2. About me
• Education:
– Computer Science Eng & PhD Computer Science
– MsC Library & Information Science Work experience:
– Till 2002: mix of industry (e-commerce) and part-time
lecturing.
– Now professor, University of Alcalá
– Technical coordinator of LUISA (FP6), coord. of VOA3R
(ICT PSP), agINFRA (FP7), SEMAGROW (FP7)
• Service:
– Board member & TG LOD leader, EuroCRIS
– EIC Emerald’s Program journal and Inderscience’s IJMSO.
– EB member Interactive Learning Environments, The
Electronic Library, IJSWIS and others.
3. Contents
• What is Linked Open Data
• The Organic.Edunet & VOA3R cases for
LOD
• The general case
• Exploring the possibilities for interlinking
• Roadmap
4. Linked Open Data?
• What is LOD?
– A movement of people, organizations and
networks towards making “data” in general
more readily available on the Web.
– A set of technological conventions to make
data available for machines (=software).
– A evolution(/simplification) of the idea of the
(Semantic) Web.
– In a loose sense, a field of research framed
in the idea of “Web Science”.
5. Linked Data Principles
1. Use URIs as names for things.
2. Use HTTP URIs so that people can
look up those names.
3. When someone looks up a URI,
provide useful RDF information.
4. Include RDF statements that link to
other URIs so that they can discover
related things.
Tim Berners-Lee 2007
http://www.w3.org/DesignIssues/LinkedData.html
10. <http://voa3r.cc.uah.es:8080/dataset/resource/persons/Diane_Le_H%
C3%A9naff>
a cerif:Person ;
rdfs:label "Diane Le Hénaff" ;
cerif:gender "f" ;
cerif:internalIdentifier
"ff8081813078a4dc01308979fe2c0002" ;
cerif:keyword ”agriculture" ;
cerif:linksToProject
<http://voa3r.cc.uah.es:8080/dataset/resource/
proj_pers/VOA3R-Diane_Le_H%C3%A9naff-uuid> ;
cerif:uri <fr.linkedin.com/in/lehenaff> ;
cerif:isAuthor <http://www.inra.fr/234562>
•
The case of VOA3R
11. Limitations (from a linked data
perspective)
Web
API
A
Aggregator (harvester or
query client)
Shortcomings
1. APIs provide proprietary
interfaces OAI-PMH + SQI
2. Aggregators are based on
a fixed set of data sources.
(not necessarily, but
require some registry of
providers)
3. You can not set hyperlinks
neither between learning
object descriptions nor
from them to other data
or terminologies (they
can be there somehow but
not interpreted as such)
Web
API
B
Web
API
C
Web
API
D
Adapted from: Bizer:, C.- The Web of Linked Data (2009)
12. Browsing & querying
Adapted from: Christian Bizer: The Web
of Linked Data (26/07/2009)
B C
LO
typed
links
A D E
typed
links
typed
links
typed
links
LO
Term
Term
LO
LO LO
LO
Term
Term
Query resolverBrowser
Term(s)
16. The steps towards the Web of
Linked Learning
• Exposure phase – already started
– Converting metadata into common RDF
• Interlinking phase – tools available
– Adding the links between the materials
• Consumption phase – still to be imagined
– Designing creative uses of the interlinked
materials.
17. GLOBE Materials
17
• GLOBE(Global Learning Objects Brokered
Exchange) enables share and reuse
between several learning object repositories
• We harvested GLOBE through OAI-PMH and
got around 770,000 metadata records (IEEE
LOM)
• Just to test the possibilities of interlinking
18. GLOBE materials: Keywords
• Around 5,5 million keywords in the sample (~7
keywords per resource)
• Large number of keywords generated via machine
translation (referenced by codes starting with “x-mt-”)
• Frequencies are high for relatively high number of
keywords (beyond 15)
• (might be attributed to automated extraction)
18
19. GLOBE materials: Classification
19
• A total of ~ 700k classifications distributed
across ~500k resources with ~1M taxon entries.
• About 92% of all the resources have at most 2 classifications,
only 187 resources have more than 10.
• There were only 43 different classification
purposes, with “discipline” 60% and “Technical
design” around 18%.
• The latter is from a vocabulary specific of the MACE project.
11% of the purposes were blank.
• Keywords and classifications were matched
against each other for the same resources (
~270k coincidences)
• This says that 38% of classifications look redundant from a
lexical perspective.
20. Possibilities of interlinking
• Checking exact lexical match of keywords
and classifications with DBPedia give
around 30% and 33% of matches.
– Note this is just a non-informed approach on a
sample.
– Indicator of hundreds of thousands links.
• Interlinking exercise with Limes (on
500.000 GLOBE records):
– For coverage to DBPedia or FAO countries
dataset: ~9000 (98% threshold)
21. Conclusions
• Linked data for educational resources
extends current investment for better
machine processing.
– And for “discovering related things”!
• The technology is there, but the
applications still not.
– Now it is time to put the efforts in exposing.
• There is a huge potential for interlinking
– only in keywords and classification!
