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Semantic Tags Generation and Retrieval for Online Advertising - CIKM 2010

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One of the main problems in online advertising is to display ads which are relevant and appropriate \wrt what the user is looking for. Often search engines fail to reach this goal as they do not ...

One of the main problems in online advertising is to display ads which are relevant and appropriate \wrt what the user is looking for. Often search engines fail to reach this goal as they do not consider semantics attached to keywords. In this paper we propose a system that tackles the problem by two different angles: help (i) advertisers to create more efficient ads campaigns and (ii) ads providers to properly match ads content to keywords in search engines.
We exploit semantic relations stored in the DBpedia dataset and use an hybrid ranking system to rank keywords and to expand queries formulated by the user. Inputs of our ranking system are (i) the DBpedia dataset; (ii) external information sources such as classical search engine results and social tagging systems.
We compare our approach with other RDF similarity measures, proving the validity of our algorithm with an extensive evaluation involving real users.

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    Semantic Tags Generation and Retrieval for Online Advertising - CIKM 2010 Semantic Tags Generation and Retrieval for Online Advertising - CIKM 2010 Presentation Transcript

    • Semantic Tags Generation and Retrieval for Online Advertising
      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)
    • Outline
      • Tags in Web 2.0 -> 3.0
      • Computational advertising
      • NOT (Not Only Tag): semantic tag cloud generation
      • DBpediaRanker: RDF ranking in DBpedia
      • Conclusion and Future work
    • Who is using tags nowadays?
      and manymore…
    • What about Tags in Online Advertising?
      meat
      cooking
      fish
      spaghetti
      pasta
      dessert
      food
      recipes
    • BigG (& co.) helps you… in half (i)
      Keyword Tool
      • Based on actual Google search queries
      • Generates keywords based on the content of a URL, words or phrases
      1
      2
      3
      …nice, butthereis no “semantics” in it.
      You can notexpandyourkeywordslistexploiting the meaningof a term (keyword/tag/query)
      https://adwords.google.com/select/KeywordToolExternal
    • BigG (& co.) helps you… in half (ii)
      Keyword Tool
      • Based on actual Google search queries
      • Generates keywords based on the content of a URL, words or phrases
      …nice, butthereis no “semantics” in it.
      You can notexpandyourkeywordslistexploiting the meaningof a term (keyword/tag/query)
    • WhynottouseSemantictags?
      Pluggedinto the Web 3.0
      Disambiguation
      Relations amongtags
      Machineunderstandable
      NOT:NotOnlyTag
      http://sisinflab.poliba.it/not-only-tag/
    • NOT: NotOnlyTag
      Objectives
      • Assist advertisers to create more efficient ads campaigns
      • Support ads providers to properly match ads content to keywords in search engines
      Improve
      advertiserexperienceand ad selection
    • Whatisbehind NOT? (i)
    • Whatisbehind NOT? (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 NOT
      Magento


      Content_management_systems
      Free_business_software

      Web_development
      Web_applications
      PHP

      Open_source_CMS
      Web_application_frameworks
      Zend_Framework

      JavaServer_Faces
      Python_web_application_frameworks
      Joomla_extensions
      Drupal


      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)
      Show the semantic tag cloud
      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
      2
      1
      Query engine
      Tag Cloud Generator
      GUI
      Runtime search
      3
      3
      Interface
    • DBpediaRanker: ranking
      Graph-based and text-based ranking
      ?r1
      ?r2
      isSimilar
      hasValue
      v
      Ranking based on external sources
    • DBpediaRanker: anexample (i)
      wikilinkScore(Zend_Framework, PHP) = 2
      abstractScore(Zend_Framework, PHP) = 1.0
    • delicious
      sim(Zend_Framework, PHP)Google = 1.53e6 / 2.96e6 + 1.53e6 / 1.71e9 ≈ 0.52 + 0
      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 Ritchie belong to 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
      • NOT: a prototype system for tag cloud generation in semantic advertising
      • DBpediaRanker: ranking algorithms for resources in DBpedia
      Future work
      • Use the back-end of the system to develop new interfaces for exploratory browsing
      • Improve ranking algorithms
      • Combine a content-based recommendation and a collaborative-filtering approach
      • Develop a platform to test our system with real ads about different domains
    • Q&A
      Semantic Tags Generation and Retrieval for Online Advertising (CIKM 2010)
      Thanksforyourattention!
      See you tomorrow at PIKM 2010in Room Alberta at 4pmwith…
      From Exploratory Search to Web Search and back
      If you're interested in learning more…
      Roberto Mirizzi, Tommaso Di Noia. From Exploratory Search to Web Search and back. 4th Workshop for Ph.D. Students in Information and Knowledge Management (PIKM 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