1. Interlinking educationalresourcestowards the Web of Linked Learning?Miguel-Angel SiciliaUniversity of Alcal (Madrid)msicilia@uah.es

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-timelecturing. Now professor, University of Alcal Technical coordinator of LUISA (FP6), coord. of VOA3R(ICT PSP), agINFRA (FP7), SEMAGROW (FP7) Service: Board member & TG LOD leader, EuroCRIS EIC Emeralds Program journal and Indersciences IJMSO. EB member Interactive Learning Environments, TheElectronic Library, IJSWIS and others. 3. Contents What is Linked Open Data The Organic.Edunet & VOA3R cases forLOD The general case Exploring the possibilities for interlinking Roadmap 4. Linked Open Data? What is LOD? A movement of people, organizations andnetworks towards making data in generalmore readily available on the Web. A set of technological conventions to makedata available for machines (=software). A evolution(/simplification) of the idea of the(Semantic) Web. In a loose sense, a field of research framedin the idea of Web Science. 5. Linked Data Principles1. Use URIs as names for things.2. Use HTTP URIs so that people canlook up those names.3. When someone looks up a URI,provide useful RDF information.4. Include RDF statements that link toother URIs so that they can discoverrelated things.Tim Berners-Lee 2007http://www.w3.org/DesignIssues/LinkedData.html 6. The Organic.Edunet case From this. 7. The Organic.Edunet case (contd.) To this.a OrEd:resource ;lom:classification db:Classification-7924 ;lom:copyrightAndOtherRestrictions "yes" ;lom:cost "no" ;lom:keyword db:Keyword-9588-14 lom:lifecycleStatus lomvoc:Status-final ;lom:location ;lom:coverage lom:metaMetadataSchema "LOMv1.0" , "LREv3.0" ;lom:structure lomvoc:Structure-collection ;lom:relation dcterms:format "text/html" ;dcterms:language "en" ;dcterms:rights "Not specified @en" ;dcterms:title "Mezgazdasgi termnyek hlzata @hu" , "Red de cultivosagronmicos @es" , "Agronomic crops network @en" , "Agronomiskplanteproduksjonsnettverk @no " . 8. The case of VOA3R 9. a cerif:Person ;rdfs:label "Diane Le Hnaff" ;cerif:gender "f" ;cerif:internalIdentifier"ff8081813078a4dc01308979fe2c0002" ;cerif:keyword agriculture" ;cerif:linksToProject ;cerif:uri ;cerif:isAuthor The case of VOA3R 10. Limitations (from a linked dataperspective)WebAPIAAggregator (harvester orquery client)Shortcomings1. APIs provide proprietaryinterfaces OAI-PMH + SQI2. Aggregators are based ona fixed set of data sources.(not necessarily, butrequire some registry ofproviders)3. You can not set hyperlinksneither between learningobject descriptions norfrom them to other dataor terminologies (theycan be there somehow butnot interpreted as such)WebAPIBWebAPICWebAPIDAdapted from: Bizer:, C.- The Web of Linked Data (2009) 11. Browsing & queryingAdapted from: Christian Bizer: The Webof Linked Data (26/07/2009)B CLOtypedlinksA D EtypedlinkstypedlinkstypedlinksLOTermTermLOLO LOLOTermTermQuery resolverBrowserTerm(s) 12. The LOD cloud13 13. Another view of the LOD cloud14 14. Is that available?15Around 10% of links from DBPedia are broken (onaverage) 15. The steps towards the Web ofLinked 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 interlinkedmaterials. 16. GLOBE Materials17 GLOBE(Global Learning Objects BrokeredExchange) enables share and reusebetween several learning object repositories We harvested GLOBE through OAI-PMH andgot around 770,000 metadata records (IEEELOM) Just to test the possibilities of interlinking 17. GLOBE materials: Keywords Around 5,5 million keywords in the sample (~7keywords per resource) Large number of keywords generated via machinetranslation (referenced by codes starting with x-mt-) Frequencies are high for relatively high number ofkeywords (beyond 15) (might be attributed to automated extraction)18 18. GLOBE materials: Classification19 A total of ~ 700k classifications distributedacross ~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 classificationpurposes, with discipline 60% and Technicaldesign around 18%. The latter is from a vocabulary specific of the MACE project.11% of the purposes were blank. Keywords and classifications were matchedagainst each other for the same resources (~270k coincidences) This says that 38% of classifications look redundant from alexical perspective. 19. Possibilities of interlinking Checking exact lexical match of keywordsand classifications with DBPedia givearound 30% and 33% of matches. Note this is just a non-informed approach on asample. Indicator of hundreds of thousands links. Interlinking exercise with Limes (on500.000 GLOBE records): For coverage to DBPedia or FAO countriesdataset: ~9000 (98% threshold) 20. Conclusions Linked data for educational resourcesextends current investment for bettermachine processing. And for discovering related things! The technology is there, but theapplications still not. Now it is time to put the efforts in exposing. There is a huge potential for interlinking only in keywords and classification!