Presentation from mentoring event of Open Education Europa Challenge (http://www.openeducationchallenge.eu/) about using Linked Data in educational applications.

1. Exploiting (Linked) Web Data in Educational Applications
Stefan Dietze L3S Research Center http://purl.org/dietze @stefandietze - Open Education Challenge, Berlin, 2014 -
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Stefan Dietze

2. Linked Data for education
 Data sharing: TED, Open Courseware, mEducator, LinkedUp,
LAK….
 Tutorials & workshops (eg „Linked Learning“ series)
 LinkedUniversities.org and LinkedEducation.org
 W3C Linked Open Education community group
Research areas
 Web & data science, information retrieval, semantic web &
Linked Data, data & knowledge integration
 Application domains: education/TEL, Web archiving, …
Some projects
Introduction
http://www.l3s.de/
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 See also: http://purl.org/dietze
Stefan Dietze

3. Social
Media
Exploiting Open Data for Education?nutshell
(Open) Educational Resources
World Wide Web
Distance Universities
MOOCs
Linked Open Data
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4. How Open is Open Data?
Open Data (as in “open licensing”)
Open licensing (ODL, CC etc)
Yet: variety of approaches
APIs/feeds: SOAP, REST, etc
Diverse schemas & vocabularies
(lack of) controlled vocabularies
Reuse & interoperability?
Linked Data (technology) (as in “interoperability”)
Defacto Standard for Open Data on the Web
W3C standards:
Common HTTP interface: SPARQL
Common representation: RDF
Dereferencable URIs
Shared/linked vocabularies
Linked Open Data
5-star scheme by Sir Tim Berners Lee
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5. Semantic Web
Example: Google Knowledge Graph (DBpedia, Freebase, Yago etc)
W3C standards (RDF & SPARQL) for knowledge representation and querying
URIs to identify/link data
“A little semantics goes a long way” (J. Hendler1)
dbp:United_States
http://dbpedia.org/resource/Cambridge_MA
dbp:W3C
country
cityOf
1 Hendler, J., The Dark Side of the Semantic Web, IEEE Intelligent Systems, Jan/Feb 2007
schema:City
typeOf
dbp:MIT
ru.dbp:Кембридж_(Массачусетс)
sameAs
headquarterOf

6. HTTP accessibility: persistent URIs, SPARQL
FOAF
Gene Ontology
BIBO
Geo Ontology
DBpedia Ontology
Dublin Core
BBC Programmes
Connected graph of open Web data (500+ datasets and 100 billion triples)
Persistent, dereferencable URIs & content negotiation, shared/linked vocabularies
SPARQL to query via HTTP
Other „incarnations“:
Google Knowledge Graph
Facebook Open Graph
http://schema.org
http://dbpedia.org/resource/Cambridge_MA
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7. LD to ensure discoverability of content/Websites (eg schema.org/microdata/RDFa)
Annotating HTML documents about (educational) material with schema.org (eg LRMI, Learning Resource Metadata Initiative)
Adopted by major sites (YouTube, LinkedIn etc) & tool support (DRUPAL, WordPress)
LD is not just for your data Schema.org for discovery of content/websites
http://schema.org
© Ramanathan V. Guha, Google, SemTech2014
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8. Other learning-relevant data & resources
Publications & literature
(Social) media resource metadata
Domain-specific knowledge: Bioportal, Europeana, Geonames, …
Cross-domain factual knowledge: DBpedia, Freebase, …
LD as body of knowledge for education
http://linkededucation.org
http://linkeduniversities.org
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Educational datasets and vocabularies
University Linked Data: The Open University UK, http://data.open.ac.uk, Southampton University, http://education.data.gov.uk, …
Open Educational Resources metadata: mEducator, Open Learn, Open Courseware, …
Schemas: Learning Resource Metadata Initiative (LRMI, mEducator Educational Resources schema, BIBO, AAISO, …

9. LD as background knowledge for educational apps?
http://metamorphosis.med.duth.gr/
Title: ECG Patient case 1001 chest and limb leads
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10. Title: ECG Patient case 1001 chest and limb leads
„ECG“ dismabiguation on Wikipedia: 9 meanings
LD as background knowledge for educational apps?
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11. dbpedia.org/resource/Electrocardiagraphy
1. Understanding data: contextual disambiguation through NLP tools
2. Enrichment with factual knowledge
dbpedia:Электрокардиография
category:Cardiac_procedures
dbpedia:Willem_Einthoven
3. interlinking with related resources
bbc:ProgrammeXY
slideshare:SlidesetXY
yovisto:VideolectureXY
Title: ECG Patient case 1001 chest and limb leads
Understanding, enriching, linking data
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12. „Success models“: data & applications
Supporting innovative tools & applications
Evaluation methods
LinkedUp – Linking Web Data for Education
Technology transfer & community-building
Involving educators, developers, computer scientists, data engineers…
http://www.linkedup-challenge.org/
Data curation & profiling
Collecting & exposing open data for education
Profiling of Web Data
http://data.linkededucation.org
EC-funded project aimed at advancing take-up of open data and related technologies
http://www.linkedup-project.eu/events
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http://www.linkedup-project.eu/

