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Putting Intelligence in Open Data - With examples in education


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Presentation at the FORTH Institute, Heraklion, 26-05-2014

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Putting Intelligence in Open Data - With examples in education

  1. 1. Putting intelligence With examples in education Mathieu d’Aquin (@mdaquin) Knowledge Media Institute, The Open University, UK in web data
  2. 2. Mathieu d’Aquin (@mdaquin) Knowledge Media Institute, The Open University, UK Research Fellow – Background in Artificial Intelligence, Knowledge Engineering, Reasoning Working on Semantic Web, Linked Data and Knowledge Technologies Especially applied to education and personal information management/Privacy Research Lab, ~75 people, many industrial and academic collaborations, Leader in semantic web, linked data, TEL, learning analytics, new media research Open and Distance Learning University, the biggest university in the UK in number of students (~250,000 per year), 13 regional centres, + national centres. Almost all teaching at distance. Putting intelligence With examples in educationin web data
  3. 3. Semantic Web/Linked Data Technologies?
  4. 4. The Semantic Web Connected knowledge where entities, concrete and abstract, have formal attached meaning/interpretations The Web Network of documents interconnected with hyperlinks The Linked Data Web Graph of data objects connected by labelled hyperlinks
  5. 5. Example (in education):
  6. 6. Course information: 600 modules/ description of the course, information about the levels and number of credits associated with it, topics, and conditions of enrolment. Research publications and people: 25,000 academic articles / information about authors, dates, abstract and venue of the publication. Podcasts: 2220 video podcasts and 1500 audio podcats / short description, topics, link to a representative image and to a transscript if available, information about the course the podcast might relate to and license information regarding the content of the podcast. Open Educational Resources: 640 OpenLearn Units / short description, topics, tags used to annotate the resource, its language, the course it might relate to, and the license that applies to the content. Youtube videos: 900 videos / short description of the video, tags that were used to annotate the video, collection it might be part of and link to the related course if relevant. University buildings: 100 buildings / address, a picture of the building and the sub-divisions of the building into floors and spaces. Library catalogue: 12,000 books/ topics, authors, publisher and ISBN, as well as the course related. Others… Content
  7. 7. owl:sameAs offers location (Ireland) niversity
  8. 8. SPARQL Show some basic slides there Front page Sparql endpoint Update follow up application slide Discou – CRC – tackboard – REF - openlearn select distinct ?q (count(distinct ?t) as ?n) where { ?q a <>. ?q <> ?p. ?p <> ?s. {{?s <> ?c} union {?s <> ?c}}. ?c <> ?t. [] <> ?t. } group by ?q order by desc(?n) List of courses (degrees, etc.) at The Open University, with number of topics they cover
  9. 9. Simple example Interactive map of Open University Buildings in the UK
  10. 10. Spaces Floors ID Address Post- code Buildings build 1 build1- address Postcode- mk76aa name “Berrill building” Milton Keynes inDistrict Buckingha mshire inCounty Mk76aa- location location lat long 52.024924 -0.709726
  11. 11. Many other (simple) applications
  12. 12. Data Linked Data The Semantic Web
  13. 13. Gene Ontology FMA Ontology LODE BIBO Geo Ontology DBPedia Ontology Dublin Core FOAF DOAP SIOC Music Ontology Media Ontology rNews Ontologies
  14. 14. Example in education: DiscOU See | d'aquin et al @ Demo ISWC 2012
  15. 15. Example in research: The Listening Experience Database Project with Royal College of Music and the Open University's Art Faculty Goal: Create a large database of evidence of people listening to music (of any genre, at any time, in any place) See
  16. 16. Linked data and ontologies to support data crowd- sourcing
  17. 17. Results ~700 contributions so far
  18. 18. Data/Information/Knowledge on the Semantic Web NLP Information retrieval Recommender Systems Data Mining Step further: intelligent applications and knowledge discovery
  19. 19. The Linked Data Web Graph of data objects connected by labelled hyperlinks The Semantic Web Connected knowledge where entities, concrete and abstract, have formal attached meaning/interpretations Intelligent Web information and knowledge processing Discovering knowledge models
  20. 20. Example in Education: The LinkedUp Data Catalogue See See d'Aquin et al in ERCIM News 96
  21. 21. Summary visualisation of extracted schema
  22. 22. FCA-based technique to extract URI patterns in RDF datasets
  23. 23. Interactive mapping onto canonical ontologies
  24. 24. See d'Aquin et al @ WebSci2013 Getting a top level view of an area through its datasets … and looking at relationships between datasets (see the Datanode ontology)
  25. 25. Example(s) in Education: Learning Analytics Location of students showing particular interest based on their enrolment into courses
  26. 26. ID course post- code Students .uk Topics data.ordnancesurvey. Districts Location Clustering Other resources DBpedia Geonames
  27. 27. How to interpret the results? See d'Aquin and Jay @ LAK2013 Sequence mining to find common study pathways and FCA_Linked Data to interpret them
  28. 28. Can we use linked data automatically to explain data patterns? See Tiddi et al. @ ESWC2014 Taking inspiration from ILP: Interest in studying Health and Social Care Positive examples Negative examples Swansea East London Machester Milton Keynes Sheffield Brighton Southampton Bristol … …. Background Knowledge?
  29. 29. Linked Data Traversal See Tiddi et al. @ ESWC2014 Swansea Manchester Sheffield Southampton East London Milton Keynes Bristol Brighton 51.2 -2.3 Dbpedia:Milton_Keynes Dbpedia:Labour 241K yago:unitary_authority opencyc:unitary_authority freebase:Bristol 198K 350mm Dbpedia:Bristol yago:city opencyc:city Dbpedia:Tory Dbpedia:Southampton freenase:Southampton 240mm 270K 290K sameAs sameAs sameAssameAs sameAs sameAs type type long lat poppop pop pop pop party party party rain rain
  30. 30. SummaryIntelligent information processing The Semantic Web Linked Data Web The Web Making smart thing with what we can find in the web Naturally integrated data, flexible model for rapid development Large scale, collaborative, distributed, uncontrolled Connected, decentralised, independent
  31. 31. Future Understand this Make explicit the competence of data in being used at the upper level, what is being done to it when going from raw to processed. Formalise the practice level in addition to the symbol, syntax and semantic levels, to boost development benefits. Create generic, standard processes for the development of intelligence semantic web systems.
  32. 32. Future And build more with it... New environments even more demanding – more sophistication and intelligence required! See
  33. 33. Thank You! More at: @mdaquin These slides at: Team: Ilaria Tiddi Alessandro Adamou Enrico Daga Keerthi Thomas
  34. 34. More complex reasoning example (in personal data management): Epistemic reasoning for privacy on Facebook • Screenshot See d'Aquin and Thomas @ Demo ISWC 2013
  35. 35. Facebook graph API Basic linked data Facebook Ontology Ontological inference (types, relations) Epistemic logic theory of Facebook Epistemic inference (who knows what)
  36. 36. Facebook Ontology (extract) Person Post Photo Video Status update Comment Agent App subclass author likes includes subclass author on Place in {Everyone, Friends_of_Friends, All_Friends, Custom} scope
  37. 37. Example epistemic rules Ka Post(X) :- author(X, a) Ka Post(X) :- scope(X, All_Friends), author(X, Y), friend(Y, a) Ka Post(X) :- includes(X,Y), friend(Y, a) Ka wasIn(P, Y) :- includes(X,Y), in(X,P), Ka Post(X) Ka wasWith (Y,Z) :- includes(X, Y), includes(X, Z), Ka Post(X)