This document outlines the goals and tangible outcomes of the LinkedUp project. The project aims to demonstrate success stories of open web data applications, establish an evaluation framework for such applications, and facilitate technology transfer in the education sector. It will run a multi-stage challenge and establish a large-scale data testbed. The challenge aims to produce highly innovative and evaluated applications using web-scale data to address educational scenarios. The project expects to increase collaboration, disseminate best practices, and raise awareness of open web data through technology transfer activities.
2. Motivation
Data on the Web
Some eyecatching opener illustrating growth and or diversity of web data
LinkedUp: Linking Web Data for Education Project
– Open Challenge in Web-scale Data Integration
Stefan Dietze
(L3S Research Center, DE)
Stefan Dietze 06/11/12 2
4. TEL data vs Linked Open Data
TEL data on the Web
Open Educational Resource (OER) metadata & MOOC
collections
(e.g. OpenCourseware, OpenLearn, Merlot, Coursera)
Competing Web interfaces (e.g. OAI-PMH, SOAP, REST)
Competing metadata standards (e.g. IEEE LOM, ADL
SCORM, DC…) & taxonomies & exchange formats (JSON,
RDF, XML)
Issues: heterogeneity & lack of interoperability
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5. TEL data vs Linked Open Data
TEL data on the Web Linked Open Data
Open Educational Resource (OER) metadata & MOOC Vision: well connected graph of open Web data
collections
(e.g. OpenCourseware, OpenLearn, Merlot, Coursera) W3C standards (RDF, SPARQL) to expose data, URIs
to interlink datasets
Competing Web interfaces (e.g. OAI-PMH, SOAP, REST)
=> vast cloud of interconnected datasets
Competing metadata standards (e.g. IEEE LOM, ADL
SCORM, DC…) & taxonomies & exchange formats (JSON, Crossing all sorts of domains
RDF, XML)
32 billion triples (September 2011)
Issues: heterogeneity & lack of interoperability
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6. TEL data vs Linked Open Data
Linked Data for Education Linked Open Data
Relevant knowledge and data Vision: well connected graph of open Web data
Publications: ACM, PubMed, DBLP (L3S), OpenLibrary W3C standards (RDF, SPARQL) to expose data, URIs
(Cross-)domain knowledge & resources: Bioportal for Life to interlink datasets
Sciences, historic artefacts in Europeana, Geonames, => vast cloud of interconnected datasets
DBpedia, Freebase, …
Crossing all sorts of domains
Media resource metadata: BBC, Flickr, …
32 billion triples (September 2011)
Explicit educational data
University Linked Data: eg The Open University UK,
http://data.open.ac.uk, Southampton University, …
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 OER schema (
http://purl.org/meducator/ns)
=> http://linkededucation.org; http://linkeduniversities.org
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7. Slow take-up => crucial
challenges:
Scalability, performance &
robustness
(in large-scale data
environments)
Licensing & legal issues
Web data quality and
consistency
Benchmarking & evaluation
…
RecSys (Linked) Web Data
TEL
8. LinkedUp in a nutshell
Challenge and evaluation framework aimed at:
LinkedUp
Leap in robustness/scalability of (Big) data integration technologies
Web
data
submissi
on data
(data analytics, mining, storage, analysis)
Real-world use case: Web-based education facilitated by open Web data
Personal
data
What? Stage 1-
Initialisation
Initialisation
When? 3 stages of the LinkedUp competition
LinkedUp Challenge Environment
How? • Lowest requirements level for participation
• LinkedUp Evaluation Framework
• Inital prototypes and mockups, use of data
…
n
o
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t
a
p
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testbed required • Methods and Test Cases
Stage 2 • 10 to 20 projects are expected • LinkedUp Data Testbed
• Competitor ranking list
• Medium requirements level for participation …provides:
• Working prototypes, minimum amount of
LinkedUp Support Actions Legal & technical
data sources, clear target user group
Stage 3 • 5 to 10 projects are expected • Dissemination (events, training) guidance
a
i
r
e
t
i
r
c • Data sharing initiatives Data & use cases
• Deployment in real-world use cases • Community building & clustering
