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A presentation of the Corrib clan that was shown in Korea

A presentation of the Corrib clan that was shown in Korea

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Slawek Korea Presentation Transcript

  • 1. Semantic Infrastructure Lab (Corrib) Digital Enterprise Research Institute National University of Ireland, Galway
  • 2. Outline
    • Motivation
    • JeromeDL
    • FOAFRealm
    • S3B
    • MarcOnt
    • Didaskon
    • Conclusion
  • 3. Motivation
    • Semantic Web (2.0?) will not emerge by its own
    • We need to build an infrastructure first
    • Open source – fast research dissemination channel
    • JeromeDL spin-off projects (divide and conquer approach)
  • 4. About us
    • Group of researchers from DERI Galway and students from Gdansk University of Technology
    • One goal – make semantic web 2.0 reality
    • Supervisors : prof. Stefan Decker (DERI), prof. Henryk Krawczyk (GUT), Sebastian Ryszard Kruk
    • PhD Students : Maciej Dąbrowski, Adam Gzella, Sławomir Grzonkowski , Jacek Jankowski, Krystian Samp, Tomasz Woroniecki
    • Interns (March-June 2007): Filip Czaja, Jarek Dobrzanski, Wladek Bultrowicz
    • 9 Master students from GUT
  • 5. Social Semantic Digital Library
    • A library stores and provides access to resources (books)
    • Qualified staff updates catalogues and helps users
  • 6. Social Semantic Digital Library
    • Machine-readable resources
    • Full-text index improves searching
    • Easy access
    • Availability
  • 7. Social Semantic Digital Library
    • Resources are accessible by machines, not with machines
    • Metadata is rich and extensible
    • Searching reflects meaning of terms
    • RDF is a standard for representing information
    • Not just resources but also knowledge is shared
  • 8. Social Semantic Digital Library
    • Involves the community into sharing knowledge
    • Utilizes social network in searching
    • Allows for comments, blogs, shared bookmarks
    • Easy tagging
  • 9. Social Semantic Digital Library
    • Semantic digital libraries
      • integrate information based on different metadata, e.g.: resources, user profiles, bookmarks, taxonomies
      • provide interoperability with other systems (not only digital libraries)
      • deliver more robust, user friendly and adaptable search and browsing interfaces empowered by semantics
  • 10. JeromeDL – Social Semantic Digital Library
    • JeromeDL fulfills requirements of:
    • Librarians
      • precise annotations
      • rich metadata
    • Researchers
      • easy publishing
      • searching related topics
    • Average users
      • efficient search and browsing
      • online collaboration
  • 11. Using JeromeDL
    • Uploading a resource
      • provide title, abstract, author etc.
      • provide structure of the resource (e.g., chapters)
      • choose domains of the subject
      • choose keywords for the resource
      • set additional properties
      • upload digital parts of the resource
  • 12. Using JeromeDL
  • 13. Using JeromeDL
    • An administrator either approves or rejects a published resource
  • 14. JeromeDL for a regular user
    • Browsing resources
      • by type, author, keyword, domain
    • Downloading the resource and its bibliographic description in various formats
    • Subscribing to RSS feeds
    • Searching
      • simple, advanced, distributed, semantic
  • 15. JeromeDL for a regular user
  • 16. Search and browsing lifecycle
    • Why ?
      • Information can be useful or a garbage
      • Different user goals ( Rose and Levinson: Understanding user goals in web search (2004) )
        • Resource Seeking - the user wants to find a specific resource (e.g. lyrics of a song, a program to download, a map service etc.)
        • Navigational - the user is searching for a specific web site whose URL s/he forgot
        • Informational - the user is looking for information about a topic s/he is interested in
    • How ? (Search and browsing actions)
      • [REUSE] keyword-based search (resource seeking)
      • [REDUCE] faceted navigation (navigational)
      • [RECYCLE] collaborative filtering (informational)
    • Can this process be improved with Semantic Web and Social Networking technologies?
  • 17. Query refinement in keyword-based search
    • Why simple full-text search is not enough?
      • Too many results (low precision)
      • One needs to specify the exact keyword (low recall)
      • How to distinguish between: Python and python? (high fall-out)
    • How ?
      • Disambiguation through a context
        • Query context
        • Short-term context:
          • User’s goal
          • Location
          • Time
        • Long-term context:
          • User’s interest
          • Search engine specific
