stack.

This quiz will test your understanding of the building blocks of RDF graphs.

Master the art of composing lean RDF data in Turtle format.

If you don't have a preinstalled RDF graph database, no worries - we'll download an open source workbench called Blazegraph.

In this activity, you will download Blazegraph.

Here, we will go through the procedure for running Blazegraph and starting the workbench.

In this activity, you will run Blazegraph and explore the workbench.

In this lesson, you will start to author RDF data. You will see the importance of unique identification of resource nodes and predicates using IRIs, and ways to shorten IRIs using prefix shortcuts.

'RDF type' is an important construct in the RDF graph model, allowing simple groupings and categories of things to be captured. In this lesson, we will see how to declare triples that involve the 'RDF type' predicate.

In this lesson, we will complete all the triples surrounding Bugs Bunny, his creator and his debut appearance.

Note: The triples in this tutorial can be downloaded from Lecture 16.

The Turtle syntax is a human-friendly way of writing RDF data. It's almost like writing classic sentences and in this lecture, we'll make sure to identify this syntax and make the most of appropriate syntax highlighting in Blazegraph.

This activity will enable you to get hands-on with composing RDF data using the Turtle syntax.

Another illustration of writing good RDF data.

Note: The triples in this tutorial can be downloaded from Lecture 18.

Get hands-on with composing RDF data using the Turtle syntax.

Practice makes perfect! This activity will help you complete all the triples in the graph for the remaining Looney Tunes characters, their creators and debut appearances.

Once all the triples have been written down, it's now time to load the dataset into the graph database. In this lesson, we'll see the procedure on how to load the dataset in Blazegraph.

Note: The complete set of triples can be downloaded from Lecture 21.

In this activity, you will load the dataset into the graph database. In case you need to grab the RDF data in Turtle format, there's a handy text file attached containing all the triples we've composed so far.

Approach RDF data querying problems using a wealth of constructs from the SPARQL Protocol and RDF Query Language.

In this lesson, we'll introduce how to compose a basic graph pattern to pull all the triples contained in the graph.

Pulling and displaying all the triples contained in the graph is not such a good idea in practice! For this reason, we can use the LIMIT modifier to surface a sample of triples as opposed to the whole dataset.

Test your knowledge of writing a basic graph pattern.

In this lecture, we'll explore a very simple one-hop query pattern.

This builds on top of the one-hop query pattern, where we'll attempt to find Bugs Bunny's creator.

Some more practice on the on-hop query pattern, where this time we'll be displaying more than one result in tabulated format.

Checkpoint to test your knowledge on composing a simple one-hop query pattern.

In this lesson, we'll see how to use the conjunction (.) symbol to compose more complex query patterns.

Test your knowledge of using conjunction (.) to match against more complex query patterns.

Learn a few tips on how to compose lean queries. The principles are very similar to how you compose in Turtle syntax.

Text your knowledge of pattern matching and writing lean queries.

This lecture will show you how to count things in the graph.

Test your knowledge of how to count things in the graph.

How do we remove duplicates from a list of results? It's super-easy and here, we'll show how to achieve this.

Test your knowledge of how to surface deduplicated results.

This lesson illustrates how to check for the non-existence or existence of graph patterns.

Test your knowledge of using the EXISTS clause.

In this lesson, we'll see how to break down a simple problem and write a query to discover characters who were co-created.

In this lecture, we expose how to use the MAX and MIN aggregate functions to determine the maximum and minimum values in a list.

Here, we'll see a simple example of performing a mathematical operation.

Test your knowledge of using BIND and performing a simple mathematical operation.

This lecture exposes a clever way of ordering and limiting a results set to analyse information.

Here we will get to see an example of using the AVG aggregate function for averaging values from a results set.

More advanced analysis of graph data sometimes involves the use of BIND and the IF conditional. In this lesson, we'll illustrate an example of achieving more complex analysis.

This lecture introduces the purpose of COALESCE and IF for writing lean queries where we need to run different tests.

Test your knowledge of using BIND, COALESCE and IF.

