IKHarvester - Informal Knowledge Harvester
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IKHarvester - Informal Knowledge Harvester Presentation Transcript

  • 1. IKHarvester (Informal Knowledge Harvester) Jarosław Dobrzański Jarosław Dobrzański jaroslaw.dobrzanski @deri.org 31 .05.2007
  • 2. O utline
    • Formal learning
    • Informal learning
    • Social Semantic Information Sources (SSIS)
    • IKHarvester
  • 3. Formal learning
    • Rigid courses – made once and for all
    • Traditional, old, preparatory approach
      • i.e. gathering in a classroom
    • Training is PUSHED
    • Employs advanced solutions
      • Learning Management Systems
      • Online courses
  • 4. Informal learning
    • In USA 7 5 % of organizational learning is informal
    • Self-directed learning
    • Collaborative learning
      • Communication between learners
      • Shared knowledge
    • Flexible and spontaneous (when/where/what to learn)
    • Learning is PULLED
    • Not well structured
      • Article on Wikipedia
      • Blog posts
      • Chats with communicators, i.e. Skype
      • Chats at the coffee machine
    • Cheap or costless!
  • 5. Social Semantic Information Sources
    • Compilation of the Semantic Web and Web 2.0
      • Collaboration
      • Sharing
      • Semantic annotations for resources
      • Interlinking resources and people related to the m
      • Dedicated for people and computers
    • Examples:
      • Semantic wikis: Semantic MediaWiki extension
      • Semantic blogs: SIOC Plugin for WordPress
      • JeromeDL – the Social Semantic Digital Library
  • 6. IKHarvester – Informal Knowledge Harvester
    • Goals to achieve:
      • Capturing informal learning/knowledge from SSIS
      • Providing data for eLearning frameworks, e.g. Didaskon
  • 7. Data Harvesting
    • The Semantic Web
      • RDF feeds (semantic wikis)
      • Relation with RDF documents
        • Information in HTML
    • Non-semantic web pages
      • HTML of Wikipedia or blogs on Blogger still is quite semantic – common templates of web pages
      • HTML scraping
  • 8. Data Providing
    • Learning Object Metadata (LOM)
      • Standard underlying SCORM 2004
      • Features:
        • Used in a number of LMSs
        • Rich description
        • Many aspects: educational, technical,
        • relations with other LOs, classification, ...
  • 9. Data Providing - LOM
  • 10. Service Oriented Architecture
    • Why SOA?
      • Encapsulation
      • Abstraction – hidden logic
      • Loose coupling - independancy
      • Quicker reposnses
      • Reusability - one deployment, many usages
    • REST-based Web Services
      • Popular with Web 2.0 and the Semantic Web
      • Resource-oriented
  • 11. Service Oriented Architecture specification Removes LOM for a specified LO DELETE http://notitio.us/ikh/soa/$URI$ Adds/updates LOM for a specified LO PUT / POST http://notitio.us/ikh/soa/$URI$ Returns the content of a specified LO GET http://notitio.us/ikh/soa/$URI$/content Returns LOM for a specified LO GET http://notitio.us/ikh/soa/$URI$/manifest Returns available LOs or LOs of the specified type ( type parameter) GET http://notitio.us/ikh/soa/[type] Description HTTP Method URL
  • 12. Extensibility – support for new types of respurces
  • 13. Comparison with existing tools
  • 14. IKHarvester at notitio.us
    • http:// notitio.us
      • service for collaborative knowledge aggregation and sharing
      • bookmarking services
      • rich, semantically interconnected metadata shared by using Social Semantic Collaborative Filtering
      • search ing and brows ing with
        • T agsTreeMaps
        • MultiBeeBrowse r
    • IKHarvester tasks:
      • R etrieving RDF information about Web resources bookmarked by users
      • Tagging Web resources
      • E xpos ing aggregated metadata in LOM standard
  • 15. IKHarvester at notitio.us