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PiLOD talk: Dutch Ships and Sailors


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Talk given at PiLOD meeting (25-6-2014) about the Dutch Ships and Sailors case of integrating digital history datasets.

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PiLOD talk: Dutch Ships and Sailors

  1. 1. Dutch Ships and Sailors Victor de Boer -VU Amsterdam / Beeld en Geluid – – -PiLOD 25 juni Beeld en Geluid -
  2. 2. The Problem: ((Maritime) historical) data is not integrated 25+ Maritime datasets; Heterogeneous
  3. 3. Jur Leinenga (Huygens ING) Monsterrollen Noordelijke provincies Matthias van Rossum (VU-hist) Generale Zeemonsterrollen VOC KB Delpher Dutch-Asiatic Shipping (Huygens ING) VOC Opvarenden (DANS Easy) Dutch Ships and Sailors CLARIN Call 4 project (9 mo. – ended april 2014)
  4. 4. Why Linked Data • Heterogeneous models, one dataformat – Link what can be linked • Keep specificity, allow integration at project level • Links to other sources: re-use knowledge • Extensible • Allow multiple levels of semantic enrichment/ normalization – through Named Graphs – Provenance
  5. 5. DAS GZMVOC MDB VOCOPV Begunstig den VOCOPV Soldijboek en PROV AAT VOCOPV Opvaren den foaf owl:sameAs dss:hasKBLink rdfs:subClassOf, rdfs:subPropertyOf dss:DAS link skos :exactMatch
  6. 6. Integrate metadata (properties) mdb:Schip1 mdb:Kof mdb:scheepsType das:ShipX das:Kofship das:typeOfShip dss:has_shipType rdfs:subPropertyOf rdfs:subPropertyOf
  7. 7. mdb:Schip1 mdb:Kof mdb:scheepsType das:ShipX das:Kofship das:typeOfShip Aat:Kof Aat:Platbodems skos:exactMatch skos:exactMatch skos:exactMatch Link to other datasets
  8. 8. Provenance • Individual named graphs have provenance information – Who made it (people/software?) – Based on what source – Content confidence
  9. 9. Data analysis and visualisation
  10. 10. Datathon mdb HISCO mdb:rang-bediende 54010 mdb:rang-boekhouder 33110 mdb:rang-bootsman 98120 mdb:rang-eerste_machinist 96910 mdb:rang-hofmeester 22450 mdb:rang-hofmeesteres 22450 mdb:rang-kapitein 4217 mdb:rang-kapitein_bevelvoerder 4217 mdb:rang-kok 53100 mdb:rang-kok_of_lichtmatroos 53100
  11. 11. DataLab