Linked data provides opportunities for sharing educational data on the web in a standardized way. It allows for the integration of heterogeneous educational resources and datasets from different platforms. This can enable new applications like cross-platform recommender systems and exploratory search. However, there are also challenges to address like annotation overhead, performance, and scalability when dealing with large amounts of distributed data.

1. Motivation
Data on the Web
Some eyecatching opener illustrating growth and or diversity of web data
Using Linked Data in Education – Opportunities,
Challenges, Examples
Stefan Dietze
(L3S Research Center, DE,
@stefandietze,
http://purl.org/dietze)
Stefan Dietze 13/05/13

2.  De-facto standard for sharing data on the Web
 Vision: well connected graph of open Web data
 W3C standards (RDF, SPARQL) to expose data
 Persistent URIs to interlink datasets
Linked Data
Domain
Number of
datasets
Triples % (Out-)Links %
Media 25 1,841,852,061 5.82 % 50,440,705 10.01 %
Geographic 31 6,145,532,484 19.43 % 35,812,328 7.11 %
Government 49 13,315,009,400 42.09 % 19,343,519 3.84 %
Publications 87 2,950,720,693 9.33 % 139,925,218 27.76 %
Cross-domain 41 4,184,635,715 13.23 % 63,183,065 12.54 %
Life sciences 41 3,036,336,004 9.60 % 191,844,090 38.06 %
User-generated
content
20 134,127,413 0.42 % 3,449,143 0.68 %
295 31,634,213,770 503,998,829
Source: http://lod-cloud.net/state, September 2011
Media
Ontology
FOAF
Gene
Ontology
FMA
Ontology
BIBO
Geo
Ontology
DBpedia
Ontology
Dublin
Core
rNews

3. Linked Data for Education
Why is it useful?
1. Linked Data as body of knowledge for education & TEL recommender sytems:
 vast amount of publicly available resources and data
 HTTP access according to state of the art principles
2. Linked Data as set of principles for data sharing:
 to improve interoperability of educational data
 facilitate learning analytics and recommender system
scenarios across isolated platforms
 Interlinking educational Resources and the Web of Data – a
Survey of Challenges and Approaches
Stefan Dietze, Salvador Sanchez-Alonso, Hannes Ebner, Hong Qing
Yu, Daniela Giordano, Ivana Marenzi, Bernardo Pereira Nunes,
Emerald Program: electronic Library and Information Systems,
Volume 47, Issue 1 (2013).
 Linked Data for Open and Distance Learning
Mathieu d’Aquin, report for the Common Wealth of Learning,
Stefan Dietze 13/05/13

4. Educationally relevant data, eg for informal learning
 Publications & literature: ACM, PubMed, DBLP (L3S), OpenLibrary
 Domain-specific knowledge & resources: Bioportal for Life Sciences,
historic artefacts in Europeana, Geonames
 Cross-domain knowledge: DBpedia, Freebase, …
 (Social) media resource metadata: BBC, Flickr, …
LD as body of knowledge for education
Stefan Dietze 13/05/13

5. Educationally relevant data, eg for informal learning
 Publications & literature: ACM, PubMed, DBLP (L3S), OpenLibrary
 Domain-specific knowledge & resources: Bioportal for Life Sciences,
historic artefacts in Europeana, Geonames
 Cross-domain knowledge: DBpedia, Freebase, …
 (Social) media resource metadata: BBC, Flickr, …
Explicitly educational datasets and schemas
 University Linked Data: eg The Open University UK,
http://data.open.ac.uk, Southampton University, University of
Munster (DE), http://education.data.gov.uk
 OER Linked Data: mEducator Linked ER
(http://ckan.net/package/meducator), Open Learn LD
 Schemas: Learning Resource Metadata Initiative (LRMI,
http://www.lrmi.net/), mEducator Educational Resources schema
(http://purl.org/meducator/ns)
 http://linkededucation.org &
 http://linkeduniversities.org
LD as body of knowledge for education
Stefan Dietze 13/05/13

6. LD for educational data/resource sharing
Challenges of educational resource sharing
Issues
 Heterogeneity & lack of interoperability
 Lack of take-up
 Vast Open Educational Resource (OER) & MOOC metadata collections
(e.g. OpenCourseware, OpenLearn, Merlot, ARIADNE)
 Usually exposed via APIs/services
 Competing Web interfaces (e.g. SQI, OAI-PMH, SOAP, REST) & metadata standards (e.g. IEEE LOM,
ADL SCORM, DC…)
 Competing exchange formats and serialisations (e.g. JSON, RDF, XML)
 Fragmented use of taxonomies

7. LD for educational data/resource sharing
Overview
Approaches for LD in educational data sharing
 On the-fly/automated integration of heterogeneous APIs and data (http://www.meducator.net)
 Dataset (transformation and) cataloging (http://linkedup-project.eu)
?

