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Experience from 10 months of University Linked Data

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Presentation at IFGI, Munester, Germany, http://ifgi.uni-muenster.de/ on 15/04/2011

Presentation at IFGI, Munester, Germany, http://ifgi.uni-muenster.de/ on 15/04/2011

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  • Usual pitch: - data on the web = every piece of data is web addressable, so data across different places/stores/systems become linkable: the Web = 1 data space
  • Transcript

    • 1. Experience from 10 months of University Linked Data
      Mathieu d’Aquin - @mdaquin
      Knowledge Media Institute, the Open University
      LUCERO project
      lucero-project.info – data.open.ac.uk
    • 2. Linked Data
      As set of principles and technologies for a Web of Data
      Putting the “raw” data online in a standard, web enabled representation (RDF)
      Make the data Web addressable (URIs)
      Link with other data
    • 3. Graph (up to date)
    • 4. The Open University
      The biggest university in the UK (200,000 students)
      One of the youngest (40 years)
      Most teaching done at a distance
      1 campus, 13 regional centers
      Committed to “Open”:
      Open educational material available as podcasts (iTunes U), units of course material (OpenLearn), etc.
      Tradition of investing in new technology for teaching, learning, knowledge sharing, etc.
      Role of the Knowledge Media Institute (KMi)
    • 5. So Linked Data for the OU?
      RAE
      DBPedia
      Data from
      Research
      Outputs
      OpenLearn
      Content
      ORO
      Exposed as linked data, our data interlink with each other and the external world: become part of the “global data space” on the Web
      Archive of
      Course
      Material
      Library’s
      Catalogue
      Of Digital
      Content
      geonames
      data.gov.uk
      Currently: OU public data sit in different systems – hard to discover, obtain, integrate by users.
      A/V Material
      Podcasts
      iTunesU
      BBC
      DBLP
    • 6. Why is it important?
      The OU has been the first University to expose its data as linked data: http://data.open.ac.uk
      Now widely recognized as a critical step forward for the HE sector in the UK (and worldwide)
      Favor transparency and reuse of data, both externally and internally
      Reduces cost of dealing with our own public data: integration and reuse by design
      Enable both new kinds of applications, and to make the ones that are already feasible more cost effective
      At least 3 other UK universities have now followed our example:
      http://data.online.lincoln.ac.uk/, http://data.ox.ac.uk/, http://data.southampton.ac.uk/
      And others in other countries are setting up similar initiatives
    • 7. The data.open.ac.uk Stack
      Applications
      Institutional repository data
      Research Data (Arts)
      Organizational infrastructure
      Technical infrastructure
    • 8. data.open.ac.uk
    • 9. Expose
      Store
      Collect
      Extract
      Link
      Ontologies
      Scheduler
      Cleaning rules
      RDF file (add) RDF file (delete)
      URL redirection rules
      RSS Extractor
      Delete (1)
      Add (2)
      RDF Cleaner
      Web Server
      ORO, podcast
      RSS feed
      RDF file (add) RDF file (delete)
      Triple Store
      RSS Updater
      SPARQL
      endpoint
      RDF Extractor
      New items
      Obsolete items
      Each datasets
      Index
      Entity Name System
      Search
      XML Updater
      URI creation rules
      Lib, courses, loc
      Planning + Logging
      Generic process
      Dataset specific process
    • 10. Method for a exposing a dataset
      • Identify data
      • 11. Get sample data
      • 12. Identify Copyright Issues
      • 13. Identify possible links
      • 14. Identify users and usage
      Initial Meeting with Data Owner
      Lucero Core Team
      Data Owner
      Data Modeling sessions
      Lucero KMi Team
      • Find reusable ontologies
      • 15. Map onto the data
      • 16. Identify uncovered parts
      • 17. Define URI Scheme
      Data Modeling Validation
      Lucero Core Team
      Lucero members
      Data Owner
      Development of Extractor
      URI Creation Rules Definition
      Deployment
      Lucero KMi Team
    • 18. Screenshot of the dataset page
    • 19.
    • 20. Applications
      For education
      Mobile podcast explorer, podcast explorer on TV
