SWOC - Semantic Wonder Cloud: Exploratory Search in DBpedia - SWIM 2010
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SWOC - Semantic Wonder Cloud: Exploratory Search in DBpedia - SWIM 2010

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Inspired by the Google Wonder Wheel, we present Semantic Wonder Cloud (SWOC): a tool that helps users in knowledge exploration within the DBpedia dataset by adopting a hybrid approach. We describe ...

Inspired by the Google Wonder Wheel, we present Semantic Wonder Cloud (SWOC): a tool that helps users in knowledge exploration within the DBpedia dataset by adopting a hybrid approach. We describe both the architecture and the user interface. The system exploits not only pure semantic connections in the underlying RDF graph but it mixes the meaning of such information with external non-semantic knowledge sources, such as web search engines and tagging systems. Semantic Wonder Cloud allows the user to explore the relations between resources of knowledge domain via a simple and intuitive graphical interface.
The system is available at http://sisinflab.poliba.it/semantic-wonder-cloud/index/

SWIM 2010 - 2nd International Workshop on Semantic Web Information Management

The paper won the "Best Workshop Paper Award" at 10th International Conference on Web Engineering, ICWE 2010, held in Vienna, July 05-09, 2010.

Presented by Roberto Mirizzi (http://sisinflab.poliba.it/mirizzi -
roberto.mirizzi -at- gmail.com)

Vienna (Austria), July 05, 2010

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SWOC - Semantic Wonder Cloud: Exploratory Search in DBpedia - SWIM 2010 Presentation Transcript

  • 1. Semantic Wonder Cloud: exploratory search in DBpedia
    Roberto Mirizzi1, Azzurra Ragone1,2,
    Tommaso Di Noia1, Eugenio Di Sciascio1
    1Politecnico di Bari
    Via Orabona, 4
    70125 Bari (ITALY)
    2Universityof Trento
    Via Sommarive, 14
    38100 Trento (ITALY)
  • 2. Outline
    • Exploratory search
    • 3. SWOC (Semantic Wonder Cloud): the interface
    • 4. Background technologies
    • 5. SWOC: how it works
    • 6. Ranking in Linked Data (DBpedia)
    • 7. Conclusion and Future work
  • WhatisExploratorySearch?
    [Gary Marchionini. ExploratorySearch: FromFindingtounderstanding. Communicationsof the ACM, 49(4): 41-46, 2006]
  • 8. Inspiration: Google Wonder Wheel
    ExploratorySearch in Google…
    …nice, butthereis no “semantics” in it.
    You can notdiscovernewknowledgeexploiting the meaningof a term (keyword/tag/query)
  • 9. SWOC: SemanticWonderCloud
    http://sisinflab.poliba.it/semantic-wonder-cloud/index/
  • 10. Whatisbehind SWOC? (I)
  • 11. Whatisbehind SWOC? (II)
  • 12. What is behind SWOC? (III)


    Knowledge_representation
    Data_management
    Internet_architecture

    XML-based_standards
    Distribute_computing_architecture
    Semantic_Web
    Web_services
    Semantic_Web_Services
    OWL-S
    Triplestores
    Folksonomy

    Web_service_specifications
    Internet_search
    Enterprise_application_integration
    Microformtas


    Legend
    skos:subject
    skos:broader
    Category
    Article
  • 13. The functionalarchitecture
    Back-end
    Google
    Bing
    SPARQL
    Ext. Info Sources
    Yahoo!
    Graph Explorer
    Context Analyzer
    Delicious
    Ranker
    DBpedia Lookup Service
    Storage
    XML documents
    SQL2XML Converter
    Graph Generator
    Resource Selector
    Interface
  • 14. DBpedia-Ranker: ranking
    Graph-based ranking
    ?r1
    ?r2
    isSimilar
    hasValue
    v
    Externalsources-based ranking
  • 15. DBpedia-Ranker: contextanalysis
    The samesimilaritymeasureisused in the contextanalysis
    C
    ?c1
    Algorithm:
    If(v>THRESHOLD) then
    r1belongsto the context;
    add r1to the graphexplorationqueue
    Else
    r1doesnotbelongto the context;
    exclude r1fromgraphexploration
    EndIf
    ?c2
    belongsTo
    ?r1
    ?c…
    ?cN
    hasValue
    Example:
    C = {ProgrammingLanguages, Databases, Software}
    DoesDennis Ritchiebelongsto the givencontext?
    v
  • 16. Evaluation (I)
    http://sisinflab.poliba.it/evaluation
  • 17. Evaluation (II)
    http://sisinflab.poliba.it/evaluation/data
  • 18. Conclusion
    • SWOC: a tool for exploratory search
    • 19. Ranking algorithm for RDF graphs
    Future work
    • Integrate resultsfromsearchengines in SWOC
    • 20. Test ouralgorithmswithdifferentdomains
    • 21. Extract more fine grainedcontexts
    • 22. Enrich the extractedcontextusingalsorelevantproperties
    • 23. Integrateourapproachwithrealexistingsystems
    • 24. Use the core system toautomaticallyextractrelevanttags (concepts) from a document (or from a collectionofdocuments) exploitingtoolsfornamedentitiesextraction
  • Q&A
    Semantic Wonder Cloud: exploratory search in DBpedia (SWIM 2010)
    Thanksforyourattention!
    Seeyoulater at the demo sessionand on Fridaywith…
    Ranking the Linked Data: the case of DBpedia (ICWE 2010)
    Roberto Mirizzi, AzzurraRagone, Tommaso Di Noia, Eugenio Di Sciascio
    mirizzi@deemail.poliba.it, ragone@disi.unitn.it, {ragone,dinoia,disciascio}@poliba.it