Social Semantic Search and Browsing

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    Social Semantic Search and Browsing - Presentation Transcript

    1. Social Semantic Search and Browsing Sebastian Ryszard Kruk Digital Enterprise Research Institute National University of Ireland, Galway [email_address] http://corrib.deri.ie/
    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

    + Sebastian KrukSebastian Kruk, 3 years ago

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