This document introduces linked data and the semantic web. It defines linked data as using URIs to identify things on the web and describe them using standard formats like RDF to link related things. This allows data on the web to be treated like a large database. The semantic web builds on linked data principles to publish structured data on the web that can be processed by machines, helping make information more discoverable and science more reproducible. Challenges include agreeing on definitions, performance of query languages, and the effort required to publish high-quality linked data.

1. Introduction to Linked Data
and Semantic Web
Jon Blower (@Jon_Blower)
CTO, Institute for Environmental Analytics (@env_analytics)
Coordinator, MELODIES project (@MelodiesProject)

2. Photo by Susan Lesch, from Wikipedia
Sir Tim Berners-Lee
“If … the Web made all
the online documents
look like one huge book,
[the Semantic Web] will
make all the data in the
world look like one huge
database.”
The Semantic Web is one application of
Linked Data technologies
The terms are often used interchangeably

3. Satellite
Instrument
Publication
Algorithm
Scientists
Dataset

4. The Web is the
“Web of Documents”

5. carries
produced
describes /
uses /
queries …
produced
authored
Linked Data
techniques build
the “Web of Data”

6. What could we do with this?
• Find all satellites that produced data on sea surface
temperature
• Find all experts on the use of a certain algorithm
• Find all publications written about a certain instrument
• Find the whole history (provenance) of a dataset
• Add new information (e.g. annotations) as our knowledge
grows
=> Information is more discoverable
=> Science can be more reproducible

8. 8
Search for “The Martian”,
Google shows:
• Facts about the film
• Cast
• Showtimes
• Reviews
• Related films
This is Linked Data in action!
(powered by schema.org
vocabulary)

9. Linked Data principles
• Give “things” unique and persistent identifiers
• Allow the identifier to be “looked up” on the web
– i.e. the identifier should be an HTTP URL
– e.g. http://dbpedia.org/resource/Prague
• Provide a description of the thing in a standard format
– Human readable (e.g. HTML)
– Machine readable (e.g. RDF), using agreed vocabularies
• Link to other related things
– And say why they are linked
• The web of networks and links is called a graph
Goal: Describe things more precisely, in a machine-readable way

10. The building blocks of Linked Open Data
• URIs (Uniform Resource Identifiers) to identify things
• RDF (Resource Description Framework) to encode the graph
• Ontologies and vocabularies define the terms and concepts
we use to encode information
• SPARQL (query language) to search the graph, maybe over
the Web
(Note that other graph technologies are available!)

11. Linked Open Data Cloud
https://lod-cloud.net/
Example datasets:
• DBPedia
• GeoNames
• Data.gov.uk
• Bibliothèque
nationale de
France
• World War 1 as
Linked Open Data
• UK Met Office
Weather Forecasts

12. Some challenges (i.e. costs!)
• Agreeing on definitions (ontologies/vocabularies)
• Performance and efficiency of RDF and SPARQL
• Searching across multiple data stores efficiently
• Steep learning curve and (often) low maturity of tools
=> Significant effort to publish good-quality Linked Data
• Overall challenge:
• Balance effort and cost of publication with user benefit

13. Finally... a few things to check out
• Schema.org
• Simple vocabularies for common concepts (e.g. for search
engines)
• Geospatial RDF stores
• E.g. Strabon (http://earthanalytics.eu/)
• JSON-LD
• Makes Linked Data more friendly
• CoverageJSON (https://covjson.org)
• Encodes nD geospatial data in JSON, uses JSON-LD
• Other non-RDF graph technologies
• Property graphs (e.g. Neo4j)
• Facebook Graph API
• ...

14. Thank you!
Jon Blower (@Jon_Blower)
CTO, Institute for Environmental Analytics (@env_analytics)
Coordinator, MELODIES project (@MelodiesProject)