8. LD for integration of OER repositories
Use case: biomedical education in
=> http://metamorphosis.med.duth.gr/
Metamorphosis+ Tailored (L)CMS plugins
=> http://www.meducator3.net/
Data/services integration & retrieval/search APIs
Educational Web Resources
Schemas: OAI-DC, LOM, …
Formats: XML, JSON
Interfaces: OAI-PMH, REST, SOAP
?

9. Data/services integration & retrieval/search APIs Linked Educational Resources
 http://linkededucation.org/meducator
 Approach: 1) On the fly queries via “SmartLink” (Linked Data registry execution engine for open APIs)
2) Data lifting from heterogeneous repositories using “SmartLink” API and lifting
specifications
3) Data enrichment (via DBpedia, Freebase, BioPortal) & clustering, eg to identify correlated
resources
 Goal: improvement of distributed (non-LD) data with public LOD vocabularies; tighter interlinking to
provide coherent graph of educational data (across disparate stores)
http://purl.org/smartlink
Schemas: OAI-DC, LOM, …
Formats: XML, JSON
Interfaces: OAI-PMH, REST, SOAP
LD for integration of OER repositories
Educational Web Resources
 Dietze, S., Yu, H. Q., Giordano, D., Kaldoudi, E., Dovrolis, N., Taibi, D.,
Linked Education: interlinking educational Resources and the Web
of Data, in Proceedings of the 27th ACM Symposium On Applied
Computing (SAC-2012), Special Track on Semantic Web and
Applications, Riva del Garda (Trento), Italy, 2012.

10. SmartLink: data lifting into RDF (via lifting templates)
Database of over 22 M records of biomedical resources (12 M full text)
=> www.pubmed.gov
Dereferencable RDF resource description
lifted via SmartLink:
http://purl.org/meducator/resources/25a8c581-
66d7-4186-9411-f9f0f783463e
Stefan Dietze 13/05/13
original XML resource description
retrieved via API request

11. ?
Data enrichment via DBpedia & Freebase
Semi-structured RDF
description of
educational resource
12/03/13Stefan Dietze

12. ?
Data enrichment via DBpedia & Freebase
Semi-structured RDF
description of
educational resource
12/03/13Stefan Dietze

13. ?
?
Data enrichment via DBpedia & Freebase
?
?

14. !
!
Data enrichment via DBpedia & Freebase
NER & disambiguation,
eg, via

15. db:Viral
Infectionsdb:Human
Papilloma Virus
db:Life
Sciences
<led:Resource-OpenLearn-2139393292>
<led:title>…viral…disease…</led:title>
…
</led:Resource-OpenLearn-2139393292>
<led:Resource-BBC-519215>
<led:title>…virus…</led:title>
…
</led:Resource-BBC-519215>
LD for integration of OER resources
LD vocabularies for disambiguation & clustering
<led:Resource-mEducator-2139393292>
<led:title>Virtual patient 1002,
infections & HPV</led:title>
…
</led:Resource-mEducator-2139393292>
db:Disease
Stefan Dietze 13/05/13
Nunes, B. P., Dietze, S., Casanova, M.A., Kawase, R., Fetahu, B., Nejdl,
W., Combining a co-occurrence-based and a semantic measure for
entity linking, ESWC 2013 – 10th Extended Semantic Web Conference,
Montpellier, France, May (2013).

