Linked Data for Digital Humanities 20 minute demonstration lecture

1. Linked Data
for Digital Humanities
Demonstration lecture
Victor de Boer
Web & Media Group, CS, Vrije Universiteit Amsterdam
Netherlands Institute for Sound and Vision

2. 1. Linked Data for Digital Humanities
2. Reusing background knowledge
3. Data provenance

3. Examples of Digital Humanities Projects
http://armstrongdigitalhistory.org/, http://www.vcdh.virginia.edu/courses/fall07/hius401-f/,
http://digitalhistory.unl.edu/essays/thomasessay.php, http://www.philipvickersfithian.com/2013/05/gender-in-stacks-on-managing-small.html

4. “That is great. I would love that…
…but my research questions are slightly different.”
MontyPythonandtheHolyGrail

5. Better: Data as end-product
Build tools on top of the data.
Make sure others can do so as well.
Reuse and make reusable
Machine-readable for easy processing
Fig:C.Guéret

6. Linked Data
How to expose your data in a reusable way?
So, what is it?

7. Web of Documents (WWW)
Linked Documents

8. Web of Data
Linked Data
Structured data
Graph (networked) data
W3C Web standards stack
URIs, HTTP, RDF, RDFa, RDFS, OWL, SPARQL, etc

9. Uniform Resource Identifier (URI)
Use the Web to globally
identify things in your dataset
http://rijksmuseum.nl/data/painting1
I can go there using HTTP (dereference) and
then I get information about the thing
HTML page for browsers (humans)
RDF data for data applications (machines)

10. Semantic Web standard for writing down everything we
know about things (data + metadata + vocabularies)
(Subject, Relation, Object)
rijks:Painting001 dcterms:spatial geo:Amsterdam.
Resource Description Framework (RDF)
rijks:Painting001 geo:Amsterdam
dcterms:spatial

11. Triples reusing the same URIs form Graphs
rijks:Painting001
geo:Haarlem
rijks:Frans_Hals
147590
52.38084, 4.63683
geo:Noord-Holland
geo:Netherlands
rijks:Painting002

12. http://lod-cloud.net/

13. What’s in it for the Humanities?
a seaworthy example

14. Dutch Ships and Sailors
KB NEWSPAPERS
Dutch-Asiatic Shipping“VOC Opvarenden”
Jur Leinenga
Matthias van Rossum
Elbing voyagesArchangel voyages

15. HETEROGENEOUS but LINKED DATAMODELS
dss:Record
gzmvoc:Telling
gzmvoc:telling-1046-De_Berkel
__bnode_1
gzmvoc:aziatischeBemanning
dss:Ship
gzmvoc:Schip
gzmvoc: schip-1046-De_Berkel
dss:has_ship
gzmvoc:schip
"1046"
“Schip”
“De Berkel”
rdfs:label
dss:scheepsnaam
gzmvoc:scheepsnaam
dss:ShipType
gzmvoc:Scheepstype
gzmvoc: type-Ship
dss:has_shiptype
gzmvoc:has_shiptype
gzmvoc:scheepstype
“21”
“Moorse
mattroosen”
dss:azRegistratieKop
gzmvoc:azAantalMatrozen
gzmvoc:telling
gzmvoc:heeft DAS heenreis
dss:Record
das:Voyage
das:voyage-1918_61
Integrate datasets
No monolithic datamodel needed
No normalisation / dumbing down of data needed
Retain original model and intent

16. Reuse: Links to other web documents
Historical Newspapers
http://delpher.nl
isReferencedBy

17. mdb:Schip1 mdb:Kof
mdb:scheepsType
das:ShipX das:Kofship
das:typeOfShip
Aat:Kof
Aat:Platbodems
skos: broader
Reuse: background knowledge
AAT = Art & Architecture Thesaurus
http://www.getty.edu/research/tools/vocabularies/aat/
OWL = Web ontology language
https://www.w3.org/OWL/

18. Reuse: Data provenance and the Prov-O ontology
Essential for (humanities) research:
- What is the source of the data
- What has happened to the data
- Cf. Source and tool criticism
https://www.w3.org/TR/prov-o/
For each triple, DSS records
- Who made it (people/software?)
- Based on what source
- Content confidence

19. wasDerivedFrom
Provenance: link to original scans

20. http://dutchshipsandsailors.nl/data
(but also archived at DANS)

21. Data analysis and visualisation
The interlinked knowledge graph makes it possible to
efficiently investigate integrated research questions.

22. DIVE: Event-Based
Browsing of Linked
Historical Media

23. Wrap up
1. Linked Data allows for flexible
integration of heterogeneous data,
metadata and background
knowledge media
2. (Re)use of web resources,
vocabularies and ontologies
allows for efficient and novel
research questions
3. Data provenance fits DH
requirements

24. Thank you
Additional Hands-on Material:
Querying Linked Data with SPARQL:
http://tinyurl.com/dsssparqllecture
Hands-on session with DSS:
http://tinyurl.com/dh2015dsssparql

26. BIG ? LINKED? OPEN?

27. Wrap up
• Graphs, not tables
• Web standards and technologies
• URIs for things
• RDF (triples) to describe these things and
have links
• Distributed, heterogeneous (meta)data
• Integrate datasets in a flexible way
• Cross-collection, -institution, -domain
• Re-use background knowledge
• Provenance fits DH requirements well
• Knowledge graphs enable efficient
investigation of integrated research questions.

28. BIG DATA
LINKED DATA
OPEN DATA

29. Four V’s of Big Data
http://www.ey.com/GL/en/Services/Advisory/EY-big-data-big-opportunities-big-challenges

30. BIG DATA
LINKED DATA
VARIANCE as one of the V’s of
Big Data
VOLUME, VELOCITY are
challenges for LD

32. LINKED DATA
OPEN DATA
LINKED OPEN DATA
as the way to publish and reuse
datasets across the Web

33. www.w3.org/designissues/linkeddata.html