      OU Building Map, OU location tracker (cf. foursquare)
      OU Expert Search
      Connecting courses/OpenLearn to relevant podcast
      OU Course Profile Facebook app using list of courses, “Study Buddy” app connecting facebook users to relevant courses
      For Research
      Display connections in a research community
      Research Data/Impact Analysis
      Connection research datasets to external data
    • 21. Example application: Link OpenLearn to relevant course/podcasts
    • 22. Example Application: keep track of location, meetings, tutorials, at the OU
    • 23. Example application: exploring research communities
    • 24. Example application:
      Expert Search using publication information and connecting to contact information within the OU
    • 25. Example application: Explore Information about a person in the “Reading Experience Database” based on data provided by DBPedia (Linked Data version of Wikipedia)  New ways to look at humanities research data
    • 26. Lessons Learnt
      The major part of the work is not technical
      Linked data is simple!
      Identifying available data, obtaining access to them, re-modeling them is hard
      Making people understand that it is worth doing is critical
      Especially when dealing with challenges such as data licenses, private data, etc.
      Get people involved (it is not about you, or the technology)
      A lot of people’s job (administrators, managers, researchers) is all about collecting and managing data
      A lot of this effort is lost because of closed systems, lack of integration and exposure of the data
      Our job is to demonstrate to these people how the principles of linked data can be used to leverage this effort
      Without being disruptive (e.g., the URI of a course in a browser redirects to the course webpage on the OU website
    • 27. Lessons Learnt
      There is no killer app
      The direct benefit of linked data is not in a great big smart application, it is in the many small things that are made easier
      Need to make it easy for developers to get into it, play with it, see the potential by themselves
      Integrating the benefits of linked data in the university’s practices/workflows takes time. It is not a threatening big change, but a slow, incremental adoption
      Plan for long term = need for endorsement
      We work with the assumption that, soon, it will be as common and necessary for a University to have a linked data platform as it is to have a website
      So a linked data initiative at a university cannot be a one time thing. Courses evolve, new material appear, new datasets are made available. (e.g., data.open.ac.uk is updated every day)
      It needs to become part of the University’s role and be endorsed by the departments involved (IT, communication, education, research, business)
      It does not always work
      Some applications might be incompatible with the University’s policies (e.g., Google rich snippet showing the price of a course)
      Support might only get up to a certain point
    • 28. The future
      From nice demonstrators to real semantic web applications
      Use of reasoning and data mining for data consolidation and analysis
      Need proper frameworks for application developers!
      Linked data and the Semantic Web to support research
      Not only research communities
      Identifying new research questions and collecting evidence through connected datasets
      It is not about individual Universities!
      Universities sharing data to benefit students and researchers: the higher education’s web of linked data
      Needs collective vocabularies, recipes, approaches, classifications… the GoodRelations of higher education?
    • 29. The future
      Linked data analytics/Linked data mining
      Interfaces to linked data/Making sense of linked data (with ontologies)
      Semantic web for activity data/personal data
    • 30. Thank you!
      Carlo Allocca
      (Dev)
      SalmanElahi
      ((Ex)-Dev)
      Jane Whild
      (Admin)
      FouadZablith
      (Dev)
      KMi
      AndriyNikolov
      (linking)
      Enrico Motta
      (SGP)
      Mathieu d’Aquin
      (PD)
      Arts
      Suzanne Duncanson-Hunter
      John Wolfe
      Paul Lawrence
      Richard Nurse
      ((ex-)PM)
      Owen Stephens
      (PM)
      Stuart Brown
      Com./
      Student
      Comp.
      Services
      Data Owners
      Non Scantlebury
      Library
      Specialists
      Arts Specialists
      OU Library