16. Number of resources per DBpedia reference/enrichment (subject) in mEducator dataset
DBpedia concept (http://dbpedia.org/resource/....) Linked by number of
resources
Cervical_cancer 59
Screening 31
Cervical 29
Hpv 29
Oxygenation 26
Childhood 22
differential_diagnosis 19
Knowledge 18
Learning 17
decision_making 16
Training 15
Lecture 15
Risk 15
hpv_infection 15
Fear 15
pap_smear 15
Abnormal 14
Ventilation 14
Ecg 14
..) Linked by number of
resources
59
31
29
29
26
22
19
18
17
16
15
15
15
15
15
15
14
14
14
Structural clustering of related resources
DBpedia references used most frequently to describe the
„subject“ of particular educational resources
(extracted via out of the box NER tools)
Stefan Dietze 13/05/13

17. DBpedia concept (http://dbpedia.org/resource/....) Linked by number of
resources
Cervical_cancer 59
Screening 31
Cervical 29
Hpv 29
Oxygenation 26
Childhood 22
differential_diagnosis 19
Knowledge 18
Learning 17
decision_making 16
Training 15
Lecture 15
Risk 15
hpv_infection 15
Fear 15
pap_smear 15
Abnormal 14
Ventilation 14
Ecg 14
..) Linked by number of
resources
59
31
29
29
26
22
19
18
17
16
15
15
15
15
15
15
14
14
14
Clustering of resources graph (blue nodes: resources, green nodes: enrichments)
Structural clustering of related resources
Cluster of educational resources
relating to „cervical cancer“ subject
Number of resources per DBpedia reference/enrichment (subject) in mEducator dataset
Stefan Dietze 13/05/13

18. Using LD in educational recommender systems
Cross-plattform recommendation in medical education
=> http://metamorphosis.med.duth.gr/
Metamorphosis+
 Dietze, S., Kaldoudi, E., Dovrolis, N., Yu, H.Q., Taibi, D. (2011)
MetaMorphosis+ – A social network of educational Web
resources based on semantic integration of services and data,
10th International Semantic Web Conference (ISWC2011), Bonn,
Germany

19. Enrichment: semi-automated
http://metamorphosis.med.duth.gr/
Metamorphosis+
Example: OER annotation in MetaMorphosis+
Stefan Dietze 13/05/13
 Dietze, S., Kaldoudi, E., Dovrolis, N., Yu, H.Q., Taibi, D. (2011)
MetaMorphosis+ – A social network of educational Web
resources based on semantic integration of services and data,
10th International Semantic Web Conference (ISWC2011), Bonn,
Germany

20. Access to 324 ontologies
and over 5 Mio entities
http://bioportal.bioontology.org/
Enrichment: semi-automated
http://metamorphosis.med.duth.gr/
Metamorphosis+
2. Suggested Entities
3. Selected entities from BioPortal used to describe discipline, keywords of resource
1. User-specified term during
learning resource annotation
Stefan Dietze 13/05/13
 Dietze, S., Kaldoudi, E., Dovrolis, N., Yu, H.Q., Taibi, D. (2011)
MetaMorphosis+ – A social network of educational Web
resources based on semantic integration of services and data,
10th International Semantic Web Conference (ISWC2011), Bonn,
Germany

21. Exploratory search enabled via clustering
Example: search results of OER in MetaMorphosis+
http://metamorphosis.med.duth.gr/
Metamorphosis+
Educational resources retrieved
from heterogenous repositories
based on particular user query
Stefan Dietze 13/05/13
 Dietze, S., Kaldoudi, E., Dovrolis, N., Yu, H.Q., Taibi, D. (2011)
MetaMorphosis+ – A social network of educational Web
resources based on semantic integration of services and data,
10th International Semantic Web Conference (ISWC2011), Bonn,
Germany

22. Exploratory search enabled via clustering
http://metamorphosis.med.duth.gr/
Metamorphosis+
Related resources from heterogeneous
repositories, ranked according to their similarity
Example: search results of OER in MetaMorphosis+
 Dietze, S., Kaldoudi, E., Dovrolis, N., Yu, H.Q., Taibi, D. (2011)
MetaMorphosis+ – A social network of educational Web
resources based on semantic integration of services and data,
10th International Semantic Web Conference (ISWC2011), Bonn,
Germany

23. Data/services integration & retrieval/search APIs Linked Educational Resources
 http://linkededucation.org/meducatorhttp://purl.org/smartlink
Schemas: OAI-DC, LOM, …
Formats: XML, JSON
Interfaces: OAI-PMH, REST, SOAP
LD for integration of heterogeneous APIs & data
Some issues/challenges
On-the-fly data integration, but issues wrt:
 Annotation and description overhead: data lifting requires well-defined lifting specs for each API
 Performance: distributed queries (multiple HTTP requests), on-the fly data lifting and processing
 Scalability: decrease of query performance with increasing amount of repositories and/or data
Educational Web Resources