In this lecture, we'll go through another SPARQL construct for combining graph patterns - the OPTIONAL clause, which allows for optional matching results to be surfaced.

Test your knowledge of working with the OPTIONAL clause.

The UNION clause is another way of combining graph patterns in SPARQL. It basically allows for writing queries involving disjunction.

Test your knowledge of working with the UNION clause.

This lesson illustrates the use of the MINUS clause.

Aggregation through grouping is important in data analysis. The example in this lecture shows how to use the GROUP BY modifier.

Test your knowledge of using the GROUP BY modifier.

In SPARQL, there are other kinds of queries other SELECT queries. This lecture introduces the DESCRIBE query.

Test your knowledge of building a DESCRIBE query.

This lecture illustrates how to build an ASK query, which return True or False based on the pattern being queried.

Test your knowledge of writing an ASK query.

Building cool sub-graphs is made possible with the CONSTRUCT query. This lecture introduces this query.

This is a continuation of the previous lecture where we'll see how a more advanced example of crafting a CONSTRUCT query.

Test your knowledge of building the CONSTRUCT query.

Utilise the fundamental Property Path constructs in SPARQL to traverse RDF graphs.

This lecture introduces the concept of property paths and the ease with which SPARQL makes it possible to 'connect the dots' at scale and draw useful conclusions.

Thinking of reversing direction? No problem! Enter the 'inverse path'.

Breadcrumbs are not always helpful, as we know from the fairy tale 'Hansel and Gretel'! However, SPARQL lets you find your way back with 'sequence paths'. Sequence paths allow you to go from node A to node B through the chaining of predicates (i.e. routes you can follow).

Test your knowledge of using the inverse and sequence paths.

In this lesson, we'll take a look at recursive paths, which allows us to build queries involving arbitrary lengths triples.

This is a continuation of the previous lecture on 'recursive paths'.

This lecture will illustrate a query to list all possible paths you can take between a node A and a node B in a forward direction of travel. It's not a labyrinth after all!

Test your knowledge of property paths and writing a query to understand the possible paths you can take between two nodes.

Perform RDF database updates by inserting or deleting specific triples and graph patterns, and compose inference rules.

SPARQL Update includes a set of operations for inserting and deleting specific triples, as well as inserting and deleting data based on graph patterns. In this lesson, we'll take a look at the INSERT DATA function.

This lecture covers the DELETE DATA function.

This lecture covers the INSERT function for adding new triples to the graph based on pattern matching. It is one of the mechanisms used in practice to build inference rules to automatically create new knowledge in an RDF graph.

Before you attempt this tutorial, please make sure to reload all the data by running a SPARQL update on the contents of the file reload-data-sparql-update.txt

Test your knowledge of using the INSERT function.

Here, we'll get to see an extended example of using the INSERT function.

Note: The insert query used in this tutorial can be downloaded in Lecture 75.

Test your knowledge further on using the INSERT function.

This lesson will cover the basics of the DELETE function for removing triples that conform to certain graph patterns.

Create uniquely-identifiable RDF graphs that serve as containers for triples.

A 'named graph' is a uniquely identifiable container for triples. This lecture introduces this topic.

In this tutorial, we'll get to see how to create a named graph.

After creating the named graph, we can then make it the default graph and query its contents. This lecture is about this technique for querying the triples in single named graphs as the default graph.

Test your knowledge of creating and querying a named graph.

This lessons covers the deletion of named graphs.

Test your knowledge of dropping a graph.

This lesson provides a boilerplate SPARQL query for retrieving all named graphs.

Expose and navigate the underlying schema for an RDF graph.

As you assemble triples and assign resource nodes a 'type', you indirectly create an underlying structure (a proto-ontology, so to speak). Revealing this latent underlying graph schema (or graph vocabulary) is important and this lecture introduces this topic.

In this lecture, we'll see how to surface the types of things that exist in the graph.

Here, we will illustrate a simple query to surface all the predicates used in the graph.

We can then combine the understanding from the previous two lectures to come up with a list of applicable predicates for every type of thing that exists in the graph.

