Social Semantic Search and Browsing

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Presentation describing the idea of search and browsing cycle, and its implementation using S3B component (

Presentation describing the idea of search and browsing cycle, and its implementation using S3B component (

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  • 1. Social Semantic Search and Browsing Sebastian Ryszard Kruk Digital Enterprise Research Institute National University of Ireland, Galway [email_address]
  • 2. Take away message
    • We search in different way for different things
    • Keyword search is not enough
    • We create the knowledge by sharing our (search) experience
  • 3. Outline
    • Motivation
    • How do people search
    • Search and Browsing lifecycle
    • Applying semantics and making use of social networks:
      • Keyword-based search
      • Faceted Navigation
      • Collaborative Filtering
    • Conclusions - Putting it all together
  • 4. How do people search?
    • Different user goals:
      • 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
    • Rose and Levinson: Understanding user goals in web search (2004)
  • 5. Search and browsing lifecycle
    • Why ?
      • Information can be useful
      • Information can be a garbage
    • 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?
  • 6. 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
  • 7. 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
  • 8. 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
  • 9. 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)
  • 10. 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)
  • 11. 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
  • 12. 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