Taught by Jen Williams.

Linked Data is a set of best practises that defines how to structure data so that it can be interlinked and published on the Web. By using the Web as the publishing platform, data can distributed across innumerate points, while still allowing navigation and discovery through the use of URIs in the data itself. Data models are embedded into the structure of the Linked Data itself, and follow the same best practises by being defined as Linked Data Vocabularies.

The principles of Linked Data allows developers to publish data without the need to develop custom APIs, and offers the ability for data publishers to adopt data standards to improve interoperability between disparate sources of data.

This “Introduction to Linked Data” course will cover all of the basics around the structure of the data itself, the use of URIs, popular vocabularies and show how these principles are applied by some existing Linked Data sites.

Coffee break

Taught by Kal Ahmed.

This course goes through some of the practical and technical aspects of creating and delivering linked data. We will start with an overview of how HTTP can be used to deliver data to both humans and machines from the same set of web addresses; and then move on to looking at the different syntaxes used for encoding Linked Data.

For the practical element of this course, we will work with Turtle which is also useful to understand for the later parts of the course that deal with SPARQL. Attendees will have the chance to create their own mini knowledge graphs by hand using Turtle syntax.

Lunch break

Taught by Andy Seaborne, Jen Williams, Kal Ahmed, and Stuart Williams.

SPARQL is the standard W3C query language for semantic web applications and has been widely adopted across both open source and commercial triple stores. As well as query capabilities, the SPARQL standards define the way to access triple stores over HTTP and get back query results in JSON, XML and other common data formats.

In two sessions, attendees will get a solid grounding in SPARQL, including a large component of practical exercises where attendees have the opportunity to discuss tips and tricks with the instructors for being more effective with SPARQL queries.

This first SPARQL session will cover the the fundamentals of SPARQL queries. We will then do practical work with both teaching data and an existing large RDF dataset to ground the learnings.

Attendees will need a laptop with a wi-fi connection and a browser capable of running modern Javascript applications.

Coffee break

Taught by Kal Ahmed.

In this short session we introduce the basics of RDF Schema, a vocabulary that allows us to specify how RDF types and properties interact. We will look at how we can enhance our existing hand-crafted RDF data with RDF Schema to identify types, properties and their relationships in our data. This provides the foundation for the later parts of the course that deal with RDF modelling.

Taught by Andy Seaborne.

RDF is a flexible data format for any data. Is the data what you expect it to be? By ensuring the data has the right shape, applications that consume the data don’t break as the data is updates and the shape of the data remains correct.

In this session, we will give an introduction to the W3C SHACL standard for RDF data validation and discuss how it is used to build robust RDF data-driven applications.

End of day

Taught by Andy Seaborne.

From the solid grounding in the building blocks of SPARQL, provided in the SPARQL 101 session, this second session will introduce more of the SPARQL query language as well as go in to the data access mechanism used over the web.

Coffee break

Taught by Stuart Williams.

Building on our knowledge of RDFS and SPARQL this session will introduce students to a ‘toolbox’ of SPARQL queries that can be used to introspect ‘unknown’ datasets to reveal their structure.

The techniques and tricks shown in this session can be incredibly helpful when investigating an RDF data set. We will show how to use SPARQL queries and some basic knowledge of RDF Schema to glean a lot of useful information about the content of the data set and the structure of relationships contained in it.

Lunch break

Taught by Stuart Williams.

Modelling the world in RDF involves identifying the important entity types within some domain of interest, their properties and relationships. In this session we will workshop the development of an RDF data model for a familiar domain.

We’ll start with some tabular data and work at recognising the entities and relationships within. We’ll draw some diagrams to illustrate our models and sketch some instances from the source data. If time permits we’ll write down our domain models in RDFS. We may also touch on the extra expressivity that the Web Ontology Language (OWL) brings.

Coffee break

Taught by Peter Crocker.

Big data technologies have made significant progress in addressing problems related to the volume and velocity of data. Graph technologies have enhanced our ability to deal with the challenges in the variety of data. In this talk we will look at how logic or reasoning can capture expertise and knowledge to create a true knowledge graph. We will cover a number of applications of the technology and work through a particular example that illustrates how this reasoning can transform the way in which you find answers your questions.

Coffee break

Taught by Andy Seaborne, Jen Williams, Kal Ahmed, Peter Crocker, and Stuart Williams.

For the final tutorial of the course we will take a brief spin through some of the other related technologies that we didn’t have time to include in the previous sessions! This session is intended to give students an overview of what other technologies to investigate and what tools and services are available to start their own Linked Data projects.