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Linked Open Data for Digital Humanities


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This presentation was given to Digital Humanties students on March 7. The goal is to introduce LOD and showcase what can be done with it.

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Linked Open Data for Digital Humanities

  1. 1. Linked Open Data for Digital Humanities What is Linked Open Data and why is it relevant for you ? Christophe Guéret (@cgueret)
  2. 2. Open Data “A piece of data or content is open if anyone is free to use, reuse, and redistribute it — subject only, at most, to the requirement to attribute and/or share-alike.”
  3. 3. Linked Data "a term used to describe a recommended best practice for exposing, sharing, and connecting pieces of data, information, and knowledge on the Semantic Web using URIs and RDF."
  4. 4. Linked Open Data● Linked Open Data = Open Data + Linked Data● Interconnected data sets that are on the Web and free to use● 5-star scheme
  5. 5. Why does it matter for DH ?● Digital Humanities use a lot of data and study relations between things● Data acquisition & curation represents a LOT of efforts for data consumers● Linked Open Data is a good way to ○ Facilitate your own work (as a data consumer) ○ Facilitate others work (as a data publisher)
  6. 6. Data found on the Web● You get the following table as a CSV file Kennis Stad Christophe Amsterdam David Parijs● And that Excel table from somewhere else Ville Pays Paris France Amsterdam Pays-Bas
  7. 7. And you want to integrate itKennis Stad Ville PaysChristophe Amsterdam + Paris France =?David Parijs Amsterdam Pays-Bas ● Data integration issues ○ Kennis, Stad, Ville, Pays ? ○ Parijs = Paris ? ○ Amsterdam = Amsterdam ? ● Lot of work for the (uninformed) consumer !
  8. 8. Linked Data approach● Assign unique identifiers (URIs) to concepts and things● Create a "triple": connect the identifiers with labelled, directed edges dbo:country dbpedia:Amsterdam dbpedia:Netherlands
  9. 9. Why does it solves the issue?● Shift some of the data integration load on the provider side ○ Clarify the semantics of the data ○ Refer to identifiers rather than names● There is only one "dbpedia:Amsterdam" at● Labels used for the edges are published by an external authority
  10. 10. Some vocabulary publishers
  11. 11. From triples to the Web of Data● Every triple is a bit of factual information● Because nodes are re-used across triples, the union of all the triples is a graph● The "Web of Data" is a pre-integrated, semantically clear, data set ready to be used!
  12. 12. Exploring relations in the graph
  13. 13. Lets make a social network !● The network ○ A node per European country ○ An edge means a shared official language ○ Label the edges with the languages ○ Label the nodes with the country names● Data source ○ DBpedia SPARQL● Visualisation tool ○ Gephi
  14. 14. SPARQL ?● Query language for Linked Open Data● Describe part of the graph and use variables dbo:country dbpedia:Amsterdam ?Country Suggested book to read
  15. 15. The query in SPARQLSELECT DISTINCT ?Source ?Target ?Label WHERE { ?country1 a <>. ?country1 <> ?language. ?country2 a <>. ?country2 <> ?language. FILTER (?country1 != ?country2) ?country1 <> ?Source. ?country2 <> ?Target. ?language <> ?Label. FILTER ((LANG(?Source) = "en") && (LANG(?Target) = "en") && (LANG(? Label) = "en"))}
  16. 16. Making the network● Get the query from ○● Copy & paste in to ○● Change the result format to "CSV"● Press "Run Query" and save the result● Open Gephi● Start a new project● Import the CSV file in the "Data Laboratory"
  17. 17. There is not only DBpedia ...
  18. 18. Last words● Look for data sources published as Linked Open Data (RDF), this can save you time● Consider publishing your own data as Linked Open Data● There is much more to say... ○ Using SPARQL within R (very easily) ■ ○ Reasoning capabilities of triple stores ○ Creating and extending vocabularies