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Ranking the Linked Data: the case of DBpedia - ICWE 2010

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  • 1. Ranking the LinkedData: the case ofDBpedia
    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
    Tags are all around
    NOT (Not Only Tag): what is it?
    NOT a look behind the curtains:
    Ranking of RDF resources: an hybrid approach
    Evaluation
    Conclusion and Future Work
  • 3. Tags are allaround
  • 4. Tag cloud
    and manymore…
  • 5. Tagging: a double face
    Annotation phase
    Retrieval phase
  • 6. Problemswithannotation
    Insert as much as possible tags (time consuming):
    different versions of the same tag to catch all the possible searches
    Multilingual tags
  • 7. Problem with retrieval
    Exactly (syntactic) match among tags: web service is different from web services, webservices,…
  • 8. WhynottouseSemantictags?
    Pluggedinto the Web 3.0
    Disambiguation
    Relations amongtags
    Machineunderstandable
    NOT:NotOnlyTag
    http://sisinflab.poliba.it/not-only-tag/
  • 9. Demo
    Let’s imagine to tag the book:
  • 10. NOT
    http://sisinflab.poliba.it/not-only-tag/
  • 11. Smarter tagging
    Annotation phase
    Retrieval phase
  • 12. What is behind NOT?
    DBpedia graph exploration
    Computation of similarity value between each pair of RDF resources using external information sources (search engines, bookmarking systems)
  • 13. Whatisbehind NOT? (II)
  • 14. Whatisbehind NOT? (III)
  • 15. What is behind NOT? (IV)


    Knowledge_representation
    Data_management
    Internet_architecture

    XML
    Computer_and_telecommunication_stantards
    Microformat

    Semantic_Web
    XML-based_standards
    RDFa
    Resource Description Framework
    Triplestores
    Folksonomy

    Web_services
    User_interface_markup_languages
    Scalable_Vector_Graphics
    Microformats


    Legend
    skos:subject
    skos:broader
    Category
    Article
  • 16. DBpedia-Ranker: hybrid ranking
    Graph-based ranking
    ?r1
    ?r2
    isSimilar
    hasValue
    v
    Externalsources-based ranking
  • 17. Functional Architecture
    Offline computation
    Linked Data graph exploration
    Rank nodes exploiting
    external information
    Store results as pairs of nodes together with their similarity
    Runtime Search
    Start typing a tag
    Query the system for relevant tags (corresponding to DBpedia resources)
    Show the semantic tag cloud
    Back-end
    Google
    Google
    Offline computation
    1
    Bing
    SPARQL
    Ext. Info Sources
    Yahoo!
    Graph Explorer
    Context Analyzer
    1
    2
    Delicious
    Ranker
    2
    3
    DBpedia Lookup Service
    Storage
    3
    Runtime search
    1
    2
    1
    2
    Query engine
    Cloud Generator
    GUI
    3
    3
  • 18. Evaluation
    We evaluate five different algorithms:
    DBpediaRanker
    DBpediaRanker minus Wikipedia info
    DBpediaRanker minus ext info sources
    Co-occurrence
    Similarity Distance
  • 19. Evaluation (II)
    • 50 volunteers
    • 20. Researchers in the ICT area
    • 21. 244 votes collected (on average 5 votes for each users)
    • 22. Time to vote: 1min and 40secs
    http://sisinflab.poliba.it/evaluation
  • 23. Evaluation (III)
    3.91 - Good
    http://sisinflab.poliba.it/evaluation/data
  • 24. Conclusion
    NOT *is* useful in the annotation phase:
    suggestions of semantically related tags
    Tags enrichment
    NOT *is* useful in the retrieval phase:
    Semantic match among tags
  • 25. Future Work
  • 26. Impakt Revolution
    http://sisinflab.poliba.it/impakt-revolution/
  • 27. Inspiration: Google Wonder Wheel
    ExploratorySearch in Google…
    …nice, butthereis no “semantics” in it.
    You can notdiscovernewknowledgeexploiting the meaningof a term (keyword/tag/query)
  • 28. SWOC: SemanticWonderCloud
    http://sisinflab.poliba.it/semantic-wonder-cloud/index/
  • 29. Q&A
    Thanks for being here on Friday! :-)
    http://sisinflab.poliba.it/not-only-tag/
    http://sisinflab.poliba.it/semantic-wonder-cloud/index/
    http://sisinflab.poliba.it/impakt-revolution/
    a.ragone@poliba.it
  • 30. Conclusion
    • NOT: a tool for smarter tagging
    • 31. Ranking algorithm for RDF graphs
    Future work
    • Test ouralgorithmswithdifferentdomains
    • 32. Extract more fine grainedcontexts
    • 33. Enrich the extractedcontextusingalsorelevantproperties
    • 34. Integrateourapproachwithrealexistingsystems
    • 35. Use the core system toautomaticallyextractrelevanttags (concepts) from a document (or from a collectionofdocuments) exploitingtoolsfornamedentitiesextraction