13. Community-building and collaboration Joint work on tangible outcomes (datasets, applications....)
Associated Partners
Initiatives
EC Projects
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14. Collected & curated datasets of educational relevance
Beyond collecting: published over 50 datasets as LD together with most important content providers e.g. TED, OCW, SoLAR etc
LinkedUp catalog: most comprehensive collection of LD/Open Data for education
RDF dataset metadata
Federated queries across datasets using type mappings
Publishing & curating educational data
http://data.linkededucation.org/linkedup/catalog/
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15. http://data-observatory.org/lod-explorer
Supporting developers and data consumers
Devtalk blog: developer resource & community to aid developers
Webinars and tutorials
http://data.linkededucation.org/linkedup/devtalk/
Topic-based annotation and discovery of data
Data exploration & visualisation features
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16. LinkedUp events, training & technology transfer Bringing stakeholders together
Data Providers & Data Scientists
Developers
Community-building through events & communication channels/social media (cross-disciplinary, industry & academia)
Exploitation of project outcomes across communities: technology transfer
(Co-)organised approx. 20 events (tutorials, workshops, booths etc)
More than 30 invited talks/lectures
….
Users (Learners, Tutors, Teachers)
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17. May –September 2013
October 2013 – May 2014
May 2014 – October 2014
Series of Open Data Competitions to promote applications which exploit Linked Open Data
http://www.linkedup-challenge.org/
LinkedUp Challenge

18. 23
14
13
8
9
10
0
5
10
15
20
25
Veni Vidi Vici
submissions
shortlist
LinkedUp Challenge results
 50 submissions of which 27 were shortlisted
and supported (through travel grants,
participation in events and rewards)
 13 Veni, Vidi, Vici winners
(grants: 1000 – 3000 €)
 Authors from 23 distinct, mostly European
countries
LinkedUp submissions & shortlist
Coatia; 4
Greece; 4
Belgium; 5
Italy; 7
Germany; 11
Spain;
13
France; 14
Netherlands; 15
United States;
15
United
Kingdom; 21
authors
Top-10
author‘s
origins
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19. Issues (1/3) - open data is messier than we think
SPARQL endpoint availability over time [Buil-Aranda et al 2013]
Accessibility of datasets?
Less than 50% of all SPARQL endpoints actually responsive at given point of time [Buil-Aranda2013]
“THE” SPARQL protocol? No, but many variants & subsets
Data “quality”?
…data accuracy (eg DBpedia)? [Paulheim2013]
…vocabulary reuse/links? [D’AquinWebSci13]
…schema compliance (RDFS, schemas) [HoganJWS2012]
Stefan Dietze
SPARQL Web-Querying Infrastructure: Ready for Action?, Carlos Buil-Aranda, Aidan Hogan, Jürgen Umbrich Pierre-Yves Vandenbussch, International Semantic Web Conference 2013, (ISWC2013).
Assessing the Educational Linked Data Landscape, D’Aquin, M., Adamou, A., Dietze, S., ACM Web Science 2013 (WebSci2013), Paris, France, May 2013.
Type Inference on Noisy RDF Data, Paulheim H., Bizer, C. Semantic Web – ISWC 2013, Lecture Notes in Computer Science Volume 8218, 2013, pp 510-525
An empirical survey of Linked Data conformance. Hogan, A., Umbrich, J., Harth, A., Cyganiak, R., Polleres, A., Decker., S., Journal of Web Semantics 14, 2012
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20. Issues (2/3) – accepting inconsistency
Analyzing Relative Incompleteness of Movie Descriptions in the Web of Data: A Case Study, Yuan, W., Demidova, E., Dietze, S., Zhu, X., International Semantic Web Conference 2014 (ISWC2014)
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21. Issues (3/3) – licensing/legal aspects
Dataset
Words
Pages
DBpedia
7163
16
Flickr
10367
23
ConceptNet
7163
16
World Bank
7056
16
Nature
7024
16
LinkedIn
6104
14
Google+
5740
13
Tumblr
5362
12
Twitter
4247
9
Facebook
4179
9
Mashing up data: legal and licensing related issues under-estimated
What license do you get when mashing up:
Attribution: copyright violation from missing (86%) or incorrect attribution (14%) information
Terms & conditions: complexity and conflicts when merging data from different sources
Potential non-compliance from evolution of (a) LOD applications and (b) underlying datasets (and their licenses)
T&C of established datasets
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Nature (CC0) + DBpedia (CC-ShareAlike) + FAO (Proprietary non-commercial) => ?

22. Get involved!
http://www.w3.org/community/opened
http://data.linkededucation.org/linkedup/catalog/
http://data.linkededucation.org/linkedup/devtalk/

23. Thank you!
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