24. <dc:title> <akt:has-title>
?
OER
Publication
VideoLecture
LinkedUniversities
educational videos
Step 1 – Alignment of types/properties
12/03/13 24Mathieu d‘Aquin, Stefan Dietze
Large-scale data harvesting and LD-ification
Linked Data for automated cross-platform integration
 6 million distinct (but linked) resources
 97 million RDF triples
 21.6 GB of data
 Schema: http://data.linkededucation.org/ns/linked-education.rdf
 SPARQL: http://data.linkededucation.org/request/linked-learning/sparql
LD and non-LD data
Step 2 – Linking of resources

25. <dc:title> <akt:has-title>
?
OER
Publication
VideoLecture
LinkedUniversities
educational videos
Step 1 – Alignment of types/properties
12/03/13 25Mathieu d‘Aquin, Stefan Dietze
Large-scale data harvesting and LD-ification
Linked Data for automated cross-platform integration
 6 million distinct (but linked) resources
 97 million RDF triples
 21.6 GB of data
 Schema: http://data.linkededucation.org/ns/linked-education.rdf
 SPARQL: http://data.linkededucation.org/request/linked-learning/sparql
LD and non-LD data
Step 2 – Linking of resources
Larger scale data processing, but issues wrt:
 Scalability and performance of data storage
(potential solutions: applying distributed RDF storage, map/reduce etc)
 Poor query performance (on large-scale datasets)
 Redundant data maintenance => periodic data imports
 Davide Taibi, Besnik Fetahu and Stefan Dietze. – “Towards
Integration of Web Data into a coherent Educational Data
Graph”, Linked Learning 2013 – 3rd International Workshop
on Learning and Education with the Web of Data, WWW2013
Workshop Proceedings, ACM Digital Library, 2013.

26.  EC-funded support action (11/2012 – 10/2014) aiming at
improving take-up of large-scale Web data (education as scenario)
 Three pillars:
 Linking Educational Data: data & catalog for large-scale
educational Web data applications
 LinkedUp Challenge: open data competition (over 1.5 years)
[ http://linkedup-challenge.org ]
 Evaluation Framework for open data applications
http://www.linkedup-project.eu/
LinkedUp [ http://linkedup-project.eu ]
Linking Data for Education Project

27. Stefan Dietze
LinkedUp consortium
02/04/13
http://www.linkedup-project.eu
L3S Research Center, Leibniz University, DE
 Coordinator
 Leading institute in Web science &
data technologies as well as
technology-enhanced learning
 Strong experience in coordinating R&D
projects
KMI, The Open University, UK
 Leading R&D institute in areas related to LinkedUp
 World’s largest distance university (over 200.000
students)
CELSTEC, The Open University, NL
 R&D institute in educational technologies and part of the
largest distance university in the netherlands
Exact Learning Solutions, IT
 SME in educational technologies and services with
long-standing experience in (EC-funded) R&D projects
Elsevier, NL
 Leading scientific & educational publisher
 Innovative research on the future of publishing &
extensive experience in data competitions
The Open Knowledge Foundation, UK
 Not-for profit organisation to promote open
knowledge and data; global network
 Host of key events (OKCon) and platforms (eg CKAN)

28. Commonwealth of Learning, COL (CA)
Athabasca University (CA)
Talis Group (UK)
SURF NL (NL)
Data Archiving and Networked Services, DANS (NL)
Université Fribourg, eXascale Infolab Group (CH)
Democritus University of Thrace (GR)
AKSW, Universität Leipzig (DE)
Aristotele University of Thessaloniki (GR)
CNR Institute for Educational Technologies (IT)
Clam Messina Service and Research Centre (IT)
Eurix (IT)
Ontology Engineering Group (OEG), UPM, (ESP)
18/09/1228 Stefan
International
(outside Europe)
LinkedUp network/associated partners
+ collaborators
from
…

29. http://datahub.io/dataset/meducator
LinkedUp data curation
Finding and using data
Lack of take-up due to challenges wrt:
 Scalability & performance
 Legal/licensing implications
 Quality & consistency (LOD = knowledge graph)
 Lack of reliable dataset metadata about
 Resource types
 Topics & disciplines
 Quality & availability
 Provenance
http://datahub.io/dataset/bbc
60.000.000 triples

