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INSPIRE Hackathon Webinar Intro to Linked Data and Semantics

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Jon Blower

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INSPIRE Hackathon Webinar Intro to Linked Data and Semantics

  1. 1. Introduction to Linked Data and Semantic Web Jon Blower (@Jon_Blower) CTO, Institute for Environmental Analytics (@env_analytics) Coordinator, MELODIES project (@MelodiesProject)
  2. 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. 3. Satellite Instrument Publication Algorithm Scientists Dataset
  4. 4. The Web is the “Web of Documents”
  5. 5. carries produced describes / uses / queries … produced authored Linked Data techniques build the “Web of Data”
  6. 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
  7. 7. 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)
  8. 8. 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
  9. 9. 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!)
  10. 10. 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
  11. 11. 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
  12. 12. 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 • ...
  13. 13. Thank you! Jon Blower (@Jon_Blower) CTO, Institute for Environmental Analytics (@env_analytics) Coordinator, MELODIES project (@MelodiesProject)

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