• Sustainable technologies, reaching out • Technology transfer Evaluation
to critical amount of users,
Stage 4 • 3 to 5 projects are expected
• Cashprice awards & consulting results
! 2 years ! Financial awards
E
P S
P F …
Network of supporting organisations T
I
(see 3.2 Spreading excellence, exploiting results, disseminating knowledge) S E
C B
C O
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9. LinkedUp consortium
(Scientific) expertise in three strategic areas
Data integration, Web
technologies & evaluation
Educational technologies,
(meta)data and resources
Dissemination and exploitation
of open Web data
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10. LinkedUp consortium
(Scientific) expertise in three strategic areas
L3S Research Center, Leibniz University, DE
Elsevier, NL
Leading institute in Web science &
Leading scientific & educational publisher
data technologies as well as
Innovative research on the future of publishing &
technology-enhanced learning
extensive experience in data competitions
Strong experience in coordinating EC R&D
CELSTEC, The Open University, NL projects
R&D institute in educational technologies and part of the
largest distance university in the netherlands
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)
KMI, The Open University, UK
Leading R&D institute in areas related to LinkedUp
World’s largest distance university (over 200.000
students)
Exact Learning Solutions, IT
SME in educational technologies and services with
long-standing experience in (EC-funded) R&D projects
11. LinkedUp network/associated partners
Persistent “LinkedUp Network”(community of industrial and academic institutions)
Commonwealth of Learning, COL (CA)
Athabasca University (CA)
International
Talis Group (UK) (outside Europe)
SURF NL (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)
11 18/09/12
Stefan Dietze
12. Advisory Board
Dan Brickley
Google, UK & W3C
Schema.org / Learning Resource
Metadata Initiative
FOAF project
Sören Auer
Venkataraman Balaji
Agile Knowledge Engi-
Director, Technology &
neering and Semantic
Knowledge Management
Web (AKSW) group leader, University
Commonwealth of Learning – of Leipzig
http://col.org DBpedia, Coordinator of LOD2 project
Philippe Cudré-Mauroux
Head of eXascale Infolab
University of Fribourg,
Switzerland
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13. Previous collaborations
R&D projects & events/initiatives
R&D Projects
Events & Initiatives
EC IP OKKAM: Web entity LILE: Linked Learning (Linked Data for
identification & discovery Education) workshop series
EC BPN mEducator:
Integration of educational LALD: Learning Analytics and Linked
resources based on LOD Data workshop (series)
EC STREP LUISA: Semantic LinkedEducation
Web technologies for http://linkededucation.org
sharing of OER
EC NoE STELLAR: LinkedUniversities
educational Web http://linkeduniversities.org
technologies network
Joint special issues related to LinkedUp
OpenScout: promotion of (Semantic Web Journal and ILE)
use of open educational
content European Association for Technology-
enhanced Learning (EATEL)
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14. Other related initiatives from LinkedUp partners
Large-scale challenges & competitions Web data dissemination and events
Open Data Challenge The Open Knowledge Conference (OKCon):
(http://opendatachallenge.org/): Europe‘s annual open knowledge conference run by
largest open data competition, 430 OKFN
submissions from 24 member states DataTEL theme team: gathering of open
Elsevier Grand Challenge data within education (OUNL)
(http://www.elseviergrandchallenge.com): Open Government Data Camp:
communication of scientific information. http://ogdcamp.org/
Semantic Web Challenge Open Data Handboook
(http://challenge.semanticweb.org/) large- http://opendatahandbook.org/: living online
scale Semantic Web data applications manual for basic concepts of ‘open data’
Semantic Web Service Challenge Topical working groups and hackdays, eg
(http://sws-challenge.org) : evaluation of http://okfn.org/wg/
semantic web service technologies
Data catalogues & (educational) datasets
CKAN: The Data Hub, the most important registry of open
knowledge datasets (hosted and managed by OKFN).
