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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, if ...

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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    From Exploratory Search to Web Search and back - PIKM 2010 From Exploratory Search to Web Search and back - PIKM 2010 Presentation Transcript

    • 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)
    • Outline
      • Tags to improve Web Search
      • Exploratory Search
      • LED (Lookup Explore Discover): exploratory search in the Web (of Data)
      • DBpediaRanker: RDF ranking in DBpedia
      • Conclusion and Future work
    • Why we use tags?
      and manymore…
    • WhatisExploratorySearch?
      [Gary Marchionini. ExploratorySearch: FromFindingtounderstanding. Communicationsof the ACM, 49(4): 41-46, 2006]
    • 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/
    • LED: Lookup Explore Discover
      Objectives
      • Enable users to properly explore the semantics of a keyword
      • Guide users to refine a query suggesting related topics/keywords
      Improvelookupsearchtoexploreknowledge
    • Whatisbehind LED? (i)
    • Whatisbehind LED? (ii)
      Comments
      • DBpedia resources are highly interconnected in the RDF graph
      • 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
    • 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
    • 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
    • DBpediaRanker: ranking
      Graph-based and text-based ranking
      ?r1
      ?r2
      isSimilar
      hasValue
      v
      Ranking based on external sources
    • DBpediaRanker: anexample (i)
      wikilinkScore(RDFa, Resource_Description_Framework) = 2
      abstractScore(RDFa, Resource_Description_Framework) = 1.0
    • delicious
      sim(RDFa, Resource_Description_Framework)Google = 1.67e5 / 4.42e5 + 1.67e5 / 1.19e7 = 0.39
      DBpediaRanker: anexample (ii)
    • 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
    • Evaluation (i)
      • Comparison of 5 different algorithms
      • 50 volunteers
      • Researchers in the ICT area
      • 244 votes collected (on average 5 votes for each users)
      • Average time to vote: 1min and 40secs
      http://sisinflab.poliba.it/evaluation
    • Evaluation (ii)
      3.91 - Good
      http://sisinflab.poliba.it/evaluation/data
    • Conclusion
      • LED: a system for exploratory search and query refinement on the (Semantic) Web
      • DBpediaRanker: ranking algorithms for resources in DBpedia
      Future work
      • Expose a RESTful API for building novel mashups and for comparing with different systems
      • Improve ranking algorithms
      • Deal with cases where a single knowledge base in not sufficient
      • 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