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From Exploratory Search to Web Search and back - PIKM 2010

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The power of search is with no doubt one of the main aspects for the success of the Web. Currently available search engines on the Web allow to return results with a high precision. Nevertheless,......

The power of search is with no doubt one of the main aspects for the success of the Web. Currently available search engines on the Web allow to return results with a high precision. Nevertheless, if we limit our attention only to lookup search we are missing another important search task. In exploratory search, the user is willing not only to find documents relevant with respect to her query but she is also interested in learning, discovering and understanding novel knowledge on complex and sometimes unknown topics.

In the paper we address this issue presenting LED, a web based system that aims to improve (lookup) Web search by enabling users to properly explore knowledge associated to her query. We rely on DBpedia to explore the semantics of keywords within the query thus suggesting potentially interesting related topics/keywords to the user.

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  • 1. From Exploratory Search to Web Search and back
    Roberto Mirizzi, Tommaso Di Noia
    mirizzi@deemail.poliba.it, t.dinoia@poliba.it
    Politecnico di Bari
    Via Orabona, 4
    70125 Bari (ITALY)
  • 2. Outline
    • Tags to improve Web Search
    • 3. Exploratory Search
    • 4. LED (Lookup Explore Discover): exploratory search in the Web (of Data)
    • 5. DBpediaRanker: RDF ranking in DBpedia
    • 6. Conclusion and Future work
  • Why we use tags?
    and manymore…
  • 7. WhatisExploratorySearch?
    [Gary Marchionini. ExploratorySearch: FromFindingtounderstanding. Communicationsof the ACM, 49(4): 41-46, 2006]
  • 8. Can Semantic tags support Exploratory search?
    Disambiguation
    Relations among tags
    Machine understandable
    Semantic-aided query refinement
    Plugged into the Web 3.0
    If Semantic tags helped 10% of Internet users to save 10 minutes per month on their searches, this would save globally over 4,000,000 of working hours per year
    LED: Lookup Explore Discover
    http://sisinflab.poliba.it/led/
  • 9. LED: Lookup Explore Discover
    Objectives
    • Enable users to properly explore the semantics of a keyword
    • 10. Guide users to refine a query suggesting related topics/keywords
    Improvelookupsearchtoexploreknowledge
  • 11. Whatisbehind LED? (i)
  • 12. Whatisbehind LED? (ii)
    Comments
    • DBpedia resources are highly interconnected in the RDF graph
    • 13. Not all the relevant resources for a given node are its direct neighbors
    Explore the neighborhood of a resource to discover new relevant resources not directly connected to it
    Rank the results
  • 14. DBpedia graph exploration in LED


    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
  • 15. The functionalarchitecture
    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 query
    Query the system for relevant tags (corresponding to DBpedia resources) and aggregate results
    Show the semantic tag cloud and the results
    Back-end
    Google
    1
    Bing
    SPARQL
    Ext. Info Sources
    Yahoo!
    Graph Explorer
    Context Analyzer
    1
    2
    Offline computation
    Delicious
    Ranker
    2
    3
    DBpedia Lookup Service
    Storage
    3
    1
    2
    Tag Cloud Generator
    Interface
    2
    1
    Query engine
    GUI
    Runtime search
    Meta-search
    engine
    3
    3
  • 16. DBpediaRanker: ranking
    Graph-based and text-based ranking
    ?r1
    ?r2
    isSimilar
    hasValue
    v
    Ranking based on external sources
  • 17. DBpediaRanker: anexample (i)
    wikilinkScore(RDFa, Resource_Description_Framework) = 2
    abstractScore(RDFa, Resource_Description_Framework) = 1.0
  • 18. delicious
    sim(RDFa, Resource_Description_Framework)Google = 1.67e5 / 4.42e5 + 1.67e5 / 1.19e7 = 0.39
    DBpediaRanker: anexample (ii)
  • 19. DBpediaRanker: 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
  • 20. Evaluation (i)
    • Comparison of 5 different algorithms
    • 21. 50 volunteers
    • 22. Researchers in the ICT area
    • 23. 244 votes collected (on average 5 votes for each users)
    • 24. Average time to vote: 1min and 40secs
    http://sisinflab.poliba.it/evaluation
  • 25. Evaluation (ii)
    3.91 - Good
    http://sisinflab.poliba.it/evaluation/data
  • 26. Conclusion
    • LED: a system for exploratory search and query refinement on the (Semantic) Web
    • 27. DBpediaRanker: ranking algorithms for resources in DBpedia
    Future work
    • Expose a RESTful API for building novel mashups and for comparing with different systems
    • 28. Improve ranking algorithms
    • 29. Deal with cases where a single knowledge base in not sufficient
    • 30. Combine a content-based recommendation and a collaborative-filtering approach
  • Trick or Treat?
    From Exploratory Search to Web Search and back (PIKM 2010)
    Thanksforyourattention!
    If you're interested in learning more…
    Roberto Mirizzi, AzzurraRagone, Tommaso Di Noia, Eugenio Di Sciascio. Semantic tags generation and retrieval for online advertising. 19th ACM International Conference on Information and Knowledge Management (CIKM 2010)
    Roberto Mirizzi, Azzurra Ragone, Tommaso Di Noia, Eugenio Di Sciascio. Ranking the Linked Data: the case of DBpedia. 10th International Conference on Web Engineering (ICWE 2010)
    Roberto Mirizzi, Azzurra Ragone, Tommaso Di Noia, Eugenio Di Sciascio. Semantic tag cloud generation via DBpedia. 11th International Conference on Electronic Commerce and Web Technologies (EC-Web 2010)
    Roberto Mirizzi, Azzurra Ragone, Tommaso Di Noia, Eugenio Di Sciascio. Semantic tagging for crowd computing. 18th Italian Symposium on Advanced Database Systems (SEBD 2010)
    Roberto Mirizzi, Azzurra Ragone, Tommaso Di Noia, Eugenio Di Sciascio. Semantic Wonder Cloud: exploratory search in DBpedia. 2th International Workshop on Semantic Web Information Management (SWIM 2010) - Best Workshop Paper at International Conference on Web Engineering (ICWE 2010)
    Roberto Mirizzi - mirizzi@deemail.poliba.it