LUCERO, http://data.open.ac.uk: first extensive Linked Data
university dataset, approach adopted by many universities
mEducator Linked Educational resources: one of first OER
datasets in Linked Data cloud (LUH, OUUK)
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16. Goals
Objective 1
Open Web
Data Success
Stories
evaluate create
support
demonstrate create support
support demonstrate
Educational Web data &
technologies
Objective 2
support Objective 3
Evaluation evaluate create Technology
Framework for
demonstrate / support Transfer in the
Open Web
Education
Data
Sector
Applications evaluate
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17. Goals & tangible outcomes Web
data
LinkedUp
submissi
on data
Personal
data
Stage 1-
Initialisation
Initialisation
Competition framework & community 3 stages of the LinkedUp competition
LinkedUp Challenge Environment
• Lowest requirements level for participation
• LinkedUp Evaluation Framework
Evaluation framework for large-scale • Inital prototypes and mockups, use of data
n
o
p
c
i
t
r
a
P
testbed required • Methods and Test Cases
Stage 2 • LinkedUp Data Testbed
Web data applications and data • 10 to 20 projects are expected
• Competitor ranking list
Objective 1• Medium requirements level for participation
(metrics, methods, benchmarks)
Open Web • Working prototypes, minimumgroup of
data sources, clear target user
amount
LinkedUp Support Actions
Stage 3 • 5 to 10 projects are expected
Large-scale data testbed of quality- Data Success • Dissemination (events, training)
a
e
t
i
r
c
• Data sharing initiatives
Stories • Deployment in real-world use cases
assessed datasets • Sustainable technologies, reaching out
• Community building & clustering
• Technology transfer
to critical amount of users,
Stage 4 • 3 to 5 projects are expected
• Cashprice awards & consulting
evaluate create P S
E
T P F
Network of supporting organisations I
support
(see 3.2 Spreading excellence, exploiting results, disseminating knowledge) S B
E
demonstrate create support C
O
C
support demonstrate
Periodic/continuous challenge
Educational Web data &
technologies
Objective 2
support Objective 3
Evaluation evaluate create Technology
Framework for
demonstrate / support Transfer in the
Open Web
Education
Data
Sector
Applications evaluate
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18. Goals & tangible outcomes
Challenge & evaluation framework (WP1, WP2)
LinkedUp in a nutshell
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19. Goals & tangible outcomes
Data curation & “testbed” (WP3): initial ideas
Educational data gathering - community-approach: Linked Education cloud
“LinkedUp/Linked Education cloud” as subset of LOD cloud
CKAN – “The DataHub” (ckan.net) for data collection (analogous to LOD approach)
Dedicated group (“linked-education”) for cataloging educational datasets
Educational Data
Educational data integration & infrastructure: Linked Education graph
Linked Education cloud => Linked Education graph
Integration of (selected) datasets into coherent (RDF) dataset
Infrastructure and unified (SPARQL) endpoint for LinkedUp challenge
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20. Goals & tangible outcomes
LinkedUp in a nutshell Highly innovative, evaluated applications
of large-scale Web data
“LinkedUp Challenge” offers incentive and
support to steer submissions
Objective 1
Open Web Educational scenario: (a) challenging vision
Data Success and (b) real-world scenario and
Stories requirements
evaluate create
support
demonstrate create support
support demonstrate
Educational Web data &
technologies
Objective 2
support Objective 3
Evaluation evaluate create Technology
Framework for
demonstrate / support Transfer in the
Open Web
Education
Data
Sector
Applications evaluate
Stefan Dietze 06/11/12 20
21. Goals & tangible outcomes
Success stories: in both research & practice
Technical achievements & progress in, e.g. End-user applications facilitated by Open Data & resources
Information Retrieval tasks (performance, scalability) Tutoring systems (course/resource development) &
Data integration (eg schema mapping, data interlinking, educational resource sharing and discovery solutions
entity co-reference resolution) Certificate-level Web education offerings
Characteristics
Specific & constrained challenge tasks & datasets Characteristics
Evaluation with traditional quantitative measures Open & less constrained challenge tasks (eg use cases) and
(precision, recall, response times, … ) data
Impact primarily scientific (at least in short-term) Evaluation via qualitative and quantitative criteria
Impact on academia, industry, society
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22. Goals & tangible outcomes
in a nutshell
Technology transfer, increase in
collaboration and awareness
(best practices, clusters/communities,
Objective 1 events)
Open Web Transfer of innovative R&D results
Data Success
Stories Increase in awareness about open Web
data and scalable data integration
evaluate create
methods
support
demonstrate create support
support demonstrate
Educational Web data &
technologies
Objective 2
support Objective 3
Evaluation evaluate create Technology
Framework for
demonstrate / support Transfer in the
Open Web
Education
Data
Sector
Applications evaluate
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23. Exploitation, dissemination, sustainability
Dissemination events & platforms Clustering
Joint clustering activities with
Viral dissemination channels
Showcases & tutorials collocated with
relevant conferences related organisations (“LinkedUp Sharing of publications via Mendeley,
(WWW, ISWC, ESWC, ICDE, LAK etc) Network”) … Research Gate, CiteULike,
System demonstrations …and EC-funded R&D projects, Academia.edu
Topical hackdays such as Advertisement of slides, showcases
LILE, LALD, DataTEL workshop series LOD2 and demo videos on Slideshare,
ARCOMEM Youtube, Videolectures.net, Vimeo
(established, persistent and growing
communities) SEALS, etc. Social network channels such as
Open Knowledge Conference OKCON Twitter, LinkedIn
LinkedEducation.org, Source code sharing via Source Forge
LinkedUniversities.org Use of open licensing schemes (CC)
Standardisation
Participation/support of standardisation of
schemas and technologies through working
groups (eg W3C or http://okfn.org/wg/)
Data catalogues (eg CKAN) and community data
portals (eg http://bibsoup.net/)
Standardisation initiatives and working groups
(eg Creative Commons LRMI)
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