30.  Goal: providing data consumers with descriptive metadata & search for available datasets
 “LinkedUp/Linked Education cloud” as (expanded) subset of LOD cloud at The DataHub
 Public RDF vocabulary of datasets (“Linked Education Catalog”) = dataset of datasets
(classification of datasets according to, eg, represented types, disciplines/topics, data quality, accessability)
 Integration data: links and coreferences => unified view on data => Linked Education Graph
 Infrastructure, unified (SPARQL) endpoint & APIs for distributed/federated querying
Dataset cataloging and query federation
LinkedUp approach
Educational Datasets
LinkedUp
Catalog
LinkedUp
Links
Stefan Dietze 13/05/13
Automated processing to generate:
 Descriptive Dataset Catalog
 Data Interlinking & Correlation

31. 13/05/13 31Stefan Dietze
Endpoint Retrieval
& Graph
Extraction
Schema
Extraction and
Mapping
Sample Graph
Extraction
(per dataset)
NER & NED
(per resource)
Interlinking & Co-
Resolution
(cross-dataset)
Category mapping,
normalisation,
filtering
Dataset
Catalog/Index
Links/
Cross-references
rdfs:label:„…ECB….“
?
Dataset metadata (RDF/VoID):
 Schema mappings
(types, properties)
 Entities & categories
 Topic relevance scores
 Availability, currentness
data (tbc)
dbpedia:Finance
dbpedia:Sports
dbpedia:England-Wales-Cricket-Board
dbpedia:European_Central_Bank
Dataset cataloging and query federation
LinkedUp approach

32. Linked Education Cloud & Catalog
http://datahub.io/group/linked-education
http://data.linkededucation.org/linkedup/catalog/

33. Co-occurence graph of
detected data types (in 146 datasets)
(node size: type frequency
edge: type co-occurence)
144 Vocabularies,
588 Types,
719 Properties
13/05/13 33
Assessing the Educational Linked Data Landscape,
D’Aquin, M., Adamou, A., Dietze, S., ACM Web Science
2013 (WebSci2013), Paris, France, May 2013.
Dataset cataloging and query federation
Schema assessment & mapping

34. Dataset cataloging and query federation
Co-occurence of (mapped) types
Stefan Dietze 13/05/13
Assessing the Educational Linked Data Landscape,
D’Aquin, M., Adamou, A., Dietze, S., ACM Web Science
2013 (WebSci2013), Paris, France, May 2013.
Co-occurence graph after
mapping data types
(201 most frequent types
mapped into 79 classes)

35. Dataset cataloging and query federation
Dataset graph (according to type co-occurence)
Stefan Dietze
Dataset graph
(according to type co-occurence)
13/05/13
Assessing the Educational Linked Data Landscape,
D’Aquin, M., Adamou, A., Dietze, S., ACM Web Science
2013 (WebSci2013), Paris, France, May 2013.

36. db:Viral
Infectionsdb:Human
Papilloma Virus
db:Life
Sciences
<led:Resource-OpenLearn-2139393292>
<led:title>…viral…disease…</led:title>
…
</led:Resource-OpenLearn-2139393292>
<led:Resource-BBC-519215>
<led:title>…virus…</led:title>
…
</led:Resource-BBC-519215>
<led:Resource-mEducator-2139393292>
<led:title>Virtual patient 1002,
infections & HPV</led:title>
…
</led:Resource-mEducator-2139393292>
db:Disease
Stefan Dietze
Dataset cataloging and query federation
Topic assessment via LOD
Approach
 Enriching sample
resources from
each dataset with
DBpedia
entities/categories
 Filtering &
validation
 Computation of
normalised
relevance score
13/05/13

37. Top-ranked categories/topics
in Linked Education Catalog &
their frequency
Stefan Dietze 02/04/13
DBpedia Category Total
Management 180
Academia 151
Social_sciences 131
Philosophy_of_science 125
Design 120
Sociology_index 117
Systems_science 117
Anthropology 116
Universities_and_colleges 116
Economics 114
Scientific_method 111
Cognitive_science 110
Systems 107
Sociological_terms 104
Neuropsychological_assessment 100
Concepts_in_metaphysics 96
Developmental_psychology 93
Political_philosophy 89
Cybernetics 88
Education 87
Philosophy_of_education 86
Arts 77
Critical_thinking 73
Biology 71
Political_science_terms 71
Dataset cataloging and query federation
Topic assessment via LOD