  • 18. Query refinement in keyword-based search
    • How ?
      • Query refinement)
        • Spread activation
        • Types mapping
        • Pruning
      • Acquiring the context information:
        • Previous searches of the user
        • Semantically annotated user’s bookmarks
        • Community profile
    • And ? (Manual query refinement)
      • “Tell me why” button and the transcript of refinement process
      • Continue to faceted navigation
  • 19. Faceted navigation on arbitrary graph
    • Why ?
      • The search does not end on a (long) list of results
      • The results are not a list (!) but a graph
      • We loose context with linear navigation
      • A need for unified notion (UI, SOA) of filter/narrow and browse/expand services
  • 20. Faceted navigation on arbitrary graph
    • How (SOA)?
      • Defines REST access to services and their composition
      • Basic services: access, search, filter, similar, browse, combine
      • Meta services: RDF serialization, subscription channels, service ID generation
      • Context services: manage contexts, manage service calls/compositions in the context, lists contexts
      • Statistics services: properties, values, tokens
    • How (User interface)?
      • Hexagons to capture the notion of non-linear browsing
      • Selecting values from list, tag cloud or TagsTreeMap TM
      • Context zoomable interface:
        • List (graph) of results
        • Browse from current results
        • Navigate between service call
        • Navigate between contexts (with given call)
  • 21. Social Semantic Collaborative Filtering
    • Why?
      • The bottom-line of acquiring knowledge: informal communication (“word of mouth”)
    • How?
      • Everyone classifies (filters) the information in bookmark folders (user-oriented taxonomy)
      • Peers share (collaborate over) the information (community-driven taxonomy)
    • Result?
      • Knowledge “flows“ from the expert through the social network to the user
      • System amass a lot of information on user/community profile (context)
  • 22. Social Semantic Collaborative Filtering
    • Problems?
      • The horizon of a social network (2-3 degrees of separation)
      • How to handle fine-grained information (blogs, wikis, etc.)
    • Solutions? (under testing)
      • Inference engine to suggest knowledge from the outskirts of the social network
      • Support for SIOC metadata:
        • SIOC browser in SSCF
        • Annotations and evaluations of “local” resources
  • 23. Putting it all together user profile: recent actions refine search results filter, record, annotate, and share results and actions re-call shared actions user profile: user’s interests filter, record, annotate, and share results
  • 24. Introduction to MarcOnt
    • Motivation:
    • Provide set of tools for
    • collaborative ontology
    • development
    • MarcOnt Initiative goals:
    • Collaboration
    • Tools for domain experts
    • Mediation services
  • 25. MarcOnt Mediation Services
    • 2. Format translation
    1. Format co-operation MarcOnt Mediation Services RDF Translator
  • 26. MarcOnt Ontology
    • Central point of MarcOnt Initiative
    • Translation and mediation format
    • Continuos collaborative ontology improvement
    • Knowledge from the domain experts
    • Community influence and evaluation
  • 27. MarcOnt Portal
    • 3. Source of knowledge
    • Portal provides:
    • Suggestions
    • Annotations
    • Versioning
    • Ontology editor
  • 28. MarcOnt Initiative summary
    • MarcOnt Initiative goals:
    • Create a framework for collaborative ontology improvement (E-learning)
    • Provide domain experts with tools to share their knowledge
    • Offer tools for data mediation between different data formats
  • 29. Didaskon
    • Didaskon - Automated Curriculum Composition based on the Work-flow Scheduling of Semantically Annotated Learning Object Services
    • Architecture of the future e-Learning system (our idea presented on LACLO 2006):
    • Ontology for user model – delivering personalised content
    • Ontology for content - ensuring cooperation of heterogeneous environments which use different formats
  • 30. Didaskon - Architecture
    • Didaskon – e-Learning framework, that will be based on existing solutions:
    • FOAFRealm - users management,
    • JerlomeDL – learning object’s repository,
    • MBB – improved browsing,
    • MarcOnt – handling different data formats,
    • SSIS – tracking informal learning
  • 31. Conclusion
    • Together with smaller projects (JOnto, TagsTreeMaps, HexBrowser) these are our building blocks for the Semantic Web (2.0)
    • The initial infrastructure has been delivered - time to start researching again
    • Please visit: http://www.corrib.org/ for more information