Test your knowledge of building queries to surface the graph vocabulary.

Get pointers for further learning.

Course summary.

This is the concluding lecture for this series. Hope you've enjoyed the course!

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Focuses on the 'why' of data representation and architecture, not just the 'what' and 'how'
Taught by instructors who seem to have deep experience in data and architecture
Develops skills for managing, organizing, and using data to build AI and advanced analytics
Exposes learners to an up-and-coming topic that is essential in modern data analysis and AI development
May be good for learners who work with large and complex datasets and are interested in the internals of and managing data at scale
Hands-on activities help make learning feel practical, not conceptual

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Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in RDF and SPARQL Essentials with these activities:
Review RDF and SPARQL fundamentals
Refresh your memory about RDF and SPARQL fundamentals to prepare for this course.
Show steps
  • Review the basics of RDF and SPARQL data models.
  • Recall how to query RDF graphs using SPARQL.
  • Consider practical applications of RDF and SPARQL.
Review relational database concepts
Recall the basics of relational databases to draw parallels with RDF.
Show steps
  • Revise the concepts of tables, rows, and columns.
  • Recap the principles of data normalization and database design.
  • Identify similarities and differences between relational databases and RDF.
Follow a tutorial on SPARQL queries
Reinforce your understanding of SPARQL by completing a guided tutorial.
Show steps
  • Choose a SPARQL tutorial that aligns with your learning objectives.
  • Follow the steps in the tutorial to write and execute SPARQL queries.
  • Experiment with different SPARQL constructs to explore their capabilities.
Six other activities
Expand to see all activities and additional details
Show all nine activities
Practice writing SPARQL queries
Strengthen your SPARQL proficiency by practicing writing queries.
Show steps
  • Identify a dataset that you can use for practicing SPARQL queries.
  • Formulate specific questions that you want to answer using SPARQL.
  • Translate your questions into SPARQL queries.
  • Execute your queries and analyze the results.
Develop a cheat sheet for SPARQL constructs
Create a resource that summarizes key SPARQL constructs for quick reference.
Show steps
  • Review the SPARQL syntax and identify the most commonly used constructs.
  • Organize the constructs into categories, such as basic queries, advanced queries, and property paths.
  • Create a cheat sheet that provides a concise explanation and syntax for each construct.
  • Use the cheat sheet as a reference while working on SPARQL queries.
Mentor a beginner in RDF and SPARQL
Share your knowledge and solidify your understanding by mentoring others.
Show steps
  • Identify someone who is new to RDF and SPARQL and is seeking guidance.
  • Establish regular communication channels for mentoring sessions.
  • Provide personalized guidance and support as the mentee progresses in their learning.
  • Encourage the mentee to ask questions, experiment with SPARQL queries, and seek additional resources.
Build a small RDF knowledge graph project
Apply your knowledge of RDF and SPARQL to a hands-on project.
Show steps
  • Choose a domain or topic that interests you.
  • Design an RDF schema that represents the concepts and relationships in your chosen domain.
  • Populate your RDF graph with data using RDF serialization formats like Turtle or JSON-LD.
  • Write SPARQL queries to retrieve and analyze information from your knowledge graph.
  • Present your project and demonstrate how it leverages RDF and SPARQL.
Participate in a SPARQL query competition
Challenge yourself and test your SPARQL skills against others.
Show steps
  • Find a SPARQL query competition that aligns with your interests and skill level.
  • Study the competition rules and dataset.
  • Develop efficient and effective SPARQL queries to answer the competition questions.
  • Submit your queries and wait for the results.
  • Reflect on your performance and identify areas for improvement.
Contribute to an open-source RDF or SPARQL project
Engage with the open-source community and enhance your skills.
Show steps
  • Identify an open-source RDF or SPARQL project that interests you.
  • Review the project's documentation and familiarize yourself with its codebase.
  • Identify areas where you can contribute, such as feature enhancements, bug fixes, or documentation improvements.
  • Fork the project, make your changes, and submit a pull request.
  • Work with the project maintainers to incorporate your contributions.

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