38. Enhanced Dataset Description
using Semantic Annotations
Established Dataset/Resource Correlations
based on Semantic Annotations (categories)
Indexing of Linked Data, What’s all the data
about, Fetahu, B; Adamou, A., Dietze, S., d’Aquin,
M., Nunes, B.P., ISWC2013 – 12th International
Semantic Web Conference; under review.
Dataset cataloging and query federation
Topics and dataset similarities

39. Example: Learning Analytics & Knowledge (LAK) Dataset
Extraction process
 CKAN linkededucation
 LAK tutorial
 LAK Data
 LAK challenge
 LILE2013 @ www
http://www.solaresearch.org/resources/lak-dataset/

40. Learning Analytics & Knowledge (LAK) Dataset
 A corpus of metadata and full-text of 331 learning analytics & educational
data mining publications
 Freely, openly available in variety of structured formats
 Open access as well as previously non-public resources
Publication # of papers
Proceedings of the ACM International Conference on Learning Analytics
and Knowledge (LAK) (2011-12)
66
The open access journal Educational Technology & Society special issue
on “Learning and Knowledge Analytics”: Educational Technology &
Society (Special Issue on Learning & Knowledge Analytics, edited by
George Siemens & Dragan Gašević), 2012, 15, (3), pp. 1-163.
10
Proceedings of the International Conference on Educational Data
Mining (2008-12)
239
Journal of Educational Data Mining (2008-12) 16
Special permission
from ACM
Stefan Dietze 13/05/13

41. Learning Analytics & Knowledge (LAK) Dataset
Extraction process
Stefan Dietze 13/05/1313/05/13
Taibi, D., Dietze, S., Fostering analytics on learning
analytics research: the LAK dataset,
http://resources.linkededucation.org/2013/03/lak-dataset-
taibi.pdf

42. Learning Analytics & Knowledge (LAK) Dataset
How to access?
Used schemas
Semantic Web Conference ontology
http://data.semanticweb.org/ns/swc/ontology
Linked Education schema
http://data.linkededucation.org/ns/linked-education.rdf
 Dumps in zipped RDF/XML
 Dumps in R
 Linked Data/SPARQL endpoint
http://www.solaresearch.org/resources/lak-dataset/

43. LAK Data Challenge
in a nutshellhttp://www.solaresearch.org/events/lak/lak-data-challenge/
2. Prize
iPad, 16 GB
1. Prize
iPad, 64 GB
3. Prize
iPod, 16 GB
+ additional publicity through
LinkedUp
Objectives:
 Analysis & assessment of the emerging LAK community
(topics, people, citations or connections with other fields)
 Innovative applications to explore, navigate and visualise
the dataset (and/or its correlation with other datasets)
 Usage of the dataset as part of recommender systems

44. LAK Challenge – the many faces of a small dataset
Analysis Exploration & Visualisation
Search & Recommendation Correlation & Enrichment
http://ceur-ws.org/Vol-974

45. Cite4Me – Exploiting LAK and LinkedUp data
13/05/13 45
Stefan Dietze
http://cite4me.com
Cite4Me: Semantic Retrieval and Analysis of Scientific
Publications, B. P. Nunes, B. Fetahu, and M. A. Casanova.
Proceedings of the LAK Data Challenge, held at LAK 2013, the
Third Conference on Learning Analytics and Knowledge, (2013)
 Semantic & graph search
 Clustering of researchers, teams, organisations
 Using LinkedUp data catalog & interlinking mechanisms

46. What to do with all the data: LinkedUp Challengenutshell
 Open competition running over 1.5 years
 First competition “Veni” (March 2013 - October 2013): Innovative prototypes and tools
for analysing and integrating open educational Web data
 Second competition “Vidi” (November 2013 - April 2014): Challenging and innovative,
mature data-driven applications
 Third competition “Vici” (May 2014 - November 2014): Robust applications for large-
scale educational use-cases, offered and provided by LinkedUp
 First deadline: 27 June
http://www.linkedup-challenge.org/
Stefan Dietze 13/05/13

47. Incentives
 Total prize budget of almost 40.000 EUR
 Work with large network of experts in the field of linked
open data in education
 Participate in affiliated events (Linked Learning workshop
series, satellite workshops and tutorials at major
conferences like WWW2013, LAK2013, ESWC2013)
 Present your work at the OKCon (http://okcon.org)
 Benefit from support & dedicated datasets
 Deploy your applications in real-world use cases
(BBC, Elsevier, Commonwealth of Learning…)
Stefan Dietze 13/05/13

48. Stefan Dietze 13/05/13
Evaluation & timeline
 Register already now:
http://linkedup-challenge.org
 Initial deadline: 27 June
(„Veni“ stage)
 Vedi & Vici stage to be
announced soon
 Review panel for open track
 Evaluation framework under
development

49. Summary and outlook
Summary
 Different ways of using LD in education (knowledge source, disambiguation, interlinking, enrichment)
 Vast amounts of useful data
 LinkedUp (http://linkedup-project.eu):
 Linked Education data catalog (http://data.linkededucation.org/linkedup/catalog/): Linked Data-
based catalog of open educational datasets (dataset metadata about, types, topics etc)
 On the way: exposing non-LD educational data according to LD priniciples (eg LAK dataset)
Future work
 Query federation and dedicated APIs
 Exploitation in innovative educational scenarios and applications => LinkedUp Challenge
(http://linkedup-challenge.org)
Stefan Dietze 13/05/13

50. Linked Learning 2013 @ WWW2013
in a nutshell
 CKAN linkededucation
 LAK tutorial
 LAK Data
 LAK challenge
 LILE2013 @ www
19/02/2013 50Stefan Dietze
http://lile2013.linkededucation.org/

51. References
 Interlinking educational Resources and the Web of Data – a Survey of Challenges and Approaches, Stefan Dietze,
Salvador Sanchez-Alonso, Hannes Ebner, Hong Qing Yu, Daniela Giordano, Ivana Marenzi, Bernardo Pereira Nunes, Emerald
Program: electronic Library and Information Systems, Volume 47, Issue 1 (2013).
 Assessing the Educational Linked Data Landscape, D’Aquin, M., Adamou, A., Dietze, S., ACM Web Science 2013
(WebSci2013), Paris, France, May 2013.
 Linked Education: interlinking educational Resources and the Web of Data, Stefan Dietze, Honq Qing Yu,
Daniela Giordano, Eleni Kaldoudi, Nikolas Dovrolis and Davide Taibi, ACM Symposium On Applied Computing (SAC-2012),
Special Track on Semantic Web and Applications
 As Simple As It Gets – A sentence simplifier for different learning levels and contexts
Nunes, B. P., Kawase, R., Siehndel, P., Casanova, M.A., Dietze, S., in ICALT 2013: 13th IEEE International Conference on
Advanced Learning Technologies (ICALT), Beijing, China, July 15-18 (2013).
 Fostering analytics on learning analytics research: the LAK dataset, Taibi, D., Dietze, S., Technical Report, 03/2013, URL:
http://resources.linkededucation.org/2013/03/lak-dataset-taibi.pdf
 Semantic Web Journal Special Issue on Linked Data for Science and Education. , Kessler C., d’Aquin M. and Dietze S. (eds)
http://iospress.metapress.com/content/m87017012802/
 Putting Linked Data to Use in a Large Higher-Education Organisation
Mathieu d’Aquin, Interacting with Linked Data workshop 2012
 Information Organization on the Internet based on Heterogeneous Social Networks, Kaldoudi, E., Dovrolis, N., Dietze, S.,
29th ACM International Conference on Design of Communication (ACM SIGDOC’11), Pisa, 2011.
 MetaMorphosis+ – A social network of educational Web resources based on semantic integration of services and data,
Dietze, S., Kaldoudi, E., Dovrolis, N., Yu, H.Q., Taibi, D. (2011), 10th International Semantic Web Conference (ISWC2011),
Bonn, Germany
Stefan Dietze 13/05/13

52. Thank you!
Contact
 http://purl.org/dietze | @stefandietze
See also (general)
 http://linkedup-project.eu
 http://linkedup-challenge.org
http://linkededucation.org
 http://linkeduniversities.org
See also (data)
 http://datahub.io/group/linked-education
 http://data.linkededucation.org/linkedup/catalog /
 http://www.solaresearch.org/resources/lak-dataset/
 http://datahub.io/dataset/meducator
Stefan